An inertial sensor-based comprehensive analysis of manual wheelchair user mobility during daily life in people with SCI

An inertial sensor-based comprehensive analysis of manual wheelchair user mobility during daily life in people with SCI | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results An inertial sensor-based comprehensive analysis of manual wheelchair user mobility during daily life in people with SCI View ORCID Profile Kathylee Pinnock Branford , Meegan G. Van Straaten , View ORCID Profile Omid Jahanian , Melissa M. B. Morrow , View ORCID Profile Stephen M. Cain doi: https://doi.org/10.1101/2025.04.02.646948 Kathylee Pinnock Branford 1 Department of Chemical and Biomedical Engineering, West Virginia University , Morgantown, West Virginia United States of America * Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kathylee Pinnock Branford Meegan G. Van Straaten 2 Division of Health Care Delivery Research, Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic , Rochester, Minnesota, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site Omid Jahanian 3 Rehabilitation Medicine Research Center, Department of Physical Medicine & Rehabilitation, Mayo Clinic , Rochester, Minnesota, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Omid Jahanian Melissa M. B. Morrow 4 Department of Physical Therapy and Rehabilitation Sciences, Center for Health Promotion, Performance, and Rehabilitation Research, The University of Texas Medical Branch , Galveston, Texas, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site Stephen M. Cain 1 Department of Chemical and Biomedical Engineering, West Virginia University , Morgantown, West Virginia United States of America * Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Stephen M. Cain For correspondence: stephen.cain{at}mail.wvu.edu Abstract Full Text Info/History Metrics Preview PDF Abstract This study employed three inertial measurement units to quantify the mobility characteristics of 12 manual wheelchair users with spinal cord injuries (SCI) over 7 consecutive days, revealing nuanced patterns of daily movement. Mobility metrics were calculated for measures of distance traveled, movement duration, and speed. A mobility profile was created to understand patterns of movement behaviors. Participants moved 65.54 ± 21.81 min daily, traveled 1488.15 ± 700.09 meters at an average speed of 0.43 ± 0.16 m/s, and executed approximately 910 turns and 428 starts/stops per day. Mobility predominantly occurred in short bouts (<215 seconds), accounting for 94% of the mobility bouts. Mean mobility characteristics remained consistent across participants despite individual variability in high-resolution metrics, including starts/stops, turns and navigated slopes exceeding the ADA recommended ratio. This methodology provides insights into real-world manual wheelchair mobility and future research could inform rehabilitation strategies and assistive technology development. These methods underscore the critical importance of personalized, high-resolution mobility assessments in understanding and optimizing manual wheelchair users’ functional independence and quality of life. Introduction Manual wheelchair (MWC) use is a critical aspect of mobility for many individuals with spinal cord injuries (SCIs) and other disabilities. It is estimated that about 40% of the 1.5 million MWC users in the U.S. are individuals with SCIs [ 1 ]. Each year, approximately 18,000 new cases of traumatic SCIs occur in the United States, contributing to a growing population of MWC users [ 2 ]. Limited mobility can negatively affect overall health and has been linked to various health conditions, including diabetes and obesity [ 3 – 6 ]. Consequently, understanding the factors that impact mobility and accessibility during daily life is crucial. Despite the well-documented benefits of an active lifestyle, research indicates that physical inactivity is more prevalent among individuals with disabilities compared to those without disabilities [ 7 ]. Reduced mobility can further worsen secondary health complications, such as pressure injuries, obesity, cardiovascular disease, and depression. MWC users face unique challenges to maintain sufficient mobility and physical activity levels due to inadequate accessibility, lack of specialized exercise equipment, and upper extremity pain or fatigue [ 8 ]. Several studies have identified environmental barriers that limit the mobility and participation of MWC users, including obstacles related to the environment, such as narrow doorways, steep ramps, and uneven sidewalks. Inaccessible public transportation, lack of accessible housing, and attitudinal barriers can also restrict community participation [ 9 ]. Addressing these challenges requires innovative approaches to accurately assess and monitor the mobility of MWC users in their daily environments. Gaining insights into the daily experiences and challenges faced by wheelchair users requires an understanding of their mobility in real-world environments. Advancements in wearable sensor technologies have enabled researchers to objectively measure these patterns in free-living conditions, allowing them to collect data and compute metrics such as distance traveled, speed, movement duration, and number of turns, all of which provide a deeper understanding of free-living wheelchair mobility [ 10 – 17 ]. Previous research has established baseline metrics for wheelchair mobility. Researchers [ 16 ] monitored the mobility characteristics and activity levels of MWC users, employing a custom data logger on the MWC in residential settings and at the National Veterans Wheelchair Games. In home environments, wheelchair users traveled an average of 2,457.0 ± 1,195.7 meters daily at a speed of 0.79 ± 0.19 meters per second, accumulating 47.9 ± 21.4 minutes of wheelchair movement. Similarly, another study [ 18 ] examined activity and mobility in long-term care facilities, finding that residents traveled 532 ± 406 meters per day at 0.76 ± 0.18 meters per second and engaged in 10.8 ± 6.9 minutes of movement per hour. Another investigation [ 12 ] quantified wheelchair mobility and assessed the relationship between wheelchair mobility, demographics, type of manual wheelchair, and participation, using a data-logging device on the MWC and survey-based assessment tool (Craig Handicap Assessment Recording Technique) for assessing the degree to which a disability limits a person’s ability to participate in their community. Participants covered approximately 1,900 meters per day at a speed of 0.63 meters per second and on average traveled for less than 60 minutes daily. Previous research [ 13 ] introduced the concept of “bouts of mobility,” providing an analysis of how MWC users move during everyday life by evaluating continuous periods