Head Impact Biomechanics Across Men's and Women's Contact Sports: A Comparative and Clustering Analysis

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While wearable sensors provide valuable biomechanics insight, most studies focus on single sports, and the variability in sensor methodologies limits cross-sport comparisons. Our objectives were to conduct a multisport comparison and clustering of head impact biomechanics features implicated in brain injury risk. We uniformly processed a multisport dataset gathered using instrumented mouthguards containing direct head impacts in men's football, men's hockey, women's rugby, and women's soccer. We compared directional and resultant peak kinematics, impulse durations, and impact directionality metrics. Then, we applied unsupervised k-means and t-distributed stochastic neighbour embedding (t-SNE) models to examine clustering in impact magnitude and frequency features. Statistically significant cross-sport differences were found in all biomechanical features. Men’s football exhibited the highest resultant peak kinematics, while women’s soccer showed lowest resultant kinematics. However, directional comparisons revealed unexpected trends such as women’s soccer impacts exhibiting high sagittal kinematics relative to other sports. Clustering analyses grouped impacts into low and high magnitude/frequency clusters that transcended sport boundaries, with only women's soccer impacts demonstrating tight clustering patterns due to consistent heading biomechanics. We uniquely curated a standardized dataset for multisport head impact biomechanics comparisons. Cross-sport differences in under-investigated biomechanical features such as directional peak kinematics may need to be further examined for potential sport-specific injury risk considerations. Despite substantial gameplay differences, we found interesting shared biomechanical patterns across sports, warranting joint analyses to inform implications in protective equipment design and injury prevention strategies. Physical sciences/Energy science and technology Physical sciences/Engineering Injury Biomechanics Instrumented Mouthguard Head Impact Biomechanics Signal Processing Unsupervised Machine Learning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Concussion, or mild traumatic brain injury (mTBI), continues to be a major health concern for many contact sport athletes [ 1 ] and a leading cause of disability, especially in youth and adolescents [ 2 ]. More recently, there is a growing body of literature suggesting repeated subconcussive events – head impacts that do not lead to clinical concussion diagnosis – can give rise to detectable neurophysiological changes [ 3 ]. To further understand how head impacts during sport are influencing brain health, researchers have applied wearable sensors to collect on-field head impact biomechanics data. Data from these sensors can be leveraged to quantify potential brain injury risk through head kinematics measures such as peak linear and angular accelerations [ 4 ] as well as simulated brain strains [ 5 ]. Various studies have deployed sensors to gather head impact data in sports, identifying common impact scenarios and mechanisms of head contact. Notably, a number of factors may influence head impact biomechanics across different sports. For example, protective headgear, such as helmets, may reduce peak head acceleration by dissipating impact energy [ 6 ] and increasing impact duration [ 7 ]. Furthermore, the mechanics of impacting objects can also affect impact biomechanics, such as stiffness and damping differences between common helmet-to-helmet impacts in American football [ 8 ] and helmet-to-board impacts in ice hockey [ 9 ]. Ice hockey, with typically higher speed on-ice collisions, may present greater momentum and higher force transferred during impacts [ 10 ]. These are just a few factors influencing impact biomechanics, with others—including gameplay dynamics and sex—also contributing to differences across sports. However, cross-sport comparison of head impact biomechanics continues to be a challenge given the varying sensor types employed, differences in sensor specifications, and non-standardized data processing techniques. More recently, instrumented mouthguards (iMGs) have become the standard sensor type in recent field studies [ 11 ], yet differences in sensor specifications and data processing methods still make comparing results across iMG studies a challenge. For example, Jones et al. [ 12 ] demonstrated substantial differences in both on-field and in-lab peak kinematics across four commonly used iMGs for the same impact scenarios. Additionally, Gellner et al. [ 13 ] highlighted sensitivity in peak kinematics to varying filter cut-off frequencies. These variations, combined with the tendency for most studies to focus on single-sport data collection and analyses, make cross-sport comparison of head impact biomechanics difficult. Furthermore, most analyses concentrate on peak head kinematics, and there is limited reporting of other biomechanical parameters implicated in brain injury risk, such as impulse durations [ 14 ], directionality [ 15 ] and frequency characteristics [ 16 ]. Uniquely, we curated raw iMG impact data across multiple sports and processed all data with a uniform pipeline to enable more controlled comparison of head impact biomechanics across university-level men’s ice hockey, men’s American football, women’s soccer, and women’s rugby. With this dataset, we had two primary objectives: (i) compare biomechanical features of iMG-measured, uniformly processed direct head impacts across sports, and (ii) use clustering methods to further examine if sports distinctly cluster by biomechanical features. We investigated diverse biomechanical features, including impact magnitude, impulse duration, acceleration direction, and frequency features that have been linked to brain injury risk. 2. Methods 2.1 Methodology Overview In our comparison, we included only video-verified direct head impacts and conducted a rigorous data quality review to ensure a high-confidence dataset. As the curated dataset was collected using three different iMGs, we developed the uniform processing pipeline to address sensor setting differences such as varying recording threshold, recording duration and sample rate, as well as ensure standardized filtering methods. Using this standardized multisport dataset, for objective (i), we examined and compared the distributions of biomechanical features across sports, including impact magnitude, impulse duration, and directionality-focused features. For objective (ii), we applied an unsupervised k-means clustering algorithm and a t-distributed stochastic neighbour embedding (t-SNE) model to examine the clustering of impact magnitude and frequency features and determine if sport-specific clusters arise. 2.2 Dataset Descriptions A head impact database was curated from previously collected iMG data from university level men’s American football [ 17 , 18 ], women’s soccer [ 19 ], men’s ice hockey [ 20 ], and women’s rugby [ 21 ] teams. Both men’s hockey and women’s rugby teams wore Prevent Biometrics [ 22 ] iMG’s. The women’s soccer team wore instrumented mouthpieces from Wake Forest Center for Injury Biomechanics [ 23 ] and men’s football wore Stanford University iMG’s [ 24 ]. All iMG impacts were video-verified and only direct head contacts were included in the analysis. This enables comparison of direct head impact biomechanics separately from inertial head acceleration events due to body contact, which can have very different mechanisms and are also usually more difficult to confidently verify through video analysis. All impact traces were visually inspected to keep only recordings where clearly observable linear and angular acceleration impulses could be identified around the time of trigger. The Prevent iMG, Wake Forest Mouthpiece, and Stanford University iMG all had varying initial recording settings including sampling frequency, trigger thresholds, and impact durations. Coupling of the iMG to the teeth was assessed for Prevent iMG-recorded impacts using proximity sensor data [ 21 ], where only well-coupled impacts were included for analysis. Part of the Stanford iMG dataset from Wu et al., 2017 [ 18 ] used iMGs that included a similar proximity sensor to include only well-coupled impacts. The Wake Forest instrumented mouthpiece was a retainer-style device that was difficult to decouple from teeth without fully removing from the mouth, providing confidence of device coupling. Table 1 includes the initial settings of all iMGs which were then harmonized to follow one uniform processing pipeline. All subjects were consented, and each dataset had research ethics board approval as detailed in the original publications. Table 1 Initial sensor specifications and uniform processing settings Sensor Specifications Prevent iMG Wake Forest Mouthpiece Stanford iMG Initial Sampling Frequency 3200 Hz 1000 Hz 1000 Hz Linear Acceleration Trigger (axis) 8 g (any axis) 5 g (any axis) 7 or 10 g (resultant) Impact Duration (Pre-Trigger/Post-Trigger) 50 ms (10 ms/40 ms) 150 ms (30 ms/120 ms) 100 ms (10 ms/90 ms) Changes Made Sampling Frequency Down-sampled to 1000 Hz - - Linear Acceleration Trigger (Axis) Increase to 10 g (Resultant) Increase to 10 g (Resultant) Increase to 10 g (Resultant) Impact Duration - Decrease to 50 ms Decrease to 50 ms Uniform Sampling Frequency 1000 Hz Linear Acceleration Trigger (axis) 10 g (resultant) Impact Duration (Pre-Trigger/Post-Trigger) 50 ms (10ms/40ms) 2.3 Processing Methodology We applied a uniform pre-processing and processing script to raw sensor data to minimize potential sampling and processing differences (Table 1 ). We down-sampled the Prevent iMG to 1000 Hz and truncated the impact duration window for the Wake Forest mouthpiece and Stanford iMG to match the Prevent iMG (10 ms pre-impact, 40 ms post-impact). To account for trigger differences, we chose a uniform 10g resultant linear acceleration trigger for all data. We aligned and centered all the impacts to ensure a trigger at 10g resultant with a 50 ms impact window. Data were rotated to align with anatomical axes. Following rotation, data were filtered using a low-pass 4th order Butterworth filter with cutoff frequencies of 300 Hz for linear acceleration and 180 Hz for angular velocity. Filter cutoff values were selected based on the limits of individual sensor bandwidth ranges. Following filtering, a fourth-order differentiation was applied to angular velocities to determine angular accelerations. Linear accelerations were not transformed to head center of gravity (CoG), as we did not have sufficient information from all datasets to apply subject-specific transforms, and the standard approach of transforming based on 50th percentile male human head dimensions can introduce systematic bias into the analysis. As such, all linear head kinematics analyses are based on kinematics at the upper dentition location, while rotational kinematics are invariant across the head assuming rigid body motion. 2.4 Feature Extraction and Analysis To compare head impact biomechanics across sports, we computed key biomechanical features that are commonly used in head impact biomechanics research and have known injury risk implications. These features were calculated for each recorded head impact across all sports in our dataset. Directional Magnitude Features Absolute maxima of peak linear acceleration (PLA) in anterior-posterior (AP), left-right (LR), and inferior-superior (IS) axes, as well as peak angular velocity (PAV), and peak angular acceleration (PAA) in coronal (cor.), sagittal (sag.) and horizontal (hor.) planes were computed for each impact. Resultants (R.) were also computed for PLA, PAV, and PAA. Impulse Duration Features Linear and angular acceleration impulse durations were determined from the maximum linear and angular acceleration directions for each impact, respectively. Impulse durations were extracted by calculating the width of the impulse at half of the peak acceleration value of the maximum axis. Therefore, each impact consisted of a linear acceleration and an angular acceleration impulse duration. Acceleration Direction Features Peak acceleration directions were computed by determining the peak resultant acceleration value and finding its corresponding per axis values to create a 3D acceleration vector, for both the linear and angular acceleration individually. The peak acceleration direction vectors were then divided into six direction categories (forward/anterior, backward/posterior, leftward, rightward, upward/superior, downward/inferior), separated by the 45-degree oblique directions in each anatomical plane. Sagittal and Coronal Ratios During a head impact, linear and rotational head kinematics are intrinsically coupled due to the complex head-neck-torso system's response to external impact forces. We studied the coupling between linear and rotational kinematics in sagittal and coronal impacts by calculating the linear-angular acceleration ratio. For sagittal impacts (> 80% of peak angular acceleration in sagittal plane), we calculated the sagittal acceleration ratio (rSagittal) by dividing anterior-posterior linear acceleration by sagittal angular acceleration at the moment of peak angular acceleration. Similarly, for coronal impacts (> 80% of peak angular acceleration in coronal plane), we calculated the coronal ratio (rCoronal) by dividing left-right linear acceleration by coronal angular acceleration. Frequency Features We extracted wavelet transform (WT) features to examine signal characteristics in the time-frequency domain [ 18 ]. Morlet WT was performed on the linear and angular accelerations in all directions. For each impact, the peak frequency at the peak WT amplitude in the time-domain, was computed. Further, we estimated the bandwidth of each impact by identifying the range of frequencies where the WT amplitude remained above 50% of its peak value (full-width at half maximum). 2.5 Statistical Methods and Clustering Analysis For objective (i), Kruskal-Wallis test was used to test for significance, followed by a post-hoc pairwise comparison with Bonferroni correction to statistically compare directional magnitude, impulse duration, as well as rSagittal and rCoronal features across sports. Following the Kruskal-Wallis test, we also calculated the epsilon square ( \(\:\epsilon\:\) 2 ) to determine effect size, with the classification of 0.01 \(\:\le\:\) \(\:\epsilon\:\) 2 \(\:\le\:\) 0.06 as a small effect, 0.06 \(\:\le\:\epsilon\:\) 2 \(\:\le\:\) 0.14 a medium effect, and \(\:\epsilon\:\) 2 \(\:\ge\:\) 0.14 as large effect [ 25 ]. A Chi-squared test for independence, followed by a post-hoc pairwise comparison with Bonferroni correction, was used to determine if any significant association existed between sports and the distribution of acceleration direction features. For objective (ii), we applied a k-means clustering algorithm for two separate feature groups (directional impact magnitude features and frequency features) to examine clusters in directional magnitude distributions and in impact frequency characteristics. Only impact magnitude and frequency features were used for k-means clustering, as we expected time-frequency domain features to capture the impulse duration information, and acceleration direction features did not form clear clusters (low silhouette scores) in preliminary analysis. Additionally, coupling ratios were only computed for sagittal and coronal impacts, and thus not available for including as a feature for all impacts. After our k-means analysis, we then trained a t-SNE model on all combined magnitude and frequency features to project high-dimensional non-linear features into 2D space, while maintaining local relationships, thus optimizing for cluster identification with the high-dimensional feature space. Before applying the k-means clustering algorithm, we normalized features using the min-max method prior to applying the MATLAB kmeans function (R2023b, The MathWorks, Inc., Natick, MA). We initialized seeds by implementing the MATLAB kmeans++ [ 26 ] algorithm and computed silhouette scores to evaluate the effectiveness of the clustering for each feature group [ 27 ]. To determine the optimal number of clusters, we generated an elbow plot using average silhouette scores. We identified the features contributing most to centroid movement by analyzing within-cluster sum of squares (WCSS)[ 28 , 29 ]. For the t-SNE algorithm [ 30 ], following recommended pre-processing techniques [ 30 ], we reduced the data to two dimensions using a principal component analysis (PCA). Based on the dataset size, we selected a perplexity value of 30 (default value for this data size) to define the effective number of local neighbors for the model. 