of movement using a wheel-mounted accelerometer. They revealed that the median bout lasted 21 seconds, covered 8.6 m, and occurred at 0.43 m/s. Notably, 63% of bouts were shorter than 30 seconds, and 85% lasted 60 seconds or less. More recent work [ 15 ] expanded on these findings by incorporating turn analysis using inertial measurement units (IMUs). They observed that wheelchair users make approximately 900 turns per day, with a mean velocity of 0.48 m/s and a mean daily wheeled distance of 3.10 km. Additionally, other studies have covered various aspects of wheelchair use, including the assessment of physical activity levels [ 19 – 21 ] and the analysis of movement in wheelchair sports [ 22 – 25 ]. While existing research has established fundamental wheelchair mobility metrics including bout duration, distance, turning frequency, and velocity through sensor-based measurements, there remains a critical gap in understanding how these movement characteristics vary across different environmental contexts and daily activities. This knowledge gap limits our ability to develop properly tailored mobility assistance technologies and environmental interventions that reflect real-world wheelchair usage patterns. Building upon previous research, our study aimed to develop a comprehensive analysis of MWC mobility by employing data measured by IMUs to assess and quantify detailed characteristics of MWC use during daily life. Such an approach can be used to understand modifiable factors that impact MWC mobility and their relationship to daily arm use for mobility. Our analysis includes traditional mobility metrics including distance traveled, movement duration, and mean speed. Additionally, we incorporated a more detailed analysis of MWC maneuvering, including the number of turns, starts, stops, and a novel approach called mobility profiles to classify bouts of mobility to explain different patterns of movement behavior. For each bout of mobility, we calculated bout continuity (ratio of time spent moving to total bout time), which has been identified as important for understanding indoor versus outdoor pedestrian mobility [ 26 ], and identified if the MWC user navigated any slopes exceeding ADA guidelines. Our comprehensive analysis provides a more nuanced understanding of wheelchair mobility in everyday environments. Understanding mobility patterns can inform the development of tailored interventions and assistive technologies to enhance the mobility and participation of individuals who rely on MWCs. Material and methods Participants This study was approved by the Mayo Clinic Institutional Review Board (15-004974). Participants were recruited as part of a larger longitudinal investigation titled “Natural History of Shoulder Pathology in Wheelchair Users” ( NCT02600910 ). Recruitment for the study began July 2015 and is currently ongoing. Eligible individuals were between 18 and 70 years of age, had sustained a spinal cord injury (SCI), and utilized a MWC as their primary means of mobility. Written informed consent was obtained from all participants before enrollment. Real-world data was collected from a cohort of 12 MWC users with SCI ( Table 1 ) over seven consecutive days. Participants had a mean age of 47 ± 13.4 years and a mean time since SCI of 21 ± 13.7 years. SCI levels were classified into three groups: high (C6-C8), mid (T1-T8), and low (T9-L1). View this table: View inline View popup Download powerpoint Table 1. Subject Demographics Free-living data collection protocol We utilized eight IMUs (Axivity-AX6; range ±16 g and ±2000 deg/s, sampling frequency 100 Hz, weight 11g) that were placed on the MWC (frame and wheels) ( Fig 1 ) and the MWC user (thorax, forearms, and upper arms - data was not used in this investigation). The IMUs used in this study allowed seven days of continuous data recording without recharging when operating in full IMU mode (recoding both linear acceleration and angular velocity) at 100 Hz. Download figure Open in new tab Fig 1. Inertial measurement unit (IMU) placement on MWC. The image illustrates the placement of the three MWC-mounted IMUs. The IMUs were secured with custom 3D-printed holders. Red circles indicate the mounting locations at both wheel hubs and the wheelchair frame. This study aimed to capture data representative of the participants’ typical routines, requiring 7 days of data to ensure comprehensive monitoring without disrupting normal behavior patterns [ 27 ]. Sensors were secured on the wheelchair for the entirety of the week-long data collection, as no charging was required. Participants were asked to begin each day with a wheelchair pivot movement ( Fig 2 ), creating synchronization data (angular velocity about a vertical axis) for use in post-processing to correct for time drift between sensors [ 28 – 30 ]. Additionally, participants were asked to continue their daily activities as usual and not to change their routines. Upon completion of the data collection period, a research team member retrieved each set of sensors during an in-person visit. Download figure Open in new tab Fig 2. Wheelchair Pivot Maneuver for IMU Synchronization. Schematic illustration of a wheelchair pivot maneuver. The sequence shows four positions during the maneuver: starting position (left), clockwise rotation (middle-left), counterclockwise rotation (middle-right), and return to starting position (right). Red arrows indicate the direction of rotation. This standardized movement pattern creates distinct angular velocity measurements across all IMUs. Data processing and variable extraction Once the sensors were returned to the research team, data were downloaded from the IMUs onto a computer and processed ( Fig 3 ). Sensor data were segmented by days. Next, the data were synchronized using the chair pivoting timing throughout the week by using cross-correlation to calculate the time shift between sensors using the measured angular velocities about a vertical axis (inertial reference frame) from each sensor. For each day, sensor-to-wheelchair alignment and orientation were calculated for each sensor. Download figure Open in new tab Fig 3. Data Processing for Multi-Sensor Synchronization. i. Raw sensor data from 9 sensors is separated into daily sections, from 3:00 am of the current day to 2:59 am of the next day, ii. Angular velocity measurements are corrected by removing any systematic offset for each axis, iii. Initial orientation quaternions are calculated from the corrected angular velocities and accelerations, iv. Angular velocity is transformed into the world frame using the calculated orientation, with a focus on the z-axis component, v. Processed data for each day is saved separately, containing corrected angular velocity, acceleration, world-frame z-axis angular velocity, orientation quaternions, and temperature, vi. All processed sensor data for the day is combined into a single dataset, vii. Synchronization movements are identified using the world-frame angular velocity, viii. Temporal drift between sensors is determined using cross-correlation and corrected, viiii. Data is synchronized across sensors, x. Participant sensor placement is confirmed for correct side and body segment; resolved if needed, xi . Orientation is recalculated. Orientation calculations Acceleration and angular velocity measurements made in the sensor-fixed frame were resolved into components relative to an inertial frame using estimates of the sensor orientation relative to an inertial frame obtained from sensor fusion [ 31 ]. The direction cosine matrix that defines the transformation of measurements made in the sensor-fixed frame to those in the inertial frame was defined as R inertial|IMU . Sensor-to-wheel alignment Each wheel’s axis of rotation was defined with a principal component analysis (PCA) of the measured wheel angular velocity. The first principal component, which captures the majority of variation in measured angular velocity, is used to define the axis of rotation and is aligned with the wheel axle direction. Wheel rotation velocity is calculated by taking the dot product of the measured angular velocity and the wheel axis of rotation ( ω wheel,i where i = R or L for right or left wheel). The linear velocity of each wheel was calculated by multiplying the wheel rotation velocity with the wheel radius. Wheelchair speed (v WC ) was the average of the calculated linear velocities of both wheels. Wheelchair linear acceleration (a WC ) was obtained by numerically differentiating the wheelchair speed. Sensor-to-wheelchair alignment For the IMU ( Fig 4A ) secured to the wheelchair frame, we defined two reference frames: (1) a wheelchair-fixed frame ( Fig 4B ), which is fixed to the wheelchair (and therefore the IMU) with axes aligned with the wheelchair and (2) an inertial frame ( Fig 4C ), which is fixed relative to gravity and an arbitrary heading direction. Download figure Open in new tab Fig 4. Reference frame definitions for IMU-wheelchair alignment. Illustration of three coordinate reference frames used for IMU data analysis on a manual wheelchair. ( A) A sensor-fixed frame, which is fixed to and rotates with the IMU. (B) A wheelchair-fixed frame is fixed to and rotates with the wheelchair with axes aligned with axes consistent with vehicle dynamics. (C) An inertial frame, which is fixed relative to gravity and an arbitrary heading direction and does not rotate with the sensor or wheelchair. First, we defined the wheelchair-fixed frame The average acceleration due to gravity measured during no movement was used to define a wheelchair-fixed z-axis assuming that the axis is aligned with gravity [ 32 ]: We defined the forward direction axis this approach assumes that one sensor axis is already aligned well with a wheelchair axis: And finally, we ensured that the axis is orthogonal to the and wheelchair axes: The resulting unit vectors define a wheelchair-fixed frame aligned to the wheelchair frame axes. Where R WC|IMU is the direction cosine matrix describing the transformation from the sensor-fixed frame to WC-fixed frame. To improve estimates of the wheelchair frame orientation, we subtracted the calculated acceleration of the wheelchair (a WC ) obtained from the wheel’s angular velocity from acceleration resolved in the wheelchair-fixed frame. This new acceleration was used to recalculate orientation [ 31 ]. We utilized wheelchair, sensor, and inertial frame measurements to quantify mobility ( Table 2 ). We calculated wheelchair speed, angular velocity of the MWC about vertical, wheelchair movement trajectories, wheelchair displacement, and tilt of the wheelchair. We used these calculated wheelchair kinematics to identify turns, starts and stops, mobility bouts, slopes, and slopes that exceed the ADA-compliant ratio. View this table: View inline View popup Table 2. Mobility metrics definitions and sensors used. Mobility metrics Data analysis period duration: The duration of the data analysis period was measured in hours, spanning from the first detected movement of the MWC to its last movement within the defined 24-hour timeframe (3 AM to 2:59 AM the following day), to account for individuals who remain active past midnight. Number of turns: The number of turns was determined using the IMU attached to the wheelchair frame relative to the inertial frame. We defined a turn when the angular velocity exceeded a threshold of 10°/sec, where short pauses (≤ 0.05 seconds) between movements were merged into ongoing movements and movement bouts shorter than 1 second were excluded. These thresholds were informed through the analyses of pilot data measured during an in-lab data collection and a supervised outdoor data collection; turns were confirmed by examining the MWC position trajectories, which included figure-eight routes and outdoor propulsion on a college campus. Number of starts and stops: Starts and stops were identified when wheelchair speed exceeded ±0.1 m/s for at least 2 seconds. A 1-second drop in wheelchair speed was allowed within each event, and at least one wheel revolution was required to count as a start or stop. Mobility bouts: Mobility bouts began when wheelchair speed exceeded 0.1 m/s with at least one complete wheel revolution and continued through intermittent stops until there was one minute of continuous inactivity ( Fig 5 ). This definition captures the stop-and-start nature of free-living wheelchair propulsion, with the one-minute threshold based on average traffic light cycle times adapted from the definition of the maximum resting period from [ 33 ] and the average cycle time of traffic lights (National Association of City Transportation). Continuity: Ratio of time spent moving to the total duration of a mobility bout, with 100% being representative of a continuous mobility bout with no stopping during propulsion. Total distance : Total distance per mobility bout was calculated using the number of complete wheel revolutions, calculated from wheel radius and wheelchair speed [ 34 ]. Displacement: The total change in position of the wheelchair from the starting location of the bout, calculated using the trajectory derived from the yaw angle (θ yaw ) and wheelchair linear velocity. The yaw angle was calculated from the orientation of the wheelchair axis relative to the inertial frame: where R inertial|WC represents the orientation of the inertial reference frame relative to the wheelchair. The velocities of the wheelchair in the x and y directions, v x and v y , were computed as follows: Download figure Open in new tab Fig 5. Movement bouts and bout continuity. Time series plot showing