3. Results Database: Our database consisted of 1250 total video-verified direct head impacts, after data quality screening and re-thresholding. Of the 1250 impacts, 146 were from men’s hockey (20 athletes and 80 athlete-events), 232 from men’s football (10 athletes and 22 athlete-events), 306 from women’s rugby (19 athletes and 57 athlete-events), and 566 from women’s soccer (13 athletes and 146 athlete-events). Magnitude Features: Significant differences in peak kinematics across the four sports were identified using the Kruskal-Wallis test(Fig. 1, Supplementary Table 1 & 2). Effect sizes indicated medium to large sport-based differences for PLA LR ( 2 = 0.40), PAV cor ( 2 = 0.33), and PAV R ( 2 = 0.31), suggesting these metrics vary substantially by sport. In contrast, PLA AP ( 2 = 0.04) and PAA R ( 2 = 0.13) showed weaker effect sizes. For PLA, men’s football presented the highest median PLA AP and PLA LR (10.8 g and 10.6, respectively) while women’s soccer displayed the highest PLA IS (11.2 g). Additionally, men’s football demonstrated the highest 95 th percentile PLA AP (27.5 g) and PLA IS (25.3 g) while men’s hockey had the highest 95 th percentile PLA LR (30.7 g). Post-hoc analyses highlighted most pairwise comparisons were significant, except those involving men’s hockey vs. women’s rugby. Additionally, comparisons between men’s hockey vs. women’s soccer, and women’s rugby vs. women’s soccer for PLA AP , men’s football vs. women’s rugby for PLA LR , and men’s hockey vs. men’s football for PLA IS were not significantly different. For PAV, men’s football displayed the highest median PAV cor (6.8 rad/s) and PAV sag (6.3 rad/s), while men’s hockey displayed the highest median PAV hor (5.7 rad/s). Men’s football demonstrated the highest 95 th percentile PAV for all axes. Post-hoc pairwise comparisons highlighted men’s hockey vs women’s rugby, and men’s hockey vs men’s football to consistently showing no significant differences across all axes. For PAA, men’s football displayed the highest median PAA cor (1332 rad/s 2 ), PAA sag (966 rad/s 2 ), and PAA hor (749 rad/s 2 ), and highest 95 th percentile PAA for all axes. Interestingly, men’s hockey vs. women’s rugby remained the only non-significant comparison across PAA. Across all resultant values, men’s football had the highest median PLA R (17.5 g), PAV R (11.1 rad/s), and PAA R (1789 rad/s²), while women’s soccer had the lowest (13.1 g, 5.5 rad/s, 786 rad/s²). Post-hoc analysis revealed that most pairwise comparisons were significant, with a few exceptions. For PLA R , differences were not significant between men’s hockey and women’s rugby, men’s hockey and women’s soccer, or between men’s football and women’s rugby. For PAV R , no significant differences were found between men’s hockey and women’s rugby, men’s hockey and men’s football, or men’s football and women’s rugby. Lastly, for PAA R , all pairwise comparisons were significant except between men’s hockey and women’s rugby. Impulse Duration: Comparing linear acceleration impulse durations of the maximum axis, men’s hockey and women’s rugby exhibited longer median durations at 11 ms and 10 ms, respectively, while men’s football and women’s soccer were shorter at 9 ms and 8 ms (Fig. 2A). For angular acceleration impulse durations, men’s hockey again displayed the longest median duration of 10.5 ms, closely followed by women’s rugby at 10 ms. Men’s football and women’s soccer were lower at 7 ms and 8 ms, respectively (Fig. 2B). Statistical comparisons revealed significant differences in linear impulse durations across all sports using the Kruskal-Wallis test, however post-hoc tests highlighted non-significant findings between men’s hockey vs. women’s rugby, men’s football vs. women’s rugby, and men’s football vs. women’s soccer (Supplementary table 3). Similarly, angular impulse durations were significantly different between all sports, except for men’s hockey vs. women’s rugby and men’s football vs. women’s soccer. (Supplementary table 2). Acceleration Direction: Linear directionality analysis showed men’s hockey to display a predominant proportion of leftward (21.9%) and rightward (22.6%) accelerations. Men’s football had primarily backwards impact accelerations (34.3%) while women’s soccer consisted of primarily upwards accelerations (59.4%). Women’s rugby had a more distributed impact classification with most impacts being leftward (32.6%) accelerations (Fig. 3A & Table 2). Chi-squared tests and post-hoc comparisons showed statistically significant differences between all sports for linear acceleration directions. Table 2 Peak linear acceleration directions for all sports Sport Upward Downward Forward Backward Leftward Rightward Men’s Hockey 20.6% 7.5% 11.6% 15.8% 21.9% 22.6% Men’s Football 11.2% 7.7% 4.7% 34.3% 18.9% 23.2% Women’s Soccer 59.4% 12.6% 4.5% 21.1% 1.2% 1.2% Women’s Rugby 4.2% 11.4% 9.4% 23.8% 32.6% 18.6% Table 3 Peak angular acceleration direction for all sports Sport Forward Sagittal Backward Sagittal Sagittal Leftward Coronal Rightward Coronal Coronal Leftward Horizontal Rightward Horizontal Horiz. Men’s Hockey 19.2% 16.4% 35.6% 15.1% 18.5% 33.6% 10.3% 20.5% 30.8% Men’s Football 3.4% 24.5% 27.9% 28.4% 36.9% 65.3% 3.4% 3.4% 6.8% Women’s Soccer 3.4% 79.2% 82.6% 4.3% 6.9% 11.2% 3.9% 2.3% 6.2% Women’s Rugby 22.1% 10.7% 32.8% 21.8% 20.6% 42.4% 13.7% 11.1% 24.8% Men’s football displayed the most impacts where the skull rotates in the coronal plane (leftward: 28.4%, rightward: 36.9%). Women’s soccer had a large proportion of impacts where the skull is rotating backwards in the sagittal plane (79.2%) (Fig. 3B & Table 3). Both men’s hockey and women’s rugby had relatively more equal distributions of angular directions. Chi-squared tests followed by post-hoc tests revealed statistically significant differences between all sports for angular acceleration directions. Sagittal and Coronal Ratios: Of the 1250 head impacts, 570 were sagittal dominant, with 34 from men’s hockey, 46 from men’s football, 69 from women’s rugby, and 412 from women’s soccer (Fig. 4A). Women’s soccer displayed the highest percentage of sagittal dominant impacts (73%) while men’s football had the least (20%). Men’s football exhibited the highest median rSagittal length of 15.9cm while men’s hockey displayed the lowest median with 9.7cm (Fig 4B). We also found 299 coronal dominant impacts with 43 from men’s hockey, 124 from men’s football, 104 from women’s rugby, and 28 from women’s soccer (Fig. 4A). Men’s football had the highest percentage of rCoronal impacts (53%) while women’s soccer had the least (5%). Men’s hockey displayed the highest median rCoronal lengths with 13.8 cm while men’s football had the lowest median rCoronal length of 4.6cm (Fig. 4B). Kruskal-Wallis tests highlighted significant differences between all sports for rSagittal and rCoronal ratios. Post-hoc tests highlighted significant pairwise comparisons between all comparisons for rSagittal except between men’s football and women’s rugby, and men’s hockey and women’s soccer. Further, rCoronal showed significant differences between all pairwise comparisons except men’s football vs. women’s soccer and men’s hockey vs. women’s rugby (Supplementary table 4). Magnitude and Frequency Clusters: For both the magnitude and frequency feature groups, k-means clustering identified k = 2 as the most optimal (i.e., two clusters), with silhouette scores of 0.45 and 0.37, respectively. Clustering with magnitude features identified two clusters that may be differentiated into low magnitude (Cluster 1) and high magnitude (Cluster 2) clusters based on resultant peak kinematics (Fig. 5A). They may also be differentiated by directional kinematics, where the low magnitude cluster consisted of primarily sagittal plane kinematics, and the high magnitude cluster showed higher coronal plane kinematics. Of those two clusters, women’s soccer impacts were found to largely represent the low magnitude cluster (79.9% of all total impacts) (Cluster 1), whereas the high magnitude cluster comprised of impacts mostly from men’s football, men’s hockey, and women’s rugby (Cluster 2). Frequency feature clustering also comprised of low frequency (Cluster 1) and high frequency (Cluster 2) clusters, mainly differentiated by low vs. high peak frequencies but comparable ranges of bandwidths (Fig. 5B). Similar to the magnitude features, the lower frequency cluster (Cluster 1) was moderately dominated by women’s soccer (65.8% of all total impacts), while the higher frequency cluster (Cluster 2) had a relatively even distribution of other sports. For magnitude-based features and clustering, WCSS analysis revealed that PAV cor had the greatest influence on cluster centroid displacement, while PAA sag had the least. Similarly, for frequency-based features, angular frequency in the sagittal direction caused the highest centroid shifts, whereas linear frequencies in the IS direction had the lowest effect. Interactions of Feature Groups: Interactions of magnitude and frequency features highlight some interesting sport specific similarities and differences (Fig. 5C). Both men’s hockey and men’s football impacts were mostly high magnitude with more even distribution in low and high frequency clusters. Men’s football and women’s rugby showed slight dominance in impacts with high magnitude and low frequency (48.9% and 54.4% of all impacts, respectively). Lastly, women’s soccer demonstrated mostly low magnitude and low frequency impacts (62.2% of all impacts). t-SNE Trained on all Feature Groups: To further explore our impact dataset, we trained our t-SNE model on all of our features to further observe any sport-specific clustering, as t-SNE is a useful tool to determine similarities in high dimensional data. We generally observed tighter clustering of women’s soccer impacts, consistent with k-means findings, while other sports impacts showed more overlapping patterns (Fig. 6). Some sport-specific small subclusters were noted and further analyzed to examine if they belonged to specific players or playing positions, yet no conclusive patterns were found. 4. Discussion We uniformly processed raw iMG head impact data across four sports—men’s football, men’s hockey, women’s rugby, and women’s soccer—and compared their biomechanical features. For objective (i), we identified statistically significant differences across sports in the distributions of peak kinematics, impulse durations, coupling ratios, and resultant acceleration directionality. However, for objective (ii), our clustering results highlighted cross-sport similarities, suggesting that head impacts may exhibit shared biomechanical characteristics across sports. Men’s football exhibited the highest median peak resultant PLA, PAV, and PAA, while women’s soccer had the lowest across all resultant kinematic metrics. Comparing our kinematic magnitudes to previous literature remains challenging due to the wide variability in reported kinematics for similar impact scenarios. A non-comprehensive review of prior studies showed a wide range of reported peak kinematics in the four sports we examined, with mean PLA and PAA ranges of 21 g – 32 g and 1017 rad/s² – 2120 rad/s² for men’s football [ 31 – 34 ], 19–40 g and 1750–3500 rad/s² for men’s hockey [ 10 , 35 ], 11 g – 31 g and 800 rad/s² − 4291 rad/s² for women’s rugby [ 36 – 38 ], and 5 g – 39 g and 1750 rad/s² – 7713 rad/s² for women’s soccer [ 39 – 41 ]. Notably, our study reports values on the lower end of these ranges, perhaps reflecting improvements in data quality and standardization using instrumented mouthguard sensors. A cross-sport comparison study by Naunheim et al. [ 42 ] in 2000, used a helmet-mounted sensor, and found men’s football to have the lowest mean PLA (29.2 g), followed by hockey (35 g) and soccer the highest (54.7 g), which not only show substantially higher kinematics for all sports compared with our study, but also opposite trends in cross-sport impact severity. More recently, using iMG sensors, Gabler et al. [ 43 ] reported median kinematics for collegiate men’s football at 17 g and 1180 rad/s², and Miller et al., [ 44 ] reported soccer heading with 9.4 g and 689 rad/s 2 which align more closely with our current findings. Therefore, we believe as sensor accuracy continues to improve and processing methods become more standardized, cross-sport comparisons will become more consistent and reliable. Notably, men’s football exhibited substantially higher coronal rotational kinematics compared with the other sports, with a relatively large number of high rotation impacts (14% impacts over 5,000 rad/s 2 of coronal angular acceleration). Interestingly, while women’s soccer impacts had generally lower peak resultant values, the median and 75th percentile inferior-superior peak linear accelerations in soccer impacts were the highest among sports. In addition, women’s soccer median and 75th percentile sagittal angular acceleration were second highest among sports. Since the soccer heading impacts were primarily in the sagittal plane, our directional magnitude analyses revealed that women’s soccer impacts can exhibit higher kinematics relative to other sports in certain directional measures, even though traditional resultant values would indicate lowest severity impacts. As it is known there can be direction-dependence of injury risk, and some injury risk criteria such as the Brain Injury Criterion (BrIC) have established directional thresholds, our findings show that directional kinematics analyses are important when investigating sport head injury risks. Our acceleration direction analyses also revealed some impact directionality differences across sports that may have injury risk implications. From Tables 2 , 3 and Fig. 4 A, we see that only women’s soccer showed predominantly sagittal impacts during heading (82.6% peak angular accelerations in sagittal plane). Further, one interesting observation was the dominance of upward acceleration observed in women’s soccer impacts (59.4%). Upon reviewing the data, we found that women’s soccer impacts typically exhibited two impulsive peaks in succession, one from the initial ball-head contact (typically to the frontal hairline region), and the second from an opposite acceleration impulse likely due to the reaction force from the head-neck stopping at the end range of motion. The upwards accelerations corresponded to the second peak, which often surpassed the magnitude of the first peak. This observation shows that traditional peak analysis may not fully capture the complexities of impact biomechanics in sports, since one may have originally considered the ball-to-head impact to be the more important to analyze for injury considerations, while we are finding that the ‘rebound’ impact can be higher magnitude. Men’s football impacts also showed some dominance in coronal peak angular accelerations (65.3%), and combined with the earlier observation of higher peak kinematics distributions compared with other sports, may prompt further investigation of coronal impacts and injury risk considerations for football. Early primate studies have shown that the coronal plane may have lower tolerance to diffuse axonal injury [ 45 ]. From our analysis of sagittal and coronal ratios, we found that men’s football had substantially lower ratios in coronal impacts, which is consistent with the observation that for a similar amount of linear accelerations, higher angular accelerations were seen in football impacts. Past work investigating the instantaneous head centre-of-rotation has noted the importance of understanding head/neck/torso and spinal constraints in investigating head rotational kinematics [ 46 ]. Interestingly, this prior laboratory-based study showed higher ratios in the coronal plane than the sagittal plane [ 46 ], which is consistent with what we saw in men’s hockey, but opposite to our findings in football impacts, demonstrating potential on-field differences in head-neck-torso engagement during impact. Overall, men’s hockey and women’s rugby showed more balanced impact directionalities, and substantially more horizontal impacts compared with men’s football and women’s soccer. Past literature have also indicated potential horizontal plane vulnerability [ 47 ] that may be important to consider for these sports. While we included both men’s and women’s data, this unique iMG-only dataset did not include matched sports to enable a sex-based analysis. Despite this, we can make preliminary observations that women’s head impacts had comparable ranges of peak kinematics as men’s head impacts in our study, especially in women’s rugby. This would challenge the theory of weaker neck muscles in women leading to more severe impacts and subsequently higher injury risk [ 48 , 49 ]. Since our study quantified sagittal and coronal linear-angular ratios, we expected these ratios to approximate the distance between the sensor and the instantaneous center of rotation. If neck length played a significant role, we anticipated higher ratios in men’s impacts compared to women’s. However, we also did not find consistent sex-based differences. Various factors, including anthropometrics, neck muscle strength, and playing style, may all contribute to differences in impact biomechanics and subsequent injury risk. Further studies with matched sports between men’s and women’s teams can further investigate these factors. Our comparison of impulse durations along with clustering of frequency-domain features give insight to the temporal dynamics and frequency characteristics of impact biomechanics across sports. While most studies focus on reporting peak head kinematics metrics, injury biomechanics literature have highlighted the importance of examining not only peak acceleration, but also the duration of the acceleration impulse when defining concussion risk curves [ 14 , 50 ]. Theoretical structural mechanics analyses and earlier cadaver/animal research [ 51 ] have derived injury risk curves that predict higher tolerance to peak accelerations at short durations and lower tolerance to peak accelerations at long durations. Our analyses have shown a wide range of impulse durations from 3 ms to 50 ms or longer, with largely overlapping ranges across sports. Interestingly, despite the expectation that helmet padding should lengthen impulse durations and mitigate high frequency content [ 52 ] we did not observe substantial differences in impulse duration between helmeted (linear median: 9 ms, angular median: 8 ms) and unhelmeted (linear median: 9 ms, angular median: 9 ms) impacts. Additionally, we found the helmeted impacts from men’s football and men’s hockey to have a greater proportion of impacts in the high frequency cluster. It is worth noting the measured frequency-dependence of brain material behaviour [ 53 ], and the potential existence of low-frequency resonance behaviour of brain tissue [ 54 ] that may prompt future studies to also consider these biomechanical parameters in conjunction with impact magnitude when determining injury risk implications. Overall, combining the findings from the two objectives, we observe that despite some notable distribution differences in biomechanical parameters, direct head impacts in the sports we examined have largely overlapping biomechanical characteristics. Women’s soccer impacts, consisting solely of heading events, exhibited the tightest biomechanical parameter distributions. Since soccer heading impacts involve largely similar directionalities and the same impacting object, they are highly similar to each other in these biomechanical features, which we observed in the majority of soccer impacts falling into the same magnitude and frequency clusters. With more varying directionality and impact scenarios in other sports, we find less clear sport distinction in the clustering analysis, despite large differences in protective equipment use and gameplay dynamics. Even within soccer heading, a small proportion of impacts have similar characteristics as those found in other sports with some of these impacts found in the high magnitude and high frequency clusters. With the T-SNE analysis that optimized for cluster formation by preserving local structures in the high dimensional feature space, there did not seem to be clear sport-specific clustering of features. Overall, we show that sport head impacts span a continuous spectrum of biomechanical parameters, and combining multisport impact data along with epidemiological considerations may give a bigger picture view of the relationship between biomechanical parameters and brain injury risks. This study has some limitations that should be considered. Our analysis focused solely on direct head impacts, excluding indirect impacts that frequently occur in sports. These indirect impacts may also contribute to brain injury risk and could alter the biomechanical comparisons across sports. Furthermore, the soccer dataset had only headers and no other recorded impact scenario (e.g., elbow-to-head, head-to-head, etc.,), which may limit the generalizability of all impacts experienced in soccer. Additionally, while we aimed to standardize sensor settings and data processing techniques, we may not be able to fully address inherent sensor hardware differences in post-processing. Also, the standardization process of down sampling and truncating impacts from some sensors may have resulted in loss of frequency- and time-domain information that we are unable to compare (e.g., normalized filter cutoffs to lowest available sensor bandwidth, reducing 100–150 ms impacts to only 50 ms). Moreover, our clustering model was trained on a select set of biomechanical features rather than an exhaustive list of all possible parameters, to focus on key parameters with injury risk implications, reduce redundancy, and improve the interpretability of findings. Including additional features may alter the observed clustering patterns. Additionally, a sport-matched comparison of sex differences was not feasible with our dataset, despite some preliminary observations of similarities in the men’s and women’s head impacts. A more controlled prospective study design could provide further insights into sex-based similarities or differences in head impact biomechanics. 5. Conclusions This study examined head impact biomechanics across multiple sports through a comprehensive analysis of various biomechanical parameters that have been implicated in brain injury risk. We uniquely curated a multisport dataset collected using iMG-type sensors. After controlling for sensor setting differences and performing uniform processing, we note interesting differences and similarities in biomechanical characteristics across men’s hockey, men’s football, women’s rugby, and women’s soccer head impacts. While men’s football showed highest peak resultant kinematics and women’s soccer showed the lowest, consistent with past observations of relative impact magnitudes, directional kinematics analyses revealed more nuanced patterns. For example, women’s soccer impacts exhibited relatively high sagittal peak linear and rotational kinematics. From our clustering analyses, we show that aside from women’s soccer demonstrating biomechanically similar impacts from heading, there may be shared biomechanical signatures across sports head impacts. These findings suggest that standardized multi-sport analyses may offer greater insights into brain injury mechanisms than isolated sport-specific studies. Future research should leverage similar standardized methodologies across expanded datasets to further elucidate the relationships between the full spectrum of impact characteristics and brain injury risk, ultimately informing more effective protective strategies across sports. Declarations Funding This study was funded by the Canadian Institute of Health Research, Michael Smith Health Research British Columbia, Canada Research Chairs program, British Columbia Knowledge Development Fund, and Canada Foundation for Innovation. Conflicts of Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this study. Availability of data and material The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Ethics approval We are using a curated dataset combining published data. Each dataset had research ethics board approval as detailed in the original publications. Code availability Code is available upon request to the corresponding author. Author Contributions Zaryan Masood: Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Conceptualization. David Luke: Writing – review & editing, Data curation, Formal analysis. Rebecca Kenny: Writing – review & editing, Data curation. Daniel Bondi: Writing – review & editing, Data Curation Adam Clansey: Writing – review & editing, Data curation. Lyndia C. Wu: Conceptualization, Funding acquisition, Data curation, Writing – review & editing, Visualization, Investigation, Validation, Formal Analysis, Methodology, Supervision, Resources, Project administration. Acknowledgements: We acknowledge that we conducted this research work at the UBC Vancouver (Point Grey) Campus, which is located on the traditional, ancestral, unceded territory of the xʷməθkʷəy̓əm (Musqueam) people. We thank the Canadian Institute of Health Research (CIHR), Michael Smith Health Research British Columbia (MSHRBC), Canada Research Chairs Program, British Columbia Knowledge Development Fund (BCKDF), and Canada Foundation for Innovation (CFI) for providing funding support for this research. Additionally, this study would not be possible without the enthusiastic and continued support of the University of British Columbia’s (UBC) men’s hockey, women’s rugby, and Stanford men’s football team athletes, coaches, and staff. References Yue, J. K., Upadhyayula, P. S., Avalos, L. N., Phelps, R. R. L., Suen, C. G. & Cage, T. A. Concussion and mild-traumatic brain injury in rural settings: Epidemiology and specific health care considerations. J. Neurosci. Rural Pract. 11, 23 (2020). https://doi.org/10.1055/s-0039-3402581 Halstead, M. E., Walter, K. D. & Moffatt, K. Sport-related concussion in children and adolescents. Pediatrics 142, e20183074 (2018). https://doi.org/10.1542/peds.2018-3074 Rawlings, S., Takechi, R. & Lavender, A. P. Effects of sub-concussion on neuropsychological performance and its potential mechanisms: A narrative review. Brain Res. Bull. 165, 56–62 (2020). https://doi.org/10.1016/j.brainresbull.2020.09.021 Camarillo, D. B., Shull, P. B., Mattson, J., Shultz, R. & Garza, D. An instrumented mouthguard for measuring linear and angular head impact kinematics in American football. Ann. Biomed. Eng. 41, 1939–1949 (2013). https://doi.org/10.1007/s10439-013-0801-y Ji, S., Zhao, W., Li, Z. & McAllister, T. W. Head impact accelerations for brain strain-related responses in contact sports: A model-based investigation. Biomech. Model. Mechanobiol. 13, 1121–1136 (2014). https://doi.org/10.1007/s10237-014-0562-z Newman, J. A. Biomechanics of human trauma: Head protection. In Accidental Injury (eds Nahum, A. M. & Melvin, J. W.) 292–310 (Springer, 1993). https://doi.org/10.1007/978-1-4757-2264-2_13 Pellman, E. J., Viano, D. C., Withnall, C., Shewchenko, N., Bir, C. A. & Halstead, P. D. Concussion in professional football: Helmet testing to assess impact performance – Part 11. Neurosurgery 58, 78–95 (2006). https://doi.org/10.1227/01.NEU.0000196265.35238.7C Pellman, E. J. et al. Concussion in professional football: Location and direction of helmet impacts – Part 2. Neurosurgery 53, 1328–1341 (2003). https://doi.org/10.1227/01.neu.0000093499.20604.21 .Poutiainen, P., Peltonen, J., Isolehto, J. & Avela, J. Comparison of impact characteristics of four different ice hockey arena dasher boards. Eur. J. Sport Sci. 14, 11–18 (2014). https://doi.org/10.1080/17461391.2012.725106 Wilcox, B. J., Machan, J. T., Beckwith, J. G., Greenwald, R. M., Burmeister, E. & Crisco, J. J. Head-impact mechanisms in men’s and women’s collegiate ice hockey. J. Athl. Train. 49, 514–520 (2014). https://doi.org/10.4085/1062-6050-49.3.19 Jones, C. M. & Brown, M. Rowan. Validation of an instrumented mouthguard. medRxiv 22271563; https://doi.org/10.1101/2022.03.02.22271563 (2022). Jones, B. et al. Ready for impact? A validity and feasibility study of instrumented mouthguards (iMGs). Br. J. Sports Med. 56, 1171–1179 (2022). https://doi.org/10.1136/bjsports-2022-105523 Gellner, R., Begonia, M. & Rowson, S. Choosing optimal cutoff frequencies for filtering linear acceleration and angular velocity signals associated with head impacts measured by instrumented mouthguards. Ann. Biomed. Eng. 52, 1415–1424 (2024). https://doi.org/10.1007/s10439-024-03466-4 Hoshizaki, T. B. et al. The development of a threshold curve for the understanding of concussion in sport. Trauma 19, 196–206 (2017). https://doi.org/10.1177/1460408616676503 Tiernan, S. & Byrne, G. The effect of impact location on brain strain. Brain Inj. 33, 427–434 (2019). https://doi.org/10.1080/02699052.2019.1566834 Laksari, K., Kurt, M., Babaee, H., Kleiven, S. & Camarillo, D. Mechanistic insights into human brain impact dynamics through modal analysis. Phys. Rev. Lett. 120, 138101 (2018). https://doi.org/10.1103/PhysRevLett.120.138101 Hernandez, F. et al. Six degree-of-freedom measurements of human mild traumatic brain injury. Ann. Biomed. Eng. 43, 1918–1934 (2015). https://doi.org/10.1007/s10439-014-1212-4 Wu, L. C. et al. Detection of American football head impacts using biomechanical features and support vector machine classification. Sci. Rep. 8, 17864 (2017). https://doi.org/10.1038/s41598-017-17864-3 Kenny, R., Elez, M., Clansey, A., Virji-Babul, N. & Wu, L. C. Head impact exposure and biomechanics in university varsity women’s soccer. Ann. Biomed. Eng. 50, 1461–1472 (2022). https://doi.org/10.1007/s10439-022-02914-3 Clansey, A. C. et al. On-field head acceleration exposure measurements using instrumented mouthguards: Multi-stage screening to optimize data quality. Ann. Biomed. Eng. 52, 2666–2677 (2024). https://doi.org/10.1007/s10439-024-03592-z Luke, D., Kenny, R., Bondi, D., Clansey, A. C. & Wu, L. C. On-field instrumented mouthguard coupling. J. Biomech. 162, 111889 (2024). https://doi.org/10.1016/j.jbiomech.2023.111889 Bartsch, A. J. et al. Laboratory and on-field data collected by a head impact monitoring mouthguard. Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. (EMBS) 2068–2072 (2019). https://doi.org/10.1109/EMBC.2019.8856907 Rich, A. M. et al. Development, validation and pilot field deployment of a custom mouthpiece for head impact measurement. Ann. Biomed. Eng. 47, 2109–2121 (2019). https://doi.org/10.1007/s10439-019-02313-1 Wu, L. C., Zarnescu, L., Nangia, V., Cam, B. & Camarillo, D. B. A head impact detection system using SVM classification and proximity sensing in an instrumented mouthguard. IEEE Trans. Biomed. Eng. 61, 2659–2668 (2014). https://doi.org/10.1109/TBME.2014.2320153 Rea, L. M. & Parker, R. A. Designing and conducting survey research: A comprehensive guide. John Wiley & Sons. (2014). Arthur, D. & Vassilvitskii, S. k-means++: The advantages of careful seeding. In Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms 1027–1035 (Society for Industrial and Applied Mathematics, 2007). Shahapure, K. R. & Nicholas, C. Cluster quality analysis using silhouette score. 747–748 (2020). https://doi.org/10.1109/DSAA49011.2020.00096 Brusco, M. J. & Steinley, D. A comparison of heuristic procedures for minimum within-cluster sums of squares partitioning. Psychometrika 72, 583–600 (2007). https://doi.org/10.1007/s11336-007-9013-4 Alghofaili, Y. Interpretable k-means: Clusters feature importances. Medium. https://medium.com/tdsarchive Van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008). Martini, D., Eckner, J., Kutcher, J. & Broglio, S. Subconcussive head impact biomechanics: Comparing differing offensive schemes. Med. Sci. Sports Exerc. 45, 755 (2013). https://doi.org/10.1249/MSS.0b013e3182798758 Broglio, S. P., Surma, T. & Ashton-Miller, J. A. High school and collegiate football athlete concussions: A biomechanical review. Ann. Biomed. Eng. 40, 37–46 (2012). https://doi.org/10.1007/s10439-011-0396-0 Rowson, S., Brolinson, G., Goforth, M., Dietter, D. & Duma, S. Linear and angular head acceleration measurements in collegiate football. ASME J. Biomech. Eng. 061016 (2009). https://doi.org/10.1115/1.3130454 Mihalik, J. P., Bell, D. R., Marshall, S. W. & Guskiewicz, K. M. Measurement of head impacts in collegiate football players: An investigation of positional and event-type differences. Neurosurgery 61, 1229–1235 (2007). https://doi.org/10.1227/01.neu.0000306101.83882.c8 Schmidt, J. D. et al. Safe-play knowledge, aggression, and head-impact biomechanics in adolescent ice hockey players. J. Athl. Train. 51, 366–372 (2016). https://doi.org/10.4085/1062-6050-51.5.04 Williams, E. M. P. et al. Sex differences in neck strength and head impact kinematics in university rugby union players. Eur. J. Sport Sci. 22, 1649–1658 (2022). https://doi.org/10.1080/17461391.2021.1973573 Langevin, T. L. et al. Head impact exposures in women’s collegiate rugby. Phys. Sportsmed. 49, 68–73 (2021). https://doi.org/10.1080/00913847.2020.1770568 Petrie, F. Analysis of head acceleration kinematics in collegiate and elite women’s rugby union. Thesis or report (2021). (❗ If published, update source.) McCuen, E. et al. Collegiate women’s soccer players suffer greater cumulative head impacts than their high school counterparts. J. Biomech. 48, 3720–3723 (2015). https://doi.org/10.1016/j.jbiomech.2015.08.003 Lamond, L. C. et al. Linear acceleration in direct head contact across impact type, player position, and playing scenario in collegiate women’s soccer players. J. Athl. Train. 53, 115–121 (2018). https://doi.org/10.4085/1062-6050-90-17 Filben, T. M. et al. Characterization of head impact exposure in women’s collegiate soccer. J. Appl. Biomech. 38, 2–11 (2021). https://doi.org/10.1123/jab.2020-0304 Naunheim, R. S., Standeven, J., Richter, C. & Lewis, L. M. Comparison of impact data in hockey, football, and soccer. J. Trauma 48, 938–941 (2000). Gabler, L. F. et al. Distribution of position-specific head impact severities among professional and Division I collegiate American football athletes during games. BMJ Open Sport Exerc. Med. 11, e002365 (2025). https://doi.org/10.1136/bmjsem-2024-002365 Miller, L. E. et al. Characterizing head impact exposure in youth female soccer with a custom-instrumented mouthpiece. Res. Sports Med. 28, 55–71 (2020). https://doi.org/10.1080/15438627.2019.1590833 Gennarelli, T. A. et al. Diffuse axonal injury and traumatic coma in the primate. Ann. Neurol. 12, 564–574 (1982). https://doi.org/10.1002/ana.410120611 Kuo, C., Fanton, M., Wu, L. & Camarillo, D. Spinal constraint modulates head instantaneous center of rotation and dictates head angular motion. J. Biomech. 76, 220–228 (2018). https://doi.org/10.1016/j.jbiomech.2018.05.024 Hernandez, F. & Camarillo, D. B. Voluntary head rotational velocity and implications for brain injury risk metrics. J. Neurotrauma 36, 1125–1135 (2019). https://doi.org/10.1089/neu.2016.475 Vasavada, A. N., Danaraj, J. & Siegmund, G. P. Head and neck anthropometry, vertebral geometry and neck strength in height-matched men and women. J. Biomech. 41, 114–121 (2008). https://doi.org/10.1016/j.jbiomech.2007.07.007 Hrysomallis, C. Neck muscular strength, training, performance and sport injury risk: A review. Sports Med. 46, 1111–1124 (2016). https://doi.org/10.1007/s40279-016-0490-4 Goldsmith, W. & Monson, K. L. The state of head injury biomechanics: Past, present, and future. Part 2: Physical experimentation. Crit. Rev. Biomed. Eng. 33, 105–207 (2005). https://doi.org/10.1615/CritRevBiomedEng.v33.i2.20 Hirsch, A. E. & Ommaya, A. K. Protection from brain injury: The relative significance of translational and rotational motions of the head after impact. SAE Tech. Pap. 700899 (1970). https://doi.org/10.4271/700899 Kuhn, E. N. et al. Youth helmet design in sports with repetitive low- and medium-energy impacts: A systematic review. Sports Eng. 20, 29–40 (2017). https://doi.org/10.1007/s12283-016-0215-9 Lv, H. et al. MR elastography frequency–dependent and independent parameters demonstrate accelerated decrease of brain stiffness in elder subjects. Eur. Radiol. 