wheelchair speed data for a single participant, illustrating key bout metrics. The figure highlights three distinct movement bouts. These velocities were integrated over time to determine the displacement in the x and y directions, denoted as d x and d y , respectively. The total displacement was then calculated using the magnitude of the 2D displacement vector, which combines both x and y directional movements to give the overall change in position. Slope: The slope angle was defined by the pitch angle (θ slope ) defined by the vertical orientation relative to the wheelchair-fixed frame A moving average filter using a window size of 5 seconds was used to smooth the data by averaging it over a 500 samples (5 seconds × 100 samples/second). Slope was considered non-zero when the pitch angle exceeded 0.5° and was maintained for at least one meter. ADA compliance: The number of slopes encountered during a bout where the slope angle exceeded the American disability act recommendation (4.76 degrees or 1:12 ratio). Download figure Open in new tab Figure 6. Relationship between wheelchair speed and slope during real-world mobility. Wheelchair speed and pitch angle data (slope) for a single participant over a portion of a day (approximately 5 minutes). Statistical analyses MWC mobility bouts were analyzed using five metrics (duration, continuity, mean speed, total distance, and displacement data) from twelve participants. A k-means cluster analysis was performed to categorize movement bouts. Descriptive statistical analysis was conducted on 7 days of mobility data from 12 manual wheelchair users, categorized by injury level (Mid: T1-T8, n=6; High: C6-C8, n=3; Low: T9-L1, n=3). For each participant, means, standard deviations, medians, and ranges were calculated across the 11 mobility metrics. Within-person and between-person variability was assessed visually by examining data distribution patterns and histograms and quantitatively with calculated range and standard deviation. The participants were divided into three groups based on injury level to identify potential differences in mobility characteristics across injury levels. Mobility profiles Individual mobility profiles were created by calculating totals per week of the number of turns, starts and stops, and slopes steeper than 1:12 rise (ADA compliance slope). Mobility profiles also included the per week number of short duration (700s) bouts; thresholds for each bout were determined from the k-means cluster analysis. Since short mobility bouts account for 94% of all bouts, additional metrics were calculated for short mobility bouts, including total movement duration, total distance covered, mean velocity, and maximum velocity attained. The mobility profiles provide insight into the physical demands posed by the MWC users ‘environments. Together, these metrics offer a comprehensive assessment of participants’ movement patterns, capturing the dynamic aspects of their mobility and their interaction with varying terrain. Results Our analysis incorporates data from all participants (n = 12) collected over a total of 84 days, encompassing a total of 1,213 hours of participant data analysis time and 6,024 mobility bouts. On average, participants recorded 16 ± 2 hours (median: 16 hours; range: 9–22 hours) of data analysis time and 502 ± 109 mobility bouts per day (median: 494; range: 328–674). These mobility bouts included a cumulative movement duration of 92 hours and a total distance of 125 km for all participants. The median mobility bout lasted 40 seconds, covering 9 meters at an average speed of 0.43 m/s ( Table 3 ). The substantial differences between mean and median values highlight a skewed distribution, with outliers contributing to consistently higher means than medians, underscoring the impact of long duration mobility bouts on the overall statistical distribution. View this table: View inline View popup Download powerpoint Table 3. Descriptive statistics for all mobility bouts (n = 6024). Traditional mobility metrics showed patterns of variability that differed between and within participants ( Fig 7 ). Within-participant variability appears more pronounced than between-participant variability across mobility bouts movement duration, mean speed, and total distance. Participants were categorized into Low (T9-T1), Mid (T1-T8), and High (C6-C8) level of SCI groups, however, no clear patterns emerged for the measured bout metrics across these groups. Movement durations were 24.9-43.64 seconds, with Participant 1 (male, age range = 30-39 years, time since injury = 9 years, SCI level = Mid) showing the highest median duration and largest variability. Mobility bout total distances showed high within-person variability, with median values between 6.94-15.08 meters, although Participants 1 and 6 (male, age range = 50-59 years, time since injury = 28 years, SCI level = Mid) covered notably longer distances. Median bout speeds ranged from 0.34-0.48 m/s, with Participant 6 showing the highest median speed and Participant 8 (male, age range = 30-39 years, time since injury = 12 years, SCI level = High) the lowest for all mobility bouts. Download figure Open in new tab Fig 7. Comparison of mobility metrics across participants and SCI levels. (a) Total data analysis periods in hours and number of movement bouts per 7-day collection by participant. (b) Mobility bout movement duration (s), distance (m), and mean speed (m/s) across participants. Participants with red data points have a low-level SCI, blue data points have a mid-level SCI, and green data points have a high-level SCI. To better understand the range of movement speeds for each participant (between-day, within-person), we supplemented the mean speed data with a histogram illustrating the distribution of raw MWC speeds recorded over a week ( Fig 8 ). This approach captures the variability in the speed ranges, emphasizing that individuals exhibit diverse speed profiles across movement bouts. Download figure Open in new tab Fig 8. Distribution of time spent at different wheelchair speeds for each participant over one week. The bin width in the histogram is 0.1 m/s. Note that only the speeds between 0 and 2 m/s are shown to provide a closer view of the slower-speed range. The different MWC speed distributions and time spent at each speed highlights the varying mobility patterns between participants, which can be further understood by examining the characteristics of different mobility bouts ( Fig 9 ). For example, short bouts had high continuity (87%), covered a mean displacement of 5.91 m, and had an average mean speed of 0.42 m/s. Medium duration bouts were less continuous (69%), had a mean displacement of 26.83 m, and a mean speed of 0.49 m/s. Long duration bouts had high continuity (85%), covered a mean displacement of 