30, 6614–6623 (2020). https://doi.org/10.1007/s00330-020-07054-7 Laksari, K., Wu, L. C., Kurt, M., Kuo, C. & Camarillo, D. C. Resonance of human brain under head acceleration. J. R. Soc. Interface 12, 20150331 (2015). https://doi.org/10.1098/rsif.2015.0331 Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.docx Cite Share Download PDF Status: Published Journal Publication published 19 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 16 Sep, 2025 Reviews received at journal 16 Jun, 2025 Reviewers agreed at journal 08 Jun, 2025 Reviewers agreed at journal 06 Jun, 2025 Reviewers invited by journal 14 May, 2025 Editor assigned by journal 14 May, 2025 Editor invited by journal 14 May, 2025 Submission checks completed at journal 05 May, 2025 First submitted to journal 05 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6566803","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":456655300,"identity":"0b7c9b6e-f595-4c76-8e1c-452d38f0d474","order_by":0,"name":"Zaryan Masood","email":"","orcid":"","institution":"University of British Columbia","correspondingAuthor":false,"prefix":"","firstName":"Zaryan","middleName":"","lastName":"Masood","suffix":""},{"id":456655301,"identity":"8f19bc61-2d1e-4f88-b576-70d4084f6bd4","order_by":1,"name":"David Luke","email":"","orcid":"","institution":"University of British Columbia","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Luke","suffix":""},{"id":456655302,"identity":"5a41ceb4-445f-4350-aa72-c77abe42ddc1","order_by":2,"name":"Rebecca Kenny","email":"","orcid":"","institution":"University of British Columbia","correspondingAuthor":false,"prefix":"","firstName":"Rebecca","middleName":"","lastName":"Kenny","suffix":""},{"id":456655303,"identity":"89b023d3-be89-4fe7-9b96-ca521aa4dc70","order_by":3,"name":"Daniel Bondi","email":"","orcid":"","institution":"University of British Columbia","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Bondi","suffix":""},{"id":456655304,"identity":"03418b61-a1d6-4ddb-97a6-2a60ce7d2310","order_by":4,"name":"Adam Clansey","email":"","orcid":"","institution":"University of British Columbia","correspondingAuthor":false,"prefix":"","firstName":"Adam","middleName":"","lastName":"Clansey","suffix":""},{"id":456655305,"identity":"9bd689a6-6d19-4a00-ab78-76a2935ce370","order_by":5,"name":"Lyndia Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAo0lEQVRIiWNgGAWjYBACPgYGxgdAOoGBgYdILWwMDMwGJGthkyBRi0SOWdWNCrs8Boncgx8YauyI03I750xyMYNEXrIEw7FkIrRIA7Xkth1IbADqZWBsYCZOS3HuP7iWeuK0MOc2wLUcJkKL/LNi6ZxjyYltPO+SJRKOHSeshZ/n8MbPOTV2if3swBD7UFNNWAvCOhCRQIKGUTAKRsEoGAV4AACIai/JUmabVQAAAABJRU5ErkJggg==","orcid":"","institution":"University of British Columbia","correspondingAuthor":true,"prefix":"","firstName":"Lyndia","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2025-04-30 16:53:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6566803/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6566803/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-31145-4","type":"published","date":"2025-12-19T15:57:25+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82902389,"identity":"2f722b31-282d-4f47-9fb3-8e7d4f2bef47","added_by":"auto","created_at":"2025-05-16 13:40:00","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":129197,"visible":true,"origin":"","legend":"\u003cp\u003ePer axis median (A) peak linear acceleration (PLA), (B) peak angular velocity (PAV), and (C) peak angular acceleration (PAA) across all sports.\u003cstrong\u003e \u003c/strong\u003eScatter points indicate all impacts for each sport. Boxplots indicate median (horizontal black line) with 25\u003csup\u003eth\u003c/sup\u003e and 75\u003csup\u003eth\u003c/sup\u003e percentiles. AP: Anterior-Posterior, LR: Left-Right, IS: Inferior-Superior, Cor: Coronal, Sag: Sagittal, Hor: Horizontal, R: Resultant.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6566803/v1/8471bd97f11b51719e3a256d.jpg"},{"id":82903149,"identity":"21de8c67-072d-4ffe-bf52-de141bafc794","added_by":"auto","created_at":"2025-05-16 13:48:00","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":50823,"visible":true,"origin":"","legend":"\u003cp\u003eMedian linear (A) and angular (B) impulse duration across all sports.\u003cstrong\u003e \u003c/strong\u003eScatter points indicate all impacts for each sport. Boxplots indicate median (horizontal black line) with 25\u003csup\u003eth\u003c/sup\u003e and 75\u003csup\u003eth\u003c/sup\u003e percentiles\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6566803/v1/34c53ca26d153afba4dd7c46.jpg"},{"id":82902390,"identity":"5a787ef6-e322-4619-8e04-d1ecce911af9","added_by":"auto","created_at":"2025-05-16 13:40:00","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":139342,"visible":true,"origin":"","legend":"\u003cp\u003eLinear (A) and angular (B) classification of head accelerations for each sport in all axes.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6566803/v1/27b2abbe0849d2c47c1cdfc3.jpg"},{"id":82902396,"identity":"2a49a088-1086-4602-a25c-e242d707c48e","added_by":"auto","created_at":"2025-05-16 13:40:00","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":56181,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Distribution of dominant sagittal and coronal impacts for each sport and (B) median of sagittal acceleration ratio (rSagittal) and coronal acceleration ratio (rCoronal) impacts across all sports. Scatter points indicate all impacts for each sport. Boxplots indicate median (horizontal black line) with 25\u003csup\u003eth\u003c/sup\u003e and 75\u003csup\u003eth\u003c/sup\u003e percentiles\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6566803/v1/10f144d2705796763e07a033.jpg"},{"id":82902392,"identity":"f759ca2f-5313-4bc3-a6af-18efc3fd024f","added_by":"auto","created_at":"2025-05-16 13:40:00","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":129165,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Two optimal magnitude clusters with their respective cluster median peak linear acceleration (PLA), peak angular velocity (PAV), and peak angular acceleration (PAA). (B) Three optimal frequency clusters with their respective median linear and angular peak frequencies and bandwidths. (C) Interaction of both magnitude and frequency clusters with sport-specific distribution of impacts in clusters. AP: Anterior-Posterior, LR: Left-Right, IS: Inferior-Superior, Cor: Coronal, Sag: Sagittal, Hor: Horizontal\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6566803/v1/fea71dd322a1b94761a12182.jpg"},{"id":82902394,"identity":"5500241e-e8fe-4d34-9425-e498dce85671","added_by":"auto","created_at":"2025-05-16 13:40:00","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":72932,"visible":true,"origin":"","legend":"\u003cp\u003eAll head impacts visualized using t-SNE, trained on comprehensive biomechanical features, including peak linear acceleration (PLA), peak angular velocity (PAV), peak angular acceleration (PAA), as well as linear and angular frequencies and bandwidths across all directions\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6566803/v1/868bd9c18f714db3bff53569.jpg"},{"id":98814964,"identity":"29265531-a050-47c2-ac6e-2bc851435b65","added_by":"auto","created_at":"2025-12-22 16:13:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1471180,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6566803/v1/1e5a2baa-4e57-43da-a2d8-ff6c35002f07.pdf"},{"id":82903150,"identity":"562b592b-3585-48ad-a71c-52510d3746d6","added_by":"auto","created_at":"2025-05-16 13:48:00","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":25114,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6566803/v1/9c236b67fdf64885a8a428f3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Head Impact Biomechanics Across Men's and Women's Contact Sports: A Comparative and Clustering Analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eConcussion, or mild traumatic brain injury (mTBI), continues to be a major health concern for many contact sport athletes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and a leading cause of disability, especially in youth and adolescents [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. More recently, there is a growing body of literature suggesting repeated subconcussive events \u0026ndash; head impacts that do not lead to clinical concussion diagnosis \u0026ndash; can give rise to detectable neurophysiological changes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. To further understand how head impacts during sport are influencing brain health, researchers have applied wearable sensors to collect on-field head impact biomechanics data. Data from these sensors can be leveraged to quantify potential brain injury risk through head kinematics measures such as peak linear and angular accelerations [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] as well as simulated brain strains [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eVarious studies have deployed sensors to gather head impact data in sports, identifying common impact scenarios and mechanisms of head contact. Notably, a number of factors may influence head impact biomechanics across different sports. For example, protective headgear, such as helmets, may reduce peak head acceleration by dissipating impact energy [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and increasing impact duration [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Furthermore, the mechanics of impacting objects can also affect impact biomechanics, such as stiffness and damping differences between common helmet-to-helmet impacts in American football [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and helmet-to-board impacts in ice hockey [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Ice hockey, with typically higher speed on-ice collisions, may present greater momentum and higher force transferred during impacts [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These are just a few factors influencing impact biomechanics, with others\u0026mdash;including gameplay dynamics and sex\u0026mdash;also contributing to differences across sports.\u003c/p\u003e \u003cp\u003eHowever, cross-sport comparison of head impact biomechanics continues to be a challenge given the varying sensor types employed, differences in sensor specifications, and non-standardized data processing techniques. More recently, instrumented mouthguards (iMGs) have become the standard sensor type in recent field studies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], yet differences in sensor specifications and data processing methods still make comparing results across iMG studies a challenge. For example, Jones et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] demonstrated substantial differences in both on-field and in-lab peak kinematics across four commonly used iMGs for the same impact scenarios. Additionally, Gellner et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] highlighted sensitivity in peak kinematics to varying filter cut-off frequencies. These variations, combined with the tendency for most studies to focus on single-sport data collection and analyses, make cross-sport comparison of head impact biomechanics difficult. Furthermore, most analyses concentrate on peak head kinematics, and there is limited reporting of other biomechanical parameters implicated in brain injury risk, such as impulse durations [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], directionality [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and frequency characteristics [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUniquely, we curated raw iMG impact data across multiple sports and processed all data with a uniform pipeline to enable more controlled comparison of head impact biomechanics across university-level men\u0026rsquo;s ice hockey, men\u0026rsquo;s American football, women\u0026rsquo;s soccer, and women\u0026rsquo;s rugby. With this dataset, we had two primary objectives: (i) compare biomechanical features of iMG-measured, uniformly processed direct head impacts across sports, and (ii) use clustering methods to further examine if sports distinctly cluster by biomechanical features. We investigated diverse biomechanical features, including impact magnitude, impulse duration, acceleration direction, and frequency features that have been linked to brain injury risk.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Methodology Overview\u003c/h2\u003e \u003cp\u003eIn our comparison, we included only video-verified direct head impacts and conducted a rigorous data quality review to ensure a high-confidence dataset. As the curated dataset was collected using three different iMGs, we developed the uniform processing pipeline to address sensor setting differences such as varying recording threshold, recording duration and sample rate, as well as ensure standardized filtering methods. Using this standardized multisport dataset, for objective (i), we examined and compared the distributions of biomechanical features across sports, including impact magnitude, impulse duration, and directionality-focused features. For objective (ii), we applied an unsupervised k-means clustering algorithm and a t-distributed stochastic neighbour embedding (t-SNE) model to examine the clustering of impact magnitude and frequency features and determine if sport-specific clusters arise.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Dataset Descriptions\u003c/h2\u003e \u003cp\u003eA head impact database was curated from previously collected iMG data from university level men\u0026rsquo;s American football [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], women\u0026rsquo;s soccer [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], men\u0026rsquo;s ice hockey [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and women\u0026rsquo;s rugby [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] teams. Both men\u0026rsquo;s hockey and women\u0026rsquo;s rugby teams wore Prevent Biometrics [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] iMG\u0026rsquo;s. The women\u0026rsquo;s soccer team wore instrumented mouthpieces from Wake Forest Center for Injury Biomechanics [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and men\u0026rsquo;s football wore Stanford University iMG\u0026rsquo;s [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. All iMG impacts were video-verified and only direct head contacts were included in the analysis. This enables comparison of direct head impact biomechanics separately from inertial head acceleration events due to body contact, which can have very different mechanisms and are also usually more difficult to confidently verify through video analysis.\u003c/p\u003e \u003cp\u003eAll impact traces were visually inspected to keep only recordings where clearly observable linear and angular acceleration impulses could be identified around the time of trigger. The Prevent iMG, Wake Forest Mouthpiece, and Stanford University iMG all had varying initial recording settings including sampling frequency, trigger thresholds, and impact durations. Coupling of the iMG to the teeth was assessed for Prevent iMG-recorded impacts using proximity sensor data [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], where only well-coupled impacts were included for analysis. Part of the Stanford iMG dataset from Wu et al., 2017 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] used iMGs that included a similar proximity sensor to include only well-coupled impacts. The Wake Forest instrumented mouthpiece was a retainer-style device that was difficult to decouple from teeth without fully removing from the mouth, providing confidence of device coupling. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e includes the initial settings of all iMGs which were then harmonized to follow one uniform processing pipeline. All subjects were consented, and each dataset had research ethics board approval as detailed in the original publications.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInitial sensor specifications and uniform processing settings\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSensor Specifications\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrevent iMG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWake Forest Mouthpiece\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStanford iMG\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eInitial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSampling Frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3200 Hz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1000 Hz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1000 Hz\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLinear Acceleration Trigger\u003c/p\u003e \u003cp\u003e(axis)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 g\u003c/p\u003e \u003cp\u003e(any axis)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 g\u003c/p\u003e \u003cp\u003e(any axis)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 or 10 g\u003c/p\u003e \u003cp\u003e(resultant)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImpact Duration\u003c/p\u003e \u003cp\u003e(Pre-Trigger/Post-Trigger)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 ms\u003c/p\u003e \u003cp\u003e(10 ms/40 ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e150 ms\u003c/p\u003e \u003cp\u003e(30 ms/120 ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100 ms\u003c/p\u003e \u003cp\u003e(10 ms/90 ms)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eChanges Made\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSampling Frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDown-sampled to 1000 Hz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLinear Acceleration Trigger (Axis)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIncrease to 10 g\u003c/p\u003e \u003cp\u003e(Resultant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncrease to 10 g\u003c/p\u003e \u003cp\u003e(Resultant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncrease to 10 g\u003c/p\u003e \u003cp\u003e(Resultant)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImpact Duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDecrease to 50 ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDecrease to 50 ms\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eUniform\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSampling Frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e1000 Hz\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLinear Acceleration Trigger (axis)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e10 g\u003c/p\u003e \u003cp\u003e(resultant)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImpact Duration\u003c/p\u003e \u003cp\u003e(Pre-Trigger/Post-Trigger)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e50 ms\u003c/p\u003e \u003cp\u003e(10ms/40ms)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Processing Methodology\u003c/h2\u003e \u003cp\u003eWe applied a uniform pre-processing and processing script to raw sensor data to minimize potential sampling and processing differences (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We down-sampled the Prevent iMG to 1000 Hz and truncated the impact duration window for the Wake Forest mouthpiece and Stanford iMG to match the Prevent iMG (10 ms pre-impact, 40 ms post-impact). To account for trigger differences, we chose a uniform 10g resultant linear acceleration trigger for all data. We aligned and centered all the impacts to ensure a trigger at 10g resultant with a 50 ms impact window. Data were rotated to align with anatomical axes. Following rotation, data were filtered using a low-pass 4th order Butterworth filter with cutoff frequencies of 300 Hz for linear acceleration and 180 Hz for angular velocity. Filter cutoff values were selected based on the limits of individual sensor bandwidth ranges. Following filtering, a fourth-order differentiation was applied to angular velocities to determine angular accelerations. Linear accelerations were not transformed to head center of gravity (CoG), as we did not have sufficient information from all datasets to apply subject-specific transforms, and the standard approach of transforming based on 50th percentile male human head dimensions can introduce systematic bias into the analysis. As such, all linear head kinematics analyses are based on kinematics at the upper dentition location, while rotational kinematics are invariant across the head assuming rigid body motion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Feature Extraction and Analysis\u003c/h2\u003e \u003cp\u003eTo compare head impact biomechanics across sports, we computed key biomechanical features that are commonly used in head impact biomechanics research and have known injury risk implications. These features were calculated for each recorded head impact across all sports in our dataset.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDirectional Magnitude Features\u003c/strong\u003e \u003cp\u003eAbsolute maxima of peak linear acceleration (PLA) in anterior-posterior (AP), left-right (LR), and inferior-superior (IS) axes, as well as peak angular velocity (PAV), and peak angular acceleration (PAA) in coronal (cor.), sagittal (sag.) and horizontal (hor.) planes were computed for each impact. Resultants (R.) were also computed for PLA, PAV, and PAA.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eImpulse Duration Features\u003c/strong\u003e \u003cp\u003eLinear and angular acceleration impulse durations were determined from the maximum linear and angular acceleration directions for each impact, respectively. Impulse durations were extracted by calculating the width of the impulse at half of the peak acceleration value of the maximum axis. Therefore, each impact consisted of a linear acceleration and an angular acceleration impulse duration.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAcceleration Direction Features\u003c/strong\u003e \u003cp\u003ePeak acceleration directions were computed by determining the peak resultant acceleration value and finding its corresponding per axis values to create a 3D acceleration vector, for both the linear and angular acceleration individually. The peak acceleration direction vectors were then divided into six direction categories (forward/anterior, backward/posterior, leftward, rightward, upward/superior, downward/inferior), separated by the 45-degree oblique directions in each anatomical plane.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSagittal and Coronal Ratios\u003c/strong\u003e \u003cp\u003eDuring a head impact, linear and rotational head kinematics are intrinsically coupled due to the complex head-neck-torso system's response to external impact forces. We studied the coupling between linear and rotational kinematics in sagittal and coronal impacts by calculating the linear-angular acceleration ratio. For sagittal impacts (\u0026gt;\u0026thinsp;80% of peak angular acceleration in sagittal plane), we calculated the sagittal acceleration ratio (rSagittal) by dividing anterior-posterior linear acceleration by sagittal angular acceleration at the moment of peak angular acceleration. Similarly, for coronal impacts (\u0026gt;\u0026thinsp;80% of peak angular acceleration in coronal plane), we calculated the coronal ratio (rCoronal) by dividing left-right linear acceleration by coronal angular acceleration.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFrequency Features\u003c/strong\u003e \u003cp\u003eWe extracted wavelet transform (WT) features to examine signal characteristics in the time-frequency domain [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Morlet WT was performed on the linear and angular accelerations in all directions. For each impact, the peak frequency at the peak WT amplitude in the time-domain, was computed. Further, we estimated the bandwidth of each impact by identifying the range of frequencies where the WT amplitude remained above 50% of its peak value (full-width at half maximum).\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical Methods and Clustering Analysis\u003c/h2\u003e \u003cp\u003eFor objective (i), Kruskal-Wallis test was used to test for significance, followed by a post-hoc pairwise comparison with Bonferroni correction to statistically compare directional magnitude, impulse duration, as well as rSagittal and rCoronal features across sports. Following the Kruskal-Wallis test, we also calculated the epsilon square (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\epsilon\\:\\)\u003c/span\u003e\u003c/span\u003e\u003csup\u003e2\u003c/sup\u003e) to determine effect size, with the classification of 0.01 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\le\\:\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\epsilon\\:\\)\u003c/span\u003e\u003c/span\u003e\u003csup\u003e2\u003c/sup\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\le\\:\\)\u003c/span\u003e\u003c/span\u003e 0.06 as a small effect, 0.06 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\le\\:\\epsilon\\:\\)\u003c/span\u003e\u003c/span\u003e\u003csup\u003e2\u003c/sup\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\le\\:\\)\u003c/span\u003e\u003c/span\u003e 0.14 a medium effect, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\epsilon\\:\\)\u003c/span\u003e\u003c/span\u003e\u003csup\u003e2\u003c/sup\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:\\)\u003c/span\u003e\u003c/span\u003e 0.14 as large effect [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. A Chi-squared test for independence, followed by a post-hoc pairwise comparison with Bonferroni correction, was used to determine if any significant association existed between sports and the distribution of acceleration direction features.\u003c/p\u003e \u003cp\u003eFor objective (ii), we applied a k-means clustering algorithm for two separate feature groups (directional impact magnitude features and frequency features) to examine clusters in directional magnitude distributions and in impact frequency characteristics. Only impact magnitude and frequency features were used for k-means clustering, as we expected time-frequency domain features to capture the impulse duration information, and acceleration direction features did not form clear clusters (low silhouette scores) in preliminary analysis. Additionally, coupling ratios were only computed for sagittal and coronal impacts, and thus not available for including as a feature for all impacts. After our k-means analysis, we then trained a t-SNE model on all combined magnitude and frequency features to project high-dimensional non-linear features into 2D space, while maintaining local relationships, thus optimizing for cluster identification with the high-dimensional feature space.\u003c/p\u003e \u003cp\u003eBefore applying the k-means clustering algorithm, we normalized features using the min-max method prior to applying the MATLAB kmeans function (R2023b, The MathWorks, Inc., Natick, MA). We initialized seeds by implementing the MATLAB \u003cem\u003ekmeans++\u003c/em\u003e [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] algorithm and computed silhouette scores to evaluate the effectiveness of the clustering for each feature group [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. To determine the optimal number of clusters, we generated an elbow plot using average silhouette scores. We identified the features contributing most to centroid movement by analyzing within-cluster sum of squares (WCSS)[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. For the t-SNE algorithm [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], following recommended pre-processing techniques [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], we reduced the data to two dimensions using a principal component analysis (PCA). Based on the dataset size, we selected a perplexity value of 30 (default value for this data size) to define the effective number of local neighbors for the model.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cem\u003eDatabase:\u0026nbsp;\u003c/em\u003eOur database consisted of 1250 total video-verified direct head impacts, after data quality screening and re-thresholding. Of the 1250 impacts, 146 were from men\u0026rsquo;s hockey (20 athletes and 80 athlete-events), 232 from men\u0026rsquo;s football (10 athletes and 22 athlete-events), 306 from women\u0026rsquo;s rugby (19 athletes and 57 athlete-events), and 566 from women\u0026rsquo;s soccer (13 athletes and 146 athlete-events). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMagnitude Features:\u0026nbsp;\u003c/em\u003eSignificant differences in peak kinematics across the four sports were identified using the Kruskal-Wallis test(Fig. 1, Supplementary Table 1 \u0026amp; 2). Effect sizes indicated medium to large sport-based differences for PLA\u003csub\u003eLR\u003c/sub\u003e (\u003cimg width=\"8\" height=\"20\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAwAAAAeCAMAAAAvtQ9FAAAAAXNSR0IArs4c6QAAAFdQTFRFAAAAAAAAAAA6ADo6AGa2OgAAOjoAOjpmOmaQOpC2OpDbZgAAZgA6Zjo6ZrbbZrb/kDoAkGY6kNv/tmYAtmY6tpBmttvb27aQ2////7Zm/9u2//+2///bkqmydwAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAAFiUAABYlAUlSJPAAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAAAX0lEQVQoU2NgGNxARpSNkZGJXQjkShkBRj4GGWFGfhBHkpEFSErzgGUkGbkQ3pARYAILQoCUICMjRAdQPxOfBEwcagyYK82NrEOYkVOCQUacF6xJRoSVkZGZQ4y2oQQAIfcDVKETafcAAAAASUVORK5CYII=\" alt=\"image\"\u003e\u003csup\u003e2\u003c/sup\u003e = 0.40), PAV\u003csub\u003ecor\u003c/sub\u003e (\u003cimg width=\"8\" height=\"20\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAwAAAAeCAMAAAAvtQ9FAAAAAXNSR0IArs4c6QAAAFdQTFRFAAAAAAAAAAA6ADo6AGa2OgAAOjoAOjpmOmaQOpC2OpDbZgAAZgA6Zjo6ZrbbZrb/kDoAkGY6kNv/tmYAtmY6tpBmttvb27aQ2////7Zm/9u2//+2///bkqmydwAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAAFiUAABYlAUlSJPAAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAAAX0lEQVQoU2NgGNxARpSNkZGJXQjkShkBRj4GGWFGfhBHkpEFSErzgGUkGbkQ3pARYAILQoCUICMjRAdQPxOfBEwcagyYK82NrEOYkVOCQUacF6xJRoSVkZGZQ4y2oQQAIfcDVKETafcAAAAASUVORK5CYII=\" alt=\"image\"\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e= 0.33), and PAV\u003csub\u003eR\u0026nbsp;\u003c/sub\u003e(\u003cimg width=\"8\" height=\"20\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAwAAAAeCAMAAAAvtQ9FAAAAAXNSR0IArs4c6QAAAFdQTFRFAAAAAAAAAAA6ADo6AGa2OgAAOjoAOjpmOmaQOpC2OpDbZgAAZgA6Zjo6ZrbbZrb/kDoAkGY6kNv/tmYAtmY6tpBmttvb27aQ2////7Zm/9u2//+2///bkqmydwAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAAFiUAABYlAUlSJPAAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAAAX0lEQVQoU2NgGNxARpSNkZGJXQjkShkBRj4GGWFGfhBHkpEFSErzgGUkGbkQ3pARYAILQoCUICMjRAdQPxOfBEwcagyYK82NrEOYkVOCQUacF6xJRoSVkZGZQ4y2oQQAIfcDVKETafcAAAAASUVORK5CYII=\" alt=\"image\"\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e= 0.31), suggesting these metrics vary substantially by sport. In contrast, PLA\u003csub\u003eAP\u003c/sub\u003e (\u003cimg width=\"8\" height=\"20\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAwAAAAeCAMAAAAvtQ9FAAAAAXNSR0IArs4c6QAAAFdQTFRFAAAAAAAAAAA6ADo6AGa2OgAAOjoAOjpmOmaQOpC2OpDbZgAAZgA6Zjo6ZrbbZrb/kDoAkGY6kNv/tmYAtmY6tpBmttvb27aQ2////7Zm/9u2//+2///bkqmydwAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAAFiUAABYlAUlSJPAAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAAAX0lEQVQoU2NgGNxARpSNkZGJXQjkShkBRj4GGWFGfhBHkpEFSErzgGUkGbkQ3pARYAILQoCUICMjRAdQPxOfBEwcagyYK82NrEOYkVOCQUacF6xJRoSVkZGZQ4y2oQQAIfcDVKETafcAAAAASUVORK5CYII=\" alt=\"image\"\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e= 0.04) and PAA\u003csub\u003eR\u003c/sub\u003e (\u003cimg width=\"8\" height=\"20\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAwAAAAeCAMAAAAvtQ9FAAAAAXNSR0IArs4c6QAAAFdQTFRFAAAAAAAAAAA6ADo6AGa2OgAAOjoAOjpmOmaQOpC2OpDbZgAAZgA6Zjo6ZrbbZrb/kDoAkGY6kNv/tmYAtmY6tpBmttvb27aQ2////7Zm/9u2//+2///bkqmydwAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAAFiUAABYlAUlSJPAAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAAAX0lEQVQoU2NgGNxARpSNkZGJXQjkShkBRj4GGWFGfhBHkpEFSErzgGUkGbkQ3pARYAILQoCUICMjRAdQPxOfBEwcagyYK82NrEOYkVOCQUacF6xJRoSVkZGZQ4y2oQQAIfcDVKETafcAAAAASUVORK5CYII=\" alt=\"image\"\u003e\u003csup\u003e2\u003c/sup\u003e = 0.13) showed weaker effect sizes.\u003c/p\u003e\n\u003cp\u003eFor PLA, men\u0026rsquo;s football presented the highest median PLA\u003csub\u003eAP\u003c/sub\u003e and PLA\u003csub\u003eLR\u003c/sub\u003e (10.8 g and 10.6, respectively) while women\u0026rsquo;s soccer displayed the highest PLA\u003csub\u003eIS\u003c/sub\u003e (11.2 g). Additionally, men\u0026rsquo;s football demonstrated the highest 95\u003csup\u003eth\u003c/sup\u003e percentile PLA\u003csub\u003eAP\u003c/sub\u003e (27.5 g) and PLA\u003csub\u003eIS\u003c/sub\u003e (25.3 g) while men\u0026rsquo;s hockey had the highest 95\u003csup\u003eth\u003c/sup\u003e percentile PLA\u003csub\u003eLR\u003c/sub\u003e (30.7 g). Post-hoc analyses highlighted most pairwise comparisons were significant, except those involving men\u0026rsquo;s hockey vs. women\u0026rsquo;s rugby. Additionally, comparisons between men\u0026rsquo;s hockey vs. women\u0026rsquo;s soccer, and women\u0026rsquo;s rugby vs. women\u0026rsquo;s soccer for PLA\u003csub\u003eAP\u003c/sub\u003e, men\u0026rsquo;s football vs. women\u0026rsquo;s rugby for PLA\u003csub\u003eLR\u003c/sub\u003e, and men\u0026rsquo;s hockey vs. men\u0026rsquo;s football for PLA\u003csub\u003eIS\u003c/sub\u003e were not significantly different. For PAV, men\u0026rsquo;s football displayed the highest median PAV\u003csub\u003ecor\u003c/sub\u003e (6.8 rad/s) and PAV\u003csub\u003esag\u003c/sub\u003e (6.3 rad/s), while men\u0026rsquo;s hockey displayed the highest median PAV\u003csub\u003ehor\u003c/sub\u003e (5.7 rad/s). Men\u0026rsquo;s football demonstrated the highest 95\u003csup\u003eth\u003c/sup\u003e percentile PAV for all axes. Post-hoc pairwise comparisons highlighted men\u0026rsquo;s hockey vs women\u0026rsquo;s rugby, and men\u0026rsquo;s hockey vs men\u0026rsquo;s football to consistently showing no significant differences across all axes. For PAA, men\u0026rsquo;s football displayed the highest median PAA\u003csub\u003ecor\u003c/sub\u003e (1332 rad/s\u003csup\u003e2\u003c/sup\u003e), PAA\u003csub\u003esag\u003c/sub\u003e (966 rad/s\u003csup\u003e2\u003c/sup\u003e), and PAA\u003csub\u003ehor\u003c/sub\u003e (749 rad/s\u003csup\u003e2\u003c/sup\u003e), and highest 95\u003csup\u003eth\u003c/sup\u003e percentile PAA for all axes. Interestingly, men\u0026rsquo;s hockey vs. women\u0026rsquo;s rugby remained the only non-significant comparison across PAA.