186.98 m, and had a mean speed of 0.75 m/s. Shorter-duration bouts accounted for almost 94% of the total bouts compared to long bouts, which accounted for only 0.24% of the bouts during the 7-day collections. Download figure Open in new tab Fig 9. Mobility bout classification. Comparison of the mean mobility bout characteristics for short (blue), medium (orange), and long (yellow) duration bouts for all participants. High-resolution mobility metrics characterized each participant’s mobility, including the number of bout types, turns, starts and stops, and the number of slopes exceeding the ADA recommend 1:12 ratio ( Table 4 ). Participant 6, with a mid-level injury, demonstrated the highest overall activity, recording 10,290 turns, 4273 starts/stops, and 674 mobility bouts across 7 days. In contrast, Participant 12, with a low-level injury, showed the least activity with 3,377 turns, 1,426 starts/stops, and 328 mobility bouts. View this table: View inline View popup Download powerpoint Table 4. Mobility metrics for 7-day Collection Table 5 summarizes key mobility metric daily statistics for each participant, including movement duration, distance traveled, and MWC speed. It also provides more detailed insights into the complexity of daily wheelchair use, highlighting the frequency of turns, starts and stops, encounters with steep slopes, and the number of mobility bouts. View this table: View inline View popup Download powerpoint Table 5. Key daily mobility metric statistics. We analyzed the mean bout slopes across all participants showing the distribution of mean bout slopes for each of the 12 participants ( Fig 10 ), which revealed significant variability in the terrain navigated by individuals with SCI. Most bouts for all participants occurred on relatively flat surfaces, with mean slopes clustering between 0 and 2 degrees. However, each participant demonstrated the ability to navigate steeper inclines, as evidenced by the scattered data points at higher angles. Some participants showed instances of navigating slopes exceeding the ADA recommendation. Download figure Open in new tab Fig 10. Mean bout slopes across all movement bouts for 12 participants. The x-axis represents the participants, while the y-axis shows the mean bout slope in degrees, with data ranging from 0 to 11 degrees. The black line at approximately 4.8 degrees represents the ADA (Americans with Disabilities Act) compliant slope angle limit, and the percentage represents the percentage of each participant’s navigated slopes that were steeper than the ADA recommendation. Short-duration bouts accounted for 5675 out of 6024 total bouts for all participants. A comparison of short-duration mobility bout metrics grouping participants with Low (T9-L1), Mid (T1-T8), and High (C6-C8) levels of SCI is depicted in Fig. 11 . The spider plots illustrate each participant’s number of bouts in 7 days, total 7-day movement duration, mean speed, max speed, and total distance covered in 7 days during all short duration bouts. Participants exhibited diverse data profiles with notable individual variations, and no clear pattern emerged due to spinal cord injury (SCI) level classification. Each participant demonstrated unique mobility characteristics, with some individuals showing significantly higher values across multiple parameters. Download figure Open in new tab Fig 11. 7-day mobility metrics across three SCI injury levels: Low (T9-L1), Mid (T1-T8), and High (C6-C8). Each plot’s scale starts at zero in the center, with the maximum value representing the highest value across all subjects for all short duration movement bouts. Total distance and movement duration represent the cumulative 7-day data for all short movement bouts. We created a MWC user mobility profile for each participant. The profile is a summary of 7-day mobility metrics and includes the number of turns, starts/stops, steep slopes, and bouts. For short duration bouts. A spider plot with 7-day movement duration, mean speed, maximum speed, 7-day distance, and the number of short duration bouts was created. Finally, the mobility profile includes a scatter plot of slopes navigated during 7 days of data collection with an ADA recommendation line drawn at 4.8 degrees. Fig 12 compares the mobility profiles of the most active (P6) and least active (P12) participants. Download figure Open in new tab Fig 12. Comparison of 7 Day Mobility Profiles between Participant 6 (P6) and Participant 12 (P12), across various metrics. P6 (blue) and P12 (orange), are both 50-59 years old. The metrics show P6 had more turns (10290 vs. 3377), starts/stops (4273 vs. 1426), short duration (700s) bouts. The short duration bout spider plot also reveals P6 had longer total 7-day movement duration, higher maximum speed, and higher mean speed compared to P12. Discussion Quantifying demanding physical movements such as the number of turns, number of starts and stops, and slopes that exceed the ADA compliance is crucial for understanding mobility demands for the individual and comparisons between participants. Our study, conducted with 12 participants over a total of 84 days (7 days per participant), provides insights into wheelchair mobility during daily life and provides a blueprint for adding high-resolution mobility metrics to research and clinical settings. While our sample size is smaller than some previous studies, our findings corroborate and diverge from existing literature in interesting ways. The data suggest the benefit of individualized assessments and interventions for people with SCI. Table 6 compares our findings to the results of five other studies [ 12 , 13 , 15 – 17 ] on MWC mobility in adults. Despite differences in study populations, daily distance outcomes are generally similar, except for one study on competitive athletes [ 16 ] and another conducted in a different country [ 15 ]. These discrepancies may stem from variations in sample sizes, community settings, and participant demographics. For example, the National Veterans Wheelchair Games (NVWG) likely attracts individuals with more active lifestyles than our convenience sample of patients from local healthcare clinics. Additionally, previous studies have linked employment status to greater wheeled distances [ 15 , 16 ]. View this table: View inline View popup Download powerpoint Table 6: Comparison of studies measuring daily MWC use in everyday life. Our analysis of bout lengths revealed some differences compared to previous research. We found longer mean bout lengths than previously reported [ 13 ]. This difference can likely be attributed to variations in mobility bout definitions across studies. While our study defined a bout as a period of wheelchair propulsion capturing intentional transitions between activities with pauses less than one minute (beginning when wheelchair speed exceeded 0.1 m/s with at least one complete wheel revolution), previous research used different criteria: bouts lasting at least 5 seconds, with speeds ≥ 0.12 m/s, ending when less than 0.76 m were wheeled within 15 seconds [ 13 ]. These different definitions likely account for the observed variations, although they might also reflect genuine differences in mobility patterns influenced by community environments. We defined three distinct categories of movement bouts based on duration, with short bouts (< 215s) comprising nearly 94% of weekly activity. The high standard deviations in our bout characteristics also suggest considerable variability in individual mobility. Each participant shows considerable spread in their measurements ( Fig 7 ), as evidenced by the scattered data points extending well beyond their respective box plots and numerous outliers. In contrast, between-participant variability appears more modest, with median values remaining relatively consistent across participants and is particularly visible in mean speed where medians cluster around 0.4 m/s. The interquartile ranges (boxes) also show similar patterns across participants, suggesting that while individuals exhibit high variability in the higher levels of mobility metrics, the central tendencies are fairly consistent across different participants. The histograms ( Fig. 8 ) highlight that higher speeds occur less frequently, indicating that while average speeds provide a useful measure, the distribution offers useful insight into individual mobility, illustrating how often participants reach different speeds within the week. The majority of mobility time is spent at lower speeds, with a noticeable decrease in time as speed increases ( Fig 8 ). Tracking these less frequent but higher levels of mobility may be important to understanding the physical demands of navigating environments or certain activities and understanding why some individuals develop mobility-related health problems while others do not. Our study contributes novel new information on continuity, the frequency of starts and stops (428 ± 125 per day), and encounters with slopes exceeding ADA recommendations (2.72 ± 3.35, per week), providing valuable insight into the physical demands that wheelchair users face in navigating their environments. In addition to the high physical demands of movement, more force and torque are required when MWC users propel on surfaces with greater resistance, such as ramps, interlocking pavers, and grass [ 35 , 36 ]. While most mobility occurred on relatively flat surfaces, participants occasionally navigated slopes exceeding ADA-compliant limits. Our study also underscores the importance of analyzing the number of starts, stops, and turns, as these actions require larger changes in linear and angular momentum compared to steady-state propulsion [ 37 ]; previous research has demonstrated that stress on the upper extremities during propulsion is higher during acceleration than during constant velocity. Additionally, studies have shown that the physical demands during the starting phase of wheelchair propulsion are comparable to those experienced during ramp propulsion [ 38 ], further emphasizing the importance of considering these transitional movements in MWC mobility analyses. Participants 10 and 11 exhibited the highest concentration of bouts with average slopes near or above the ADA limit, suggesting either a more challenging living environment or greater mobility capabilities. In contrast, participants 4, 8, and 12 showed the least variability in slope navigation, with most bouts occurring on flatter surfaces. While outliers reaching up to 17 degrees were observed for some participants, indicating navigation of significantly steep inclines, these steeper sections may have included instances of assisted pushing. Future work will incorporate arm movement sensors to definitively distinguish between self-propulsion and assistance during these high-slope regions. We observed substantial variability in mobility patterns across 12 SCI subjects over a week, leading to the development of an SCI mobility profile that incorporates various metrics to characterize individual mobility patterns ( Fig 12 ). While the distance traveled per day, mean speed and minutes of MWC mobility per day results align with existing wheelchair mobility literature, our high-resolution mobility metrics reveal significant individual variability among participants with SCI, suggesting that understanding these differences can provide valuable insights into how mobility demands relate to factors like community participation, quality of life, and health, highlighting the importance of personalized assessment in rehabilitation strategies and assistive technology design. There are important limitations to consider when interpreting the results of our study. Our relatively small sample size of 12 participants, limiting the generalizability of our findings to the broader wheelchair user population. We know that two participants experienced non-typical work weeks (due to training and the holidays). As has been mentioned in other studies [ 16 ] it is common for individuals to use more than one wheelchair during the week, however we only mounted sensors on one wheelchair. We know that this impacted one of our participants (P4: male, age range = 50-59 years, time since injury = 27 years, SCI level = Low) who uses a different wheelchair when mobilizing in the lower level of their house. The purpose of this study was to introduce new MWC mobility analysis methods using high-resolution mobility metrics and to demonstrate the potential utility of our approach. A larger sample size is needed to provide normative data for these metrics. While our study captured various aspects of wheelchair mobility, environmental factors such as surface types (e.g., interlocking pavers, grass) were not systematically documented; these surfaces require higher forces and torques during propulsion [ 34 , 35 ]. The sensor technology used in this study does not directly measure the surface on which the MWC is rolling, however surveys could be added to our approach to gain more information about biomechanically demanding surfaces. Conclusion Our analysis employs a novel approach to characterize manual wheelchair mobility during daily life, which enables a unique understanding of the propulsion-related demands experienced by manual wheelchair users. This study demonstrates that the data recorded by three manual wheelchair-mounted inertial measurement provides rich information that can be used to quantify mobility. While mean bout speed, duration, and total distance were consistent across participants, higher-resolution mobility metrics revealed significant individual variability. Specifically, metrics