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcross all resultant values, men\u0026rsquo;s football had the highest median PLA\u003csub\u003eR\u003c/sub\u003e (17.5 g), PAV\u003csub\u003eR\u003c/sub\u003e (11.1 rad/s), and PAA\u003csub\u003eR\u003c/sub\u003e (1789 rad/s\u0026sup2;), while women\u0026rsquo;s soccer had the lowest (13.1 g, 5.5 rad/s, 786 rad/s\u0026sup2;). Post-hoc analysis revealed that most pairwise comparisons were significant, with a few exceptions. For PLA\u003csub\u003eR\u003c/sub\u003e, differences were not significant between men\u0026rsquo;s hockey and women\u0026rsquo;s rugby, men\u0026rsquo;s hockey and women\u0026rsquo;s soccer, or between men\u0026rsquo;s football and women\u0026rsquo;s rugby. For PAV\u003csub\u003eR\u003c/sub\u003e, no significant differences were found between men\u0026rsquo;s hockey and women\u0026rsquo;s rugby, men\u0026rsquo;s hockey and men\u0026rsquo;s football, or men\u0026rsquo;s football and women\u0026rsquo;s rugby. Lastly, for PAA\u003csub\u003eR\u003c/sub\u003e, all pairwise comparisons were significant except between men\u0026rsquo;s hockey and women\u0026rsquo;s rugby.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eImpulse Duration:\u0026nbsp;\u003c/em\u003eComparing linear acceleration impulse durations of the maximum axis, men\u0026rsquo;s hockey and women\u0026rsquo;s rugby exhibited longer median durations at 11 ms and 10 ms, respectively, while men\u0026rsquo;s football and women\u0026rsquo;s soccer were shorter at 9 ms and 8 ms (Fig. 2A). For angular acceleration impulse durations, men\u0026rsquo;s hockey again displayed the longest median duration of 10.5 ms, closely followed by women\u0026rsquo;s rugby at 10 ms. Men\u0026rsquo;s football and women\u0026rsquo;s soccer were lower at 7 ms and 8 ms, respectively (Fig. 2B). Statistical comparisons revealed significant differences in linear impulse durations across all sports using the Kruskal-Wallis test, however post-hoc tests highlighted non-significant findings between men\u0026rsquo;s hockey vs. women\u0026rsquo;s rugby, men\u0026rsquo;s football vs. women\u0026rsquo;s rugby, and men\u0026rsquo;s football vs. women\u0026rsquo;s soccer (Supplementary table 3). Similarly, angular impulse durations were significantly different between all sports, except for men\u0026rsquo;s hockey vs. women\u0026rsquo;s rugby and men\u0026rsquo;s football vs. women\u0026rsquo;s soccer. (Supplementary table 2).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcceleration Direction:\u0026nbsp;\u003c/em\u003eLinear directionality analysis showed men\u0026rsquo;s hockey to display a predominant proportion of leftward (21.9%) and rightward (22.6%) accelerations. Men\u0026rsquo;s football had primarily backwards impact accelerations (34.3%) while women\u0026rsquo;s soccer consisted of primarily upwards accelerations (59.4%). Women\u0026rsquo;s rugby had a more distributed impact classification with most impacts being leftward (32.6%) accelerations (Fig. 3A \u0026amp; Table 2). Chi-squared tests and post-hoc comparisons showed statistically significant differences between all sports for linear acceleration directions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003ePeak linear acceleration directions for all sports\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"588\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.2373%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSport\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5763%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Upward\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2542%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDownward\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5593%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Forward\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4068%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBackward\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5593%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLeftward\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4068%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRightward\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.2373%;\"\u003e\n \u003cp\u003eMen\u0026rsquo;s Hockey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5763%;\"\u003e\n \u003cp\u003e20.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2542%;\"\u003e\n \u003cp\u003e7.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5593%;\"\u003e\n \u003cp\u003e11.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4068%;\"\u003e\n \u003cp\u003e15.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5593%;\"\u003e\n \u003cp\u003e21.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4068%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e22.6%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.2373%;\"\u003e\n \u003cp\u003eMen\u0026rsquo;s Football\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5763%;\"\u003e\n \u003cp\u003e11.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2542%;\"\u003e\n \u003cp\u003e7.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5593%;\"\u003e\n \u003cp\u003e4.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4068%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e34.3%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5593%;\"\u003e\n \u003cp\u003e18.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4068%;\"\u003e\n \u003cp\u003e23.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.2373%;\"\u003e\n \u003cp\u003eWomen\u0026rsquo;s Soccer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5763%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e59.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2542%;\"\u003e\n \u003cp\u003e12.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5593%;\"\u003e\n \u003cp\u003e4.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4068%;\"\u003e\n \u003cp\u003e21.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5593%;\"\u003e\n \u003cp\u003e1.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4068%;\"\u003e\n \u003cp\u003e1.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.2373%;\"\u003e\n \u003cp\u003eWomen\u0026rsquo;s Rugby\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5763%;\"\u003e\n \u003cp\u003e4.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.2542%;\"\u003e\n \u003cp\u003e11.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5593%;\"\u003e\n \u003cp\u003e9.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4068%;\"\u003e\n \u003cp\u003e23.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5593%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e32.6%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4068%;\"\u003e\n \u003cp\u003e18.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003ePeak angular acceleration direction for all sports\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.7372%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSport\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.61538%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eForward Sagittal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7372%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBackward Sagittal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.65385%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSagittal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.0962%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLeftward Coronal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2179%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRightward Coronal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.97436%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoronal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2179%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLeftward Horizontal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2179%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRightward Horizontal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.53205%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHoriz.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.7372%;\"\u003e\n \u003cp\u003eMen\u0026rsquo;s Hockey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.61538%;\"\u003e\n \u003cp\u003e19.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7372%;\"\u003e\n \u003cp\u003e16.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.65385%;\"\u003e\n \u003cp\u003e35.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.0962%;\"\u003e\n \u003cp\u003e15.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2179%;\"\u003e\n \u003cp\u003e18.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.97436%;\"\u003e\n \u003cp\u003e33.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2179%;\"\u003e\n \u003cp\u003e10.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2179%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e20.5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.53205%;\"\u003e\n \u003cp\u003e30.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.7372%;\"\u003e\n \u003cp\u003eMen\u0026rsquo;s Football\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.61538%;\"\u003e\n \u003cp\u003e3.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7372%;\"\u003e\n \u003cp\u003e24.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.65385%;\"\u003e\n \u003cp\u003e27.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.0962%;\"\u003e\n \u003cp\u003e28.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2179%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e36.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.97436%;\"\u003e\n \u003cp\u003e65.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2179%;\"\u003e\n \u003cp\u003e3.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2179%;\"\u003e\n \u003cp\u003e3.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.53205%;\"\u003e\n \u003cp\u003e6.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.7372%;\"\u003e\n \u003cp\u003eWomen\u0026rsquo;s Soccer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.61538%;\"\u003e\n \u003cp\u003e3.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7372%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e79.2%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.65385%;\"\u003e\n \u003cp\u003e82.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.0962%;\"\u003e\n \u003cp\u003e4.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2179%;\"\u003e\n \u003cp\u003e6.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.97436%;\"\u003e\n \u003cp\u003e11.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2179%;\"\u003e\n \u003cp\u003e3.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2179%;\"\u003e\n \u003cp\u003e2.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.53205%;\"\u003e\n \u003cp\u003e6.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.7372%;\"\u003e\n \u003cp\u003eWomen\u0026rsquo;s Rugby\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.61538%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e22.1%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7372%;\"\u003e\n \u003cp\u003e10.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.65385%;\"\u003e\n \u003cp\u003e32.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.0962%;\"\u003e\n \u003cp\u003e21.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2179%;\"\u003e\n \u003cp\u003e20.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.97436%;\"\u003e\n \u003cp\u003e42.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2179%;\"\u003e\n \u003cp\u003e13.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2179%;\"\u003e\n \u003cp\u003e11.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.53205%;\"\u003e\n \u003cp\u003e24.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMen\u0026rsquo;s football displayed the most impacts where the skull rotates in the coronal plane (leftward: 28.4%, rightward: 36.9%). Women\u0026rsquo;s soccer had a large proportion of impacts where the skull is rotating backwards in the sagittal plane (79.2%) (Fig. 3B \u0026amp; Table 3). \u0026nbsp;Both men\u0026rsquo;s hockey and women\u0026rsquo;s rugby had relatively more equal distributions of angular directions. Chi-squared tests followed by post-hoc tests revealed statistically significant differences between all sports for angular acceleration directions.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSagittal and Coronal Ratios:\u0026nbsp;\u003c/em\u003eOf the 1250 head impacts, 570 were sagittal dominant, with 34 from men\u0026rsquo;s hockey, 46 from men\u0026rsquo;s football, 69 from women\u0026rsquo;s rugby, and 412 from women\u0026rsquo;s soccer (Fig. 4A). Women\u0026rsquo;s soccer displayed the highest percentage of sagittal dominant impacts (73%) while men\u0026rsquo;s football had the least (20%). Men\u0026rsquo;s football exhibited the highest median rSagittal length of 15.9cm while men\u0026rsquo;s hockey displayed the lowest median with 9.7cm (Fig 4B). We also found 299 coronal dominant impacts with 43 from men\u0026rsquo;s hockey, 124 from men\u0026rsquo;s football, 104 from women\u0026rsquo;s rugby, and 28 from women\u0026rsquo;s soccer (Fig. 4A). Men\u0026rsquo;s football had the highest percentage of rCoronal impacts (53%) while women\u0026rsquo;s soccer had the least (5%). Men\u0026rsquo;s hockey displayed the highest median rCoronal lengths with 13.8 cm while men\u0026rsquo;s football had the lowest median rCoronal length of 4.6cm (Fig. 4B). Kruskal-Wallis tests highlighted significant differences between all sports for rSagittal and rCoronal ratios. Post-hoc tests highlighted significant pairwise comparisons between all comparisons for rSagittal except between men\u0026rsquo;s football and women\u0026rsquo;s rugby, and men\u0026rsquo;s hockey and women\u0026rsquo;s soccer. Further, rCoronal showed significant differences between all pairwise comparisons except men\u0026rsquo;s football vs. women\u0026rsquo;s soccer and men\u0026rsquo;s hockey vs. women\u0026rsquo;s rugby (Supplementary table 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMagnitude and Frequency Clusters:\u0026nbsp;\u003c/em\u003eFor both the magnitude and frequency feature groups, k-means clustering identified k = 2 as the most optimal (i.e., two clusters), with silhouette scores of 0.45 and 0.37, respectively. Clustering with magnitude features identified two clusters that may be differentiated into low magnitude (Cluster 1) and high magnitude (Cluster 2) clusters based on resultant peak kinematics (Fig. 5A). They may also be differentiated by directional kinematics, where the low magnitude cluster consisted of primarily sagittal plane kinematics, and the high magnitude cluster showed higher coronal plane kinematics. Of those two clusters, women\u0026rsquo;s soccer impacts were found to largely represent the low magnitude cluster (79.9% of all total impacts) (Cluster 1), whereas the high magnitude cluster comprised of impacts mostly from men\u0026rsquo;s football, men\u0026rsquo;s hockey, and women\u0026rsquo;s rugby (Cluster 2). \u0026nbsp;Frequency feature clustering also comprised of low frequency (Cluster 1) and high frequency (Cluster 2) clusters, mainly differentiated by low vs. high peak frequencies but comparable ranges of bandwidths (Fig. 5B). Similar to the magnitude features, the lower frequency cluster (Cluster 1) was moderately dominated by women\u0026rsquo;s soccer (65.8% of all total impacts), while the higher frequency cluster (Cluster 2) had a relatively even distribution of other sports. For magnitude-based features and clustering, WCSS analysis revealed that PAV\u003csub\u003ecor\u003c/sub\u003e had the greatest influence on cluster centroid displacement, while PAA\u003csub\u003esag\u003c/sub\u003e had the least. Similarly, for frequency-based features, angular frequency in the sagittal direction caused the highest centroid shifts, whereas linear frequencies in the IS direction had the lowest effect.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eInteractions of Feature Groups:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eInteractions of magnitude and frequency features highlight some interesting sport specific similarities and differences (Fig. 5C). Both men\u0026rsquo;s hockey and men\u0026rsquo;s football impacts were mostly high magnitude with more even distribution in low and high frequency clusters. Men\u0026rsquo;s football and women\u0026rsquo;s rugby showed slight dominance in impacts with high magnitude and low frequency (48.9% and 54.4% of all impacts, respectively). Lastly, women\u0026rsquo;s soccer demonstrated mostly low magnitude and low frequency impacts (62.2% of all impacts).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003et-SNE Trained on all Feature Groups:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo further explore our impact dataset, we trained our t-SNE model on all of our features to further observe any sport-specific clustering, as t-SNE is a useful tool to determine similarities in high dimensional data. We generally observed tighter clustering of women\u0026rsquo;s soccer impacts, consistent with k-means findings, while other sports impacts showed more overlapping patterns (Fig. 6). Some sport-specific small subclusters were noted and further analyzed to examine if they belonged to specific players or playing positions, yet no conclusive patterns were found.\u0026nbsp;\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eWe uniformly processed raw iMG head impact data across four sports\u0026mdash;men\u0026rsquo;s football, men\u0026rsquo;s hockey, women\u0026rsquo;s rugby, and women\u0026rsquo;s soccer\u0026mdash;and compared their biomechanical features. For objective (i), we identified statistically significant differences across sports in the distributions of peak kinematics, impulse durations, coupling ratios, and resultant acceleration directionality. However, for objective (ii), our clustering results highlighted cross-sport similarities, suggesting that head impacts may exhibit shared biomechanical characteristics across sports.\u003c/p\u003e \u003cp\u003eMen\u0026rsquo;s football exhibited the highest median peak resultant PLA, PAV, and PAA, while women\u0026rsquo;s soccer had the lowest across all resultant kinematic metrics. Comparing our kinematic magnitudes to previous literature remains challenging due to the wide variability in reported kinematics for similar impact scenarios. A non-comprehensive review of prior studies showed a wide range of reported peak kinematics in the four sports we examined, with mean PLA and PAA ranges of 21 g \u0026ndash; 32 g and 1017 rad/s\u0026sup2; \u0026ndash; 2120 rad/s\u0026sup2; for men\u0026rsquo;s football [\u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], 19\u0026ndash;40 g and 1750\u0026ndash;3500 rad/s\u0026sup2; for men\u0026rsquo;s hockey [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], 11 g \u0026ndash; 31 g and 800 rad/s\u0026sup2; \u0026minus;\u0026thinsp;4291 rad/s\u0026sup2; for women\u0026rsquo;s rugby [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], and 5 g \u0026ndash; 39 g and 1750 rad/s\u0026sup2; \u0026ndash; 7713 rad/s\u0026sup2; for women\u0026rsquo;s soccer [\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Notably, our study reports values on the lower end of these ranges, perhaps reflecting improvements in data quality and standardization using instrumented mouthguard sensors. A cross-sport comparison study by Naunheim et al. [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] in 2000, used a helmet-mounted sensor, and found men\u0026rsquo;s football to have the lowest mean PLA (29.2 g), followed by hockey (35 g) and soccer the highest (54.7 g), which not only show substantially higher kinematics for all sports compared with our study, but also opposite trends in cross-sport impact severity. More recently, using iMG sensors, Gabler et al. [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] reported median kinematics for collegiate men\u0026rsquo;s football at 17 g and 1180 rad/s\u0026sup2;, and Miller et al., [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] reported soccer heading with 9.4 g and 689 rad/s\u003csup\u003e2\u003c/sup\u003e which align more closely with our current findings. Therefore, we believe as sensor accuracy continues to improve and processing methods become more standardized, cross-sport comparisons will become more consistent and reliable.\u003c/p\u003e \u003cp\u003eNotably, men\u0026rsquo;s football exhibited substantially higher coronal rotational kinematics compared with the other sports, with a relatively large number of high rotation impacts (14% impacts over 5,000 rad/s\u003csup\u003e2\u003c/sup\u003e of coronal angular acceleration). Interestingly, while women\u0026rsquo;s soccer impacts had generally lower peak resultant values, the median and 75th percentile inferior-superior peak linear accelerations in soccer impacts were the highest among sports. In addition, women\u0026rsquo;s soccer median and 75th percentile sagittal angular acceleration were second highest among sports. Since the soccer heading impacts were primarily in the sagittal plane, our directional magnitude analyses revealed that women\u0026rsquo;s soccer impacts can exhibit higher kinematics relative to other sports in certain directional measures, even though traditional resultant values would indicate lowest severity impacts. As it is known there can be direction-dependence of injury risk, and some injury risk criteria such as the Brain Injury Criterion (BrIC) have established directional thresholds, our findings show that directional kinematics analyses are important when investigating sport head injury risks.\u003c/p\u003e \u003cp\u003eOur acceleration direction analyses also revealed some impact directionality differences across sports that may have injury risk implications. From Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, we see that only women\u0026rsquo;s soccer showed predominantly sagittal impacts during heading (82.6% peak angular accelerations in sagittal plane). Further, one interesting observation was the dominance of upward acceleration observed in women\u0026rsquo;s soccer impacts (59.4%). Upon reviewing the data, we found that women\u0026rsquo;s soccer impacts typically exhibited two impulsive peaks in succession, one from the initial ball-head contact (typically to the frontal hairline region), and the second from an opposite acceleration impulse likely due to the reaction force from the head-neck stopping at the end range of motion. The upwards accelerations corresponded to the second peak, which often surpassed the magnitude of the first peak. This observation shows that traditional peak analysis may not fully capture the complexities of impact biomechanics in sports, since one may have originally considered the ball-to-head impact to be the more important to analyze for injury considerations, while we are finding that the \u0026lsquo;rebound\u0026rsquo; impact can be higher magnitude.\u003c/p\u003e \u003cp\u003eMen\u0026rsquo;s football impacts also showed some dominance in coronal peak angular accelerations (65.3%), and combined with the earlier observation of higher peak kinematics distributions compared with other sports, may prompt further investigation of coronal impacts and injury risk considerations for football. Early primate studies have shown that the coronal plane may have lower tolerance to diffuse axonal injury [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. From our analysis of sagittal and coronal ratios, we found that men\u0026rsquo;s football had substantially lower ratios in coronal impacts, which is consistent with the observation that for a similar amount of linear accelerations, higher angular accelerations were seen in football impacts. Past work investigating the instantaneous head centre-of-rotation has noted the importance of understanding head/neck/torso and spinal constraints in investigating head rotational kinematics [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Interestingly, this prior laboratory-based study showed higher ratios in the coronal plane than the sagittal plane [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], which is consistent with what we saw in men\u0026rsquo;s hockey, but opposite to our findings in football impacts, demonstrating potential on-field differences in head-neck-torso engagement during impact. Overall, men\u0026rsquo;s hockey and women\u0026rsquo;s rugby showed more balanced impact directionalities, and substantially more horizontal impacts compared with men\u0026rsquo;s football and women\u0026rsquo;s soccer. Past literature have also indicated potential horizontal plane vulnerability [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] that may be important to consider for these sports.\u003c/p\u003e \u003cp\u003eWhile we included both men\u0026rsquo;s and women\u0026rsquo;s data, this unique iMG-only dataset did not include matched sports to enable a sex-based analysis. Despite this, we can make preliminary observations that women\u0026rsquo;s head impacts had comparable ranges of peak kinematics as men\u0026rsquo;s head impacts in our study, especially in women\u0026rsquo;s rugby. This would challenge the theory of weaker neck muscles in women leading to more severe impacts and subsequently higher injury risk [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Since our study quantified sagittal and coronal linear-angular ratios, we expected these ratios to approximate the distance between the sensor and the instantaneous center of rotation. If neck length played a significant role, we anticipated higher ratios in men\u0026rsquo;s impacts compared to women\u0026rsquo;s. However, we also did not find consistent sex-based differences. Various factors, including anthropometrics, neck muscle strength, and playing style, may all contribute to differences in impact biomechanics and subsequent injury risk. Further studies with matched sports between men\u0026rsquo;s and women\u0026rsquo;s teams can further investigate these factors.\u003c/p\u003e \u003cp\u003eOur comparison of impulse durations along with clustering of frequency-domain features give insight to the temporal dynamics and frequency characteristics of impact biomechanics across sports. While most studies focus on reporting peak head kinematics metrics, injury biomechanics literature have highlighted the importance of examining not only peak acceleration, but also the duration of the acceleration impulse when defining concussion risk curves [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Theoretical structural mechanics analyses and earlier cadaver/animal research [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] have derived injury risk curves that predict higher tolerance to peak accelerations at short durations and lower tolerance to peak accelerations at long durations. Our analyses have shown a wide range of impulse durations from 3 ms to 50 ms or longer, with largely overlapping ranges across sports. Interestingly, despite the expectation that helmet padding should lengthen impulse durations and mitigate high frequency content [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] we did not observe substantial differences in impulse duration between helmeted (linear median: 9 ms, angular median: 8 ms) and unhelmeted (linear median: 9 ms, angular median: 9 ms) impacts. Additionally, we found the helmeted impacts from men\u0026rsquo;s football and men\u0026rsquo;s hockey to have a greater proportion of impacts in the high frequency cluster. It is worth noting the measured frequency-dependence of brain material behaviour [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], and the potential existence of low-frequency resonance behaviour of brain tissue [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] that may prompt future studies to also consider these biomechanical parameters in conjunction with impact magnitude when determining injury risk implications.\u003c/p\u003e \u003cp\u003eOverall, combining the findings from the two objectives, we observe that despite some notable distribution differences in biomechanical parameters, direct head impacts in the sports we examined have largely overlapping biomechanical characteristics. Women\u0026rsquo;s soccer impacts, consisting solely of heading events, exhibited the tightest biomechanical parameter distributions. Since soccer heading impacts involve largely similar directionalities and the same impacting object, they are highly similar to each other in these biomechanical features, which we observed in the majority of soccer impacts falling into the same magnitude and frequency clusters. With more varying directionality and impact scenarios in other sports, we find less clear sport distinction in the clustering analysis, despite large differences in protective equipment use and gameplay dynamics. Even within soccer heading, a small proportion of impacts have similar characteristics as those found in other sports with some of these impacts found in the high magnitude and high frequency clusters. With the T-SNE analysis that optimized for cluster formation by preserving local structures in the high dimensional feature space, there did not seem to be clear sport-specific clustering of features. Overall, we show that sport head impacts span a continuous spectrum of biomechanical parameters, and combining multisport impact data along with epidemiological considerations may give a bigger picture view of the relationship between biomechanical parameters and brain injury risks.\u003c/p\u003e \u003cp\u003eThis study has some limitations that should be considered. Our analysis focused solely on direct head impacts, excluding indirect impacts that frequently occur in sports. These indirect impacts may also contribute to brain injury risk and could alter the biomechanical comparisons across sports. Furthermore, the soccer dataset had only headers and no other recorded impact scenario (e.g., elbow-to-head, head-to-head, etc.,), which may limit the generalizability of all impacts experienced in soccer. Additionally, while we aimed to standardize sensor settings and data processing techniques, we may not be able to fully address inherent sensor hardware differences in post-processing. Also, the standardization process of down sampling and truncating impacts from some sensors may have resulted in loss of frequency- and time-domain information that we are unable to compare (e.g., normalized filter cutoffs to lowest available sensor bandwidth, reducing 100\u0026ndash;150 ms impacts to only 50 ms). Moreover, our clustering model was trained on a select set of biomechanical features rather than an exhaustive list of all possible parameters, to focus on key parameters with injury risk implications, reduce redundancy, and improve the interpretability of findings. Including additional features may alter the observed clustering patterns. Additionally, a sport-matched comparison of sex differences was not feasible with our dataset, despite some preliminary observations of similarities in the men\u0026rsquo;s and women\u0026rsquo;s head impacts. A more controlled prospective study design could provide further insights into sex-based similarities or differences in head impact biomechanics.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study examined head impact biomechanics across multiple sports through a comprehensive analysis of various biomechanical parameters that have been implicated in brain injury risk. We uniquely curated a multisport dataset collected using iMG-type sensors. After controlling for sensor setting differences and performing uniform processing, we note interesting differences and similarities in biomechanical characteristics across men\u0026rsquo;s hockey, men\u0026rsquo;s football, women\u0026rsquo;s rugby, and women\u0026rsquo;s soccer head impacts. While men\u0026rsquo;s football showed highest peak resultant kinematics and women\u0026rsquo;s soccer showed the lowest, consistent with past observations of relative impact magnitudes, directional kinematics analyses revealed more nuanced patterns. For example, women\u0026rsquo;s soccer impacts exhibited relatively high sagittal peak linear and rotational kinematics. From our clustering analyses, we show that aside from women\u0026rsquo;s soccer demonstrating biomechanically similar impacts from heading, there may be shared biomechanical signatures across sports head impacts. These findings suggest that standardized multi-sport analyses may offer greater insights into brain injury mechanisms than isolated sport-specific studies. Future research should leverage similar standardized methodologies across expanded datasets to further elucidate the relationships between the full spectrum of impact characteristics and brain injury risk, ultimately informing more effective protective strategies across sports.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Canadian Institute of Health Research, Michael Smith Health Research British Columbia, Canada Research Chairs program, British Columbia Knowledge Development Fund, and Canada Foundation for Innovation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are using a curated dataset combining published data. Each dataset had research ethics board approval as detailed in the original publications.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCode is available upon request to the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZaryan Masood:\u003c/strong\u003e Writing \u0026ndash; review \u0026amp; editing, Writing \u0026ndash; original draft,\u003c/p\u003e\n\u003cp\u003eVisualization, Methodology, Investigation, Formal analysis, Conceptualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDavid Luke:\u003c/strong\u003e Writing \u0026ndash; review \u0026amp; editing, Data curation, Formal analysis. \u003cstrong\u003eRebecca Kenny:\u003c/strong\u003e Writing \u0026ndash; review \u0026amp; editing, Data curation. \u003cstrong\u003eDaniel Bondi:\u003c/strong\u003e Writing \u0026ndash; review \u0026amp; editing, Data Curation \u003cstrong\u003eAdam Clansey:\u003c/strong\u003e Writing \u0026ndash; review \u0026amp; editing, Data curation. \u003cstrong\u003eLyndia C. Wu:\u003c/strong\u003e Conceptualization, Funding acquisition, Data curation, Writing \u0026ndash; review \u0026amp; editing, Visualization, Investigation, Validation, Formal Analysis, Methodology, Supervision, Resources, Project administration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge that we conducted this research work at the UBC Vancouver (Point Grey) Campus, which is located on the traditional, ancestral, unceded territory of the xʷmə\u0026theta;kʷəy̓əm (Musqueam) people. We thank the Canadian Institute of Health Research (CIHR), Michael Smith Health Research British Columbia (MSHRBC), Canada Research Chairs Program, British Columbia Knowledge Development Fund (BCKDF), and Canada Foundation for Innovation (CFI) for providing funding support for this research. Additionally, this study would not be possible without the enthusiastic and continued support of the University of British Columbia\u0026rsquo;s (UBC) men\u0026rsquo;s hockey, women\u0026rsquo;s rugby, and Stanford men\u0026rsquo;s football team athletes, coaches, and staff. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eYue, J. K., Upadhyayula, P. S., Avalos, L. N., Phelps, R. R. L., Suen, C. G. \u0026amp; Cage, T. A. Concussion and mild-traumatic brain injury in rural settings: Epidemiology and specific health care considerations. J. Neurosci. Rural Pract. 11, 23 (2020). https://doi.org/10.1055/s-0039-3402581\u003c/li\u003e\n\u003cli\u003eHalstead, M. E., Walter, K. D. \u0026amp; Moffatt, K. Sport-related concussion in children and adolescents. Pediatrics 142, e20183074 (2018). https://doi.org/10.1542/peds.2018-3074\u003c/li\u003e\n\u003cli\u003eRawlings, S., Takechi, R. \u0026amp; Lavender, A. P. Effects of sub-concussion on neuropsychological performance and its potential mechanisms: A narrative review. Brain Res. Bull. 165, 56\u0026ndash;62 (2020). https://doi.org/10.1016/j.brainresbull.2020.09.021\u003c/li\u003e\n\u003cli\u003eCamarillo, D. B., Shull, P. B., Mattson, J., Shultz, R. \u0026amp; Garza, D. An instrumented mouthguard for measuring linear and angular head impact kinematics in American football. Ann. Biomed. Eng. 41, 1939\u0026ndash;1949 (2013). https://doi.org/10.1007/s10439-013-0801-y\u003c/li\u003e\n\u003cli\u003eJi, S., Zhao, W., Li, Z. \u0026amp; McAllister, T. W. Head impact accelerations for brain strain-related responses in contact sports: A model-based investigation. Biomech. Model. Mechanobiol. 13, 1121\u0026ndash;1136 (2014). https://doi.org/10.1007/s10237-014-0562-z\u003c/li\u003e\n\u003cli\u003eNewman, J. A. Biomechanics of human trauma: Head protection. In Accidental Injury (eds Nahum, A. M. \u0026amp; Melvin, J. W.) 292\u0026ndash;310 (Springer, 1993). https://doi.org/10.1007/978-1-4757-2264-2_13\u003c/li\u003e\n\u003cli\u003ePellman, E. J., Viano, D. C., Withnall, C., Shewchenko, N., Bir, C. A. \u0026amp; Halstead, P. D. Concussion in professional football: Helmet testing to assess impact performance \u0026ndash; Part 11. Neurosurgery 58, 78\u0026ndash;95 (2006). https://doi.org/10.1227/01.NEU.0000196265.35238.7C\u003c/li\u003e\n\u003cli\u003ePellman, E. J. et al. Concussion in professional football: Location and direction of helmet impacts \u0026ndash; Part 2. Neurosurgery 53, 1328\u0026ndash;1341 (2003). https://doi.org/10.1227/01.neu.0000093499.20604.21\u003c/li\u003e\n\u003cli\u003e.Poutiainen, P., Peltonen, J., Isolehto, J. \u0026amp; Avela, J. Comparison of impact characteristics of four different ice hockey arena dasher boards. Eur. J. Sport Sci. 14, 11\u0026ndash;18 (2014). https://doi.org/10.1080/17461391.2012.725106\u003c/li\u003e\n\u003cli\u003eWilcox, B. J., Machan, J. T., Beckwith, J. G., Greenwald, R. M., Burmeister, E. \u0026amp; Crisco, J. J. Head-impact mechanisms in men\u0026rsquo;s and women\u0026rsquo;s collegiate ice hockey. J. Athl. Train. 49, 514\u0026ndash;520 (2014). https://doi.org/10.4085/1062-6050-49.3.19\u003c/li\u003e\n\u003cli\u003eJones, C. M. \u0026amp; Brown, M. Rowan. Validation of an instrumented mouthguard. medRxiv 22271563; https://doi.org/10.1101/2022.03.02.22271563 (2022).\u003c/li\u003e\n\u003cli\u003eJones, B. et al. Ready for impact? A validity and feasibility study of instrumented mouthguards (iMGs). Br. J. Sports Med. 56, 1171\u0026ndash;1179 (2022). https://doi.org/10.1136/bjsports-2022-105523\u003c/li\u003e\n\u003cli\u003eGellner, R., Begonia, M. \u0026amp; Rowson, S. Choosing optimal cutoff frequencies for filtering linear acceleration and angular velocity signals associated with head impacts measured by instrumented mouthguards. Ann. Biomed. Eng. 52, 1415\u0026ndash;1424 (2024). https://doi.org/10.1007/s10439-024-03466-4\u003c/li\u003e\n\u003cli\u003eHoshizaki, T. B. et al. The development of a threshold curve for the understanding of concussion in sport. Trauma 19, 196\u0026ndash;206 (2017). https://doi.org/10.1177/1460408616676503\u003c/li\u003e\n\u003cli\u003eTiernan, S. \u0026amp; Byrne, G. The effect of impact location on brain strain. Brain Inj. 33, 427\u0026ndash;434 (2019). https://doi.org/10.1080/02699052.2019.1566834\u003c/li\u003e\n\u003cli\u003eLaksari, K., Kurt, M., Babaee, H., Kleiven, S. \u0026amp; Camarillo, D. Mechanistic insights into human brain impact dynamics through modal analysis. Phys. Rev. Lett. 120, 138101 (2018). https://doi.org/10.1103/PhysRevLett.120.138101\u003c/li\u003e\n\u003cli\u003eHernandez, F. et al. Six degree-of-freedom measurements of human mild traumatic brain injury. Ann. Biomed. Eng. 43, 1918\u0026ndash;1934 (2015). https://doi.org/10.1007/s10439-014-1212-4\u003c/li\u003e\n\u003cli\u003eWu, L. C. et al. Detection of American football head impacts using biomechanical features and support vector machine classification. Sci. Rep. 8, 17864 (2017). https://doi.org/10.1038/s41598-017-17864-3\u003c/li\u003e\n\u003cli\u003eKenny, R., Elez, M., Clansey, A., Virji-Babul, N. \u0026amp; Wu, L. C. Head impact exposure and biomechanics in university varsity women\u0026rsquo;s soccer. Ann. Biomed. Eng. 50, 1461\u0026ndash;1472 (2022). https://doi.org/10.1007/s10439-022-02914-3\u003c/li\u003e\n\u003cli\u003eClansey, A. C. et al. On-field head acceleration exposure measurements using instrumented mouthguards: Multi-stage screening to optimize data quality. Ann. Biomed. Eng. 52, 2666\u0026ndash;2677 (2024). https://doi.org/10.1007/s10439-024-03592-z\u003c/li\u003e\n\u003cli\u003eLuke, D., Kenny, R., Bondi, D., Clansey, A. C. \u0026amp; Wu, L. C. On-field instrumented mouthguard coupling. J. Biomech. 162, 111889 (2024). https://doi.org/10.1016/j.jbiomech.2023.111889\u003c/li\u003e\n\u003cli\u003eBartsch, A. J. et al. Laboratory and on-field data collected by a head impact monitoring mouthguard. Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. (EMBS) 2068\u0026ndash;2072 (2019). https://doi.org/10.1109/EMBC.2019.8856907\u003c/li\u003e\n\u003cli\u003eRich, A. M. et al. Development, validation and pilot field deployment of a custom mouthpiece for head impact measurement. Ann. Biomed. Eng. 47, 2109\u0026ndash;2121 (2019). https://doi.org/10.1007/s10439-019-02313-1\u003c/li\u003e\n\u003cli\u003eWu, L. C., Zarnescu, L., Nangia, V., Cam, B. \u0026amp; Camarillo, D. B. A head impact detection system using SVM classification and proximity sensing in an instrumented mouthguard. IEEE Trans. Biomed. Eng. 61, 2659\u0026ndash;2668 (2014). https://doi.org/10.1109/TBME.2014.2320153\u003c/li\u003e\n\u003cli\u003eRea, L. M. \u0026amp; Parker, R. A. Designing and conducting survey research: A comprehensive guide. John Wiley \u0026amp; Sons. (2014).\u003c/li\u003e\n\u003cli\u003eArthur, D. \u0026amp; Vassilvitskii, S. k-means++: The advantages of careful seeding. In Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms 1027\u0026ndash;1035 (Society for Industrial and Applied Mathematics, 2007).\u003c/li\u003e\n\u003cli\u003eShahapure, K. R. \u0026amp; Nicholas, C. Cluster quality analysis using silhouette score. 747\u0026ndash;748 (2020). https://doi.org/10.1109/DSAA49011.2020.00096\u003c/li\u003e\n\u003cli\u003eBrusco, M. J. \u0026amp; Steinley, D. A comparison of heuristic procedures for minimum within-cluster sums of squares partitioning. Psychometrika 72, 583\u0026ndash;600 (2007). https://doi.org/10.1007/s11336-007-9013-4\u003c/li\u003e\n\u003cli\u003eAlghofaili, Y. Interpretable k-means: Clusters feature importances. Medium. https://medium.com/tdsarchive\u003c/li\u003e\n\u003cli\u003eVan der Maaten, L. \u0026amp; Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579\u0026ndash;2605 (2008).\u003c/li\u003e\n\u003cli\u003eMartini, D., Eckner, J., Kutcher, J. \u0026amp; Broglio, S. Subconcussive head impact biomechanics: Comparing differing offensive schemes. Med. Sci. Sports Exerc. 45, 755 (2013). https://doi.org/10.1249/MSS.0b013e3182798758\u003c/li\u003e\n\u003cli\u003eBroglio, S. P., Surma, T. \u0026amp; Ashton-Miller, J. A. High school and collegiate football athlete concussions: A biomechanical review. Ann. Biomed. Eng. 40, 37\u0026ndash;46 (2012). https://doi.org/10.1007/s10439-011-0396-0\u003c/li\u003e\n\u003cli\u003eRowson, S., Brolinson, G., Goforth, M., Dietter, D. \u0026amp; Duma, S. Linear and angular head acceleration measurements in collegiate football. ASME J. Biomech. Eng. 061016 (2009). https://doi.org/10.1115/1.3130454\u003c/li\u003e\n\u003cli\u003eMihalik, J. P., Bell, D. R., Marshall, S. W. \u0026amp; Guskiewicz, K. M. Measurement of head impacts in collegiate football players: An investigation of positional and event-type differences. Neurosurgery 61, 1229\u0026ndash;1235 (2007). https://doi.org/10.1227/01.neu.0000306101.83882.c8\u003c/li\u003e\n\u003cli\u003eSchmidt, J. D. et al. Safe-play knowledge, aggression, and head-impact biomechanics in adolescent ice hockey players. J. Athl. Train. 51, 366\u0026ndash;372 (2016). https://doi.org/10.4085/1062-6050-51.5.04\u003c/li\u003e\n\u003cli\u003eWilliams, E. M. P. et al. Sex differences in neck strength and head impact kinematics in university rugby union players. Eur. J. Sport Sci. 22, 1649\u0026ndash;1658 (2022). https://doi.org/10.1080/17461391.2021.1973573\u003c/li\u003e\n\u003cli\u003eLangevin, T. L. et al. Head impact exposures in women\u0026rsquo;s collegiate rugby. Phys. Sportsmed. 49, 68\u0026ndash;73 (2021). https://doi.org/10.1080/00913847.2020.1770568\u003c/li\u003e\n\u003cli\u003ePetrie, F. Analysis of head acceleration kinematics in collegiate and elite women\u0026rsquo;s rugby union. Thesis or report (2021). (❗ If published, update source.)\u003c/li\u003e\n\u003cli\u003eMcCuen, E. et al. Collegiate women\u0026rsquo;s soccer players suffer greater cumulative head impacts than their high school counterparts. J. Biomech. 48, 3720\u0026ndash;3723 (2015). https://doi.org/10.1016/j.jbiomech.2015.08.003\u003c/li\u003e\n\u003cli\u003eLamond, L. C. et al. Linear acceleration in direct head contact across impact type, player position, and playing scenario in collegiate women\u0026rsquo;s soccer players. J. Athl. Train. 53, 115\u0026ndash;121 (2018). https://doi.org/10.4085/1062-6050-90-17\u003c/li\u003e\n\u003cli\u003eFilben, T. M. et al. Characterization of head impact exposure in women\u0026rsquo;s collegiate soccer. J. Appl. Biomech. 38, 2\u0026ndash;11 (2021). https://doi.org/10.1123/jab.2020-0304\u003c/li\u003e\n\u003cli\u003eNaunheim, R. S., Standeven, J., Richter, C. \u0026amp; Lewis, L. M. Comparison of impact data in hockey, football, and soccer. J. Trauma 48, 938\u0026ndash;941 (2000).\u003c/li\u003e\n\u003cli\u003eGabler, L. F. et al. Distribution of position-specific head impact severities among professional and Division I collegiate American football athletes during games. BMJ Open Sport Exerc. Med. 11, e002365 (2025). https://doi.org/10.1136/bmjsem-2024-002365\u003c/li\u003e\n\u003cli\u003eMiller, L. E. et al. Characterizing head impact exposure in youth female soccer with a custom-instrumented mouthpiece. Res. Sports Med. 28, 55\u0026ndash;71 (2020). https://doi.org/10.1080/15438627.2019.1590833\u003c/li\u003e\n\u003cli\u003eGennarelli, T. A. et al. Diffuse axonal injury and traumatic coma in the primate. Ann. Neurol. 12, 564\u0026ndash;574 (1982). https://doi.org/10.1002/ana.410120611\u003c/li\u003e\n\u003cli\u003eKuo, C., Fanton, M., Wu, L. \u0026amp; Camarillo, D. Spinal constraint modulates head instantaneous center of rotation and dictates head angular motion. J. Biomech. 76, 220\u0026ndash;228 (2018). https://doi.org/10.1016/j.jbiomech.2018.05.024\u003c/li\u003e\n\u003cli\u003eHernandez, F. \u0026amp; Camarillo, D. B. Voluntary head rotational velocity and implications for brain injury risk metrics. J. Neurotrauma 36, 1125\u0026ndash;1135 (2019). https://doi.org/10.1089/neu.2016.475\u003c/li\u003e\n\u003cli\u003eVasavada, A. N., Danaraj, J. \u0026amp; Siegmund, G. P. Head and neck anthropometry, vertebral geometry and neck strength in height-matched men and women. J. Biomech. 41, 114\u0026ndash;121 (2008). https://doi.org/10.1016/j.jbiomech.2007.07.007\u003c/li\u003e\n\u003cli\u003eHrysomallis, C. Neck muscular strength, training, performance and sport injury risk: A review. Sports Med. 46, 1111\u0026ndash;1124 (2016). https://doi.org/10.1007/s40279-016-0490-4\u003c/li\u003e\n\u003cli\u003eGoldsmith, W. \u0026amp; Monson, K. L. The state of head injury biomechanics: Past, present, and future. Part 2: Physical experimentation. Crit. Rev. Biomed. Eng. 33, 105\u0026ndash;207 (2005). https://doi.org/10.1615/CritRevBiomedEng.v33.i2.20\u003c/li\u003e\n\u003cli\u003eHirsch, A. E. \u0026amp; Ommaya, A. K. Protection from brain injury: The relative significance of translational and rotational motions of the head after impact. SAE Tech. Pap. 700899 (1970). https://doi.org/10.4271/700899\u003c/li\u003e\n\u003cli\u003eKuhn, E. N. et al. Youth helmet design in sports with repetitive low- and medium-energy impacts: A systematic review. Sports Eng. 20, 29\u0026ndash;40 (2017). https://doi.org/10.1007/s12283-016-0215-9\u003c/li\u003e\n\u003cli\u003eLv, H. et al. MR elastography frequency\u0026ndash;dependent and independent parameters demonstrate accelerated decrease of brain stiffness in elder subjects. Eur. Radiol. 30, 6614\u0026ndash;6623 (2020). https://doi.org/10.1007/s00330-020-07054-7\u003c/li\u003e\n\u003cli\u003eLaksari, K., Wu, L. C., Kurt, M., Kuo, C. \u0026amp; Camarillo, D. C. Resonance of human brain under head acceleration. J. R. Soc. Interface 12, 20150331 (2015). https://doi.org/10.1098/rsif.2015.0331\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Injury Biomechanics, Instrumented Mouthguard, Head Impact Biomechanics, Signal Processing, Unsupervised Machine Learning","lastPublishedDoi":"10.21203/rs.3.rs-6566803/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6566803/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSports head impacts have been associated both acute and long-term brain trauma. While wearable sensors provide valuable biomechanics insight, most studies focus on single sports, and the variability in sensor methodologies limits cross-sport comparisons. Our objectives were to conduct a multisport comparison and clustering of head impact biomechanics features implicated in brain injury risk. We uniformly processed a multisport dataset gathered using instrumented mouthguards containing direct head impacts in men's football, men's hockey, women's rugby, and women's soccer. We compared directional and resultant peak kinematics, impulse durations, and impact directionality metrics. Then, we applied unsupervised k-means and t-distributed stochastic neighbour embedding (t-SNE) models to examine clustering in impact magnitude and frequency features. Statistically significant cross-sport differences were found in all biomechanical features. Men\u0026rsquo;s football exhibited the highest resultant peak kinematics, while women\u0026rsquo;s soccer showed lowest resultant kinematics. However, directional comparisons revealed unexpected trends such as women\u0026rsquo;s soccer impacts exhibiting high sagittal kinematics relative to other sports. Clustering analyses grouped impacts into low and high magnitude/frequency clusters that transcended sport boundaries, with only women's soccer impacts demonstrating tight clustering patterns due to consistent heading biomechanics. We uniquely curated a standardized dataset for multisport head impact biomechanics comparisons. Cross-sport differences in under-investigated biomechanical features such as directional peak kinematics may need to be further examined for potential sport-specific injury risk considerations. Despite substantial gameplay differences, we found interesting shared biomechanical patterns across sports, warranting joint analyses to inform implications in protective equipment design and injury prevention strategies.\u003c/p\u003e","manuscriptTitle":"Head Impact Biomechanics Across Men's and Women's Contact Sports: A Comparative and Clustering Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-16 13:39:55","doi":"10.21203/rs.3.rs-6566803/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-16T14:05:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-16T15:24:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"225841514600723112738273941873516310456","date":"2025-06-08T11:13:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5777030689767064692487049283744018280","date":"2025-06-06T13:11:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-14T12:53:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-14T12:47:07+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-14T12:31:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-05T20:17:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-05-05T16:38:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e4b9ed48-bd28-4854-b252-50a04d8a2c8c","owner":[],"postedDate":"May 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":48537009,"name":"Physical sciences/Energy science and technology"},{"id":48537010,"name":"Physical sciences/Engineering"}],"tags":[],"updatedAt":"2025-12-22T16:09:53+00:00","versionOfRecord":{"articleIdentity":"rs-6566803","link":"https://doi.org/10.1038/s41598-025-31145-4","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-19 15:57:25","publishedOnDateReadable":"December 19th, 2025"},"versionCreatedAt":"2025-05-16 13:39:55","video":"","vorDoi":"10.1038/s41598-025-31145-4","vorDoiUrl":"https://doi.org/10.1038/s41598-025-31145-4","workflowStages":[]},"version":"v1","identity":"rs-6566803","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6566803","identity":"rs-6566803","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}