such as the number of turns, starts and stops, and the number and steepness of slopes varied considerably, suggesting differences in upper extremity loading during real-world manual wheelchair use. Mobility characteristics can differ due to variations in community environments, employment status, and weekly activities at the time of data collection. Our findings underscore the need for personalized assessments to better understand mobility and the potential for tailored interventions to reduce the risk of overuse injuries in manual wheelchair users. Future research should focus on developing methods to distinguish between self-propulsion and assisted propulsion in manual wheelchair users; incorporating additional body-worn sensors will enhance the accuracy and reliability of these detections. Additionally, identifying surface types and quantifying transfers are important factors that impact upper extremity loading that need further exploration. Expanding data collection to include a more diverse range of individuals from different communities will be crucial for understanding how mobility and biomechanical demands vary across populations and communities. Longitudinal studies are also needed to track changes in biomechanical demands and shoulder health over time, enabling the development of proactive strategies to prevent shoulder health decline. Additionally, exploring interventions such as new wheelchair designs, assistive technologies, or training programs could help mitigate the negative impacts of propulsion demands on musculoskeletal health. Integrating additional variables, such as individual physical characteristics and wheelchair configurations, into future analyses could uncover deeper insights into the factors influencing mobility profiles and lead to more personalized recommendations. Addressing these areas will build on our current knowledge and contribute to improving the quality of life for manual wheelchair users. Author Contributions Conceptualization: Kathylee Pinnock Branford, Stephen M. Cain Formal analysis: Kathylee Pinnock Branford, Stephen M. Cain Methodology: Kathylee Pinnock Branford, Stephen M. Cain, Omid Jahanian, Meegan G. Van Straaten, Melissa M. B. Morrow. Project administration: Melissa M. B. Morrow, Stephen M. Cain Software: Kathylee Pinnock Branford, Stephen M. Cain Supervision: Stephen M. Cain, Melissa M. B. Morrow. Visualization: Kathylee Pinnock Branford. Writing – original draft: Kathylee Pinnock Branford Writing – review & editing : Kathylee Pinnock, Stephen M. Cain, Omid Jahanian, Meegan G. Van Straaten, Melissa M. B. Morrow. Supporting information S1 Data. Dataset of movement bout metrics for all participants (n= 12) (CSV) S2 Data. Dataset of daily movement metrics: turns, bouts, starts, and stops (XLSX) References 1. ↵ Finley MA , Euiler E . Association of musculoskeletal pain, fear-avoidance factors, and quality of life in active manual wheelchair users with SCI: A pilot study . Journal of Spinal Cord Medicine . 2020 ; 43 : 497 – 504 . doi: 10.1080/10790268.2019.1565717 OpenUrl CrossRef PubMed 2. ↵ SCIMS 2023 Annual Report-Complete Public Version . 3. ↵ Levine JA , McCrady SK , Lanningham-Foster LM , Kane PH , Foster RC , Manohar CU . The Role of Free-Living Daily Walking in Human Weight Gain and Obesity . Diabetes . 2008 ; 57 : 548 – 554 . doi: 10.2337/db07-0815 OpenUrl Abstract / FREE Full Text 4. Johnson ST , Boulé NG , Bell GJ , Bell RC . Walking: a matter of quantity and quality physical activity for type 2 diabetes management . Applied Physiology, Nutrition, and Metabolism . 2008 ; 33 : 797 – 801 . doi: 10.1139/H08-055 OpenUrl CrossRef PubMed 5. Hu FB , Sigal RJ , Rich-Edwards JW , Colditz GA , Solomon CG , Willett WC , et al. Walking Compared With Vigorous Physical Activity and Risk of Type 2 Diabetes in Women . JAMA . 1999 ; 282 : 1433. doi: 10.1001/jama.282.15.1433 OpenUrl CrossRef PubMed Web of Science 6. ↵ Warms CA , Belza BL , Whitney JD . Correlates of Physical Activity in Adults With Mobility Limitations . Fam Community Health . 2007 ; 30 : S5 – S16 . doi: 10.1097/01.FCH.0000264876.42945.e4 OpenUrl CrossRef PubMed Web of Science 7. ↵ McGuire DO , Watson KB , Carroll DD , Courtney-Long EA , Carlson SA . Using two disability measures to compare physical inactivity among US adults with disabilities . Prev Chronic Dis . 2018 ; 15 . doi: 10.5888/pcd15.170261 OpenUrl CrossRef 8. ↵ Selph SS , Skelly AC , Wasson N , Dettori JR , Brodt ED , Ensrud E , et al. Physical Activity and the Health of Wheelchair Users: A Systematic Review in Multiple Sclerosis, Cerebral Palsy, and Spinal Cord Injury. Archives of Physical Medicine and Rehabilitation . W.B. Saunders ; 2021 . pp. 2464 – 2481 .e33. doi: 10.1016/j.apmr.2021.10.002 OpenUrl CrossRef 9. ↵ Kochtitzky CS , Freeland AL , Yen IH . Ensuring mobility-supporting environments for an aging population: Critical actors and collaborations . Journal of Aging Research . 2011 . doi: 10.4061/2011/138931 OpenUrl CrossRef 10. ↵ de Vries WHK , Amrein S , Arnet U , Mayrhuber L , Ehrmann C , Veeger HEJ . Classification of Wheelchair Related Shoulder Loading Activities from Wearable Sensor Data: A Machine Learning Approach . Sensors . 2022 ; 22 . doi: 10.3390/s22197404 OpenUrl CrossRef PubMed 11. Hiremath S V. , Hogaboom NS , Roscher MR , Worobey LA , Oyster ML , Boninger ML . Longitudinal Prediction of Quality-of-Life Scores and Locomotion in Individuals With Traumatic Spinal Cord Injury . Arch Phys Med Rehabil . 2017 ; 98 : 2385 – 2392 . doi: 10.1016/j.apmr.2017.05.020 OpenUrl CrossRef PubMed 12. ↵ Oyster ML , Karmarkar AM , Patrick M , Read MS , Nicolini L , Boninger ML . Investigation of factors associated with manual wheelchair mobility in persons with spinal cord injury . Arch Phys Med Rehabil . 2011 ; 92 : 484 – 490 . doi: 10.1016/j.apmr.2010.09.025 OpenUrl CrossRef PubMed 13. ↵ Sonenblum SE , Sprigle S , Lopez RA . Manual Wheelchair Use: Bouts of Mobility in Everyday Life . Rehabil Res Pract . 2012 ;2012: 1 – 7 . doi: 10.1155/2012/753165 OpenUrl CrossRef 14. Sonenblum SE , Sprigle SH , Martin JS . Everyday sitting behavior of full-time wheelchair users . J Rehabil Res Dev . 2016 ; 53 : 585 – 598 . doi: 10.1682/JRRD.2015.07.0130 OpenUrl CrossRef PubMed 15. ↵ Togni R , Kilchenmann A , Proffe A , Mullarkey J , Demkó L , Taylor WR , et al. Turning in Circles: Understanding Manual Wheelchair Use Towards Developing User-Friendly Steering Systems . Front Bioeng Biotechnol . 2022 ; 10 . doi: 10.3389/fbioe.2022.831528 OpenUrl CrossRef 16. ↵ Tolerico ML , Ding D , Cooper RA , Spaeth DM , Fitzgerald SG , Cooper R , et al. Assessing mobility characteristics and activity levels of manual wheelchair users . J Rehabil Res Dev . 2007 ; 44 : 561 – 571 . doi: 10.1682/JRRD.2006.02.0017 OpenUrl CrossRef PubMed Web of Science 17. ↵ Levy CE , Buman MP , Chow JW , Tillman MD , Fournier KA , Giacobbi P . Use of Power Assist Wheels Results in Increased Distance Traveled Compared with Conventional Manual Wheeling . Am J Phys Med Rehabil . 2010 ; 89 : 625 – 634 . doi: 10.1097/PHM.0b013e3181e72286 OpenUrl CrossRef PubMed 18. ↵ Sonenblum SE , Sprigle S , Caspall J , Lopez R . Validation of an accelerometer-based method to measure the use of manual wheelchairs . Med Eng Phys . 2012 ; 34 : 781 – 786 . doi: 10.1016/j.medengphy.2012.05.009 OpenUrl CrossRef PubMed 19. ↵ Brogioli M , Popp WL , Schneider S , Albisser U , Brust AK , Frotzler A , et al. Multi-Day Recordings of Wearable Sensors Are Valid and Sensitive Measures of Function and Independence in Human Spinal Cord Injury . J Neurotrauma . 2017 ; 34 : 1141 – 1148 . doi: 10.1089/neu.2016.4583 OpenUrl CrossRef PubMed 20. Popp WL , Brogioli M , Leuenberger K , Albisser U , Frotzler A , Curt A , et al. A novel algorithm for detecting active propulsion in wheelchair users following spinal cord injury . Med Eng Phys . 2016 ; 38 : 267 – 274 . doi: 10.1016/j.medengphy.2015.12.011 OpenUrl CrossRef PubMed 21. ↵ Schneider S , Popp WL , Brogioli M , Albisser U , Demkó L , Debecker I , et al. Reliability of Wearable-Sensor-Derived Measures of Physical Activity in Wheelchair-Dependent Spinal Cord Injured Patients . Front Neurol . 2018 ; 9 . doi: 10.3389/fneur.2018.01039 OpenUrl CrossRef 22. ↵ van der Slikke RMA , Berger MAM , Bregman DJJ , Veeger HEJ . From big data to rich data: The key features of athlete wheelchair mobility performance . J Biomech . 2016 ; 49 : 3340 – 3346 . doi: 10.1016/j.jbiomech.2016.08.022 OpenUrl CrossRef PubMed 23. van der Slikke RMA , Berger MAM , Bregman DJJ , Veeger DHEJ . Wearable wheelchair mobility performance measurement in basketball, rugby, and tennis: Lessons for classification and training . Sensors (Switzerland ). 2020 ; 20 : 1 – 13 . doi: 10.3390/s20123518 OpenUrl CrossRef 24. van der Slikke RMA , Berger MAM , Bregman DJJ , Lagerberg AH , Veeger HEJ . Opportunities for measuring wheelchair kinematics in match settings; reliability of a three inertial sensor configuration . J Biomech . 2015 ; 48 : 3398 – 3405 . doi: 10.1016/j.jbiomech.2015.06.001 OpenUrl CrossRef PubMed 25. ↵ Rietveld T , Vegter RJK , van der Slikke RMA , Hoekstra AE , van der Woude LHV , De Groot S . Wheelchair mobility performance of elite wheelchair tennis players during four field tests: Inter-trial reliability and construct validity . PLoS One . 2019 ; 14 . doi: 10.1371/journal.pone.0217514 OpenUrl CrossRef 26. ↵ Baroudi L , Yan X , Newman MW , Barton K , Cain SM , Shorter KA . Investigating walking speed variability of young adults in the real world . Gait Posture . 2022 ; 98 : 69 – 77 . doi: 10.1016/j.gaitpost.2022.08.012 OpenUrl CrossRef PubMed 27. ↵ Schneider S , Popp WL , Brogioli M , Albisser U , Demkó L , Debecker I , et al. Reliability of Wearable-Sensor-Derived Measures of Physical Activity in Wheelchair-Dependent Spinal Cord Injured Patients . Front Neurol . 2018 ; 9 . doi: 10.3389/fneur.2018.01039 OpenUrl CrossRef 28. ↵ Shabani S , Bourke AK , Muaremi A , Praestgaard J , O’Keeffe K , Argent R , et al. An Automatic Foot and Shank IMU Synchronization Algorithm: Proof-of-concept. 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) . IEEE ; 2022 . pp. 4210–4213. doi: 10.1109/EMBC48229.2022.9871162 OpenUrl CrossRef 29. Rietveld T , Vegter RJK , van der Slikke RMA , Hoekstra AE , van der Woude LH V. , de Groot S . Wheelchair mobility performance of elite wheelchair tennis players during four field tests: Inter-trial reliability and construct validity . PLoS One . 2019 ; 14 : e0217514 . doi: 10.1371/journal.pone.0217514 OpenUrl CrossRef 30. ↵ Ando T , Kitani T. A Time Alignment Method for Multiple Sensing Systems with GNSS Timing and IMUs with Frame-Sync Input. 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) . IEEE ; 2020 . pp. 1–6. doi: 10.1109/PerComWorkshops48775.2020.9156099 OpenUrl CrossRef 31. ↵ Madgwick SOH , Harrison AJL , Vaidyanathan R. Estimation of IMU and MARG orientation using a gradient descent algorithm. 2011 IEEE International Conference on Rehabilitation Robotics . IEEE ; 2011 . pp. 1–7. doi: 10.1109/ICORR.2011.5975346 OpenUrl CrossRef PubMed 32. ↵ Cain SM , McGinnis RS , Davidson SP , Vitali R V. , Perkins NC , McLean SG . Quantifying performance and effects of load carriage during a challenging balancing task using an array of wireless inertial sensors . Gait Posture . 2016 ; 43 : 65 – 69 . doi: 10.1016/j.gaitpost.2015.10.022 OpenUrl CrossRef PubMed 33. ↵ Barry G , Galna B , Lord S , Rochester L , Godfrey A . Defining ambulatory bouts in free-living activity: Impact of brief stationary periods on bout metrics . Gait Posture . 2015 ; 42 : 594 – 597 . doi: 10.1016/j.gaitpost.2015.07.062 OpenUrl CrossRef PubMed 34. ↵ Hiremath S V. , Ding D , Cooper RA . Development and evaluation of a gyroscopebased wheel rotation monitor for manual wheelchair users . Journal of Spinal Cord Medicine . 2013 ; 36 : 347 – 356 . doi: 10.1179/2045772313Y.0000000113 OpenUrl CrossRef PubMed 35. ↵ Koontz AM , Roche BM , Collinger JL , Cooper RA , Boninger ML . Manual Wheelchair Propulsion Patterns on Natural Surfaces During Start-Up Propulsion . Arch Phys Med Rehabil . 2009 ; 90 : 1916 – 1923 . doi: 10.1016/j.apmr.2009.05.022 OpenUrl CrossRef PubMed 36. ↵ Koontz AM , Cooper RA , Boninger ML , Souza AL , Fay BT . Shoulder kinematics and kinetics during two speeds of wheelchair propulsion . J Rehabil Res Dev . 37. ↵ Koontz AM , Cooper RA , Boninger ML , Yang Y , Impink BG , Van Der Woude LHV . A kinetic analysis of manual wheelchair propulsion during start-up on select indoor and outdoor surfaces . J Rehabil Res Dev . 2005 ; 42 : 447 – 458 . doi: 10.1682/JRRD.2004.08.0106 OpenUrl CrossRef PubMed Web of Science 38. ↵ Morrow MMB , Hurd WJ , Kaufman KR , An K-N . Shoulder demands in manual wheelchair users across a spectrum of activities . Journal of Electromyography and Kinesiology . 2010 ; 20 : 61 – 67 . doi: 10.1016/j.jelekin.2009.02.001 OpenUrl CrossRef PubMed Web of Science View the discussion thread. Back to top Previous Next Posted April 10, 2025. Download PDF Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following An inertial sensor-based comprehensive analysis of manual wheelchair user mobility during daily life in people with SCI Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share An inertial sensor-based comprehensive analysis of manual wheelchair user mobility during daily life in people with SCI Kathylee Pinnock Branford , Meegan G. Van Straaten , Omid Jahanian , Melissa M. B. Morrow , Stephen M. Cain bioRxiv 2025.04.02.646948; doi: https://doi.org/10.1101/2025.04.02.646948 Share This Article: Copy Citation Tools An inertial sensor-based comprehensive analysis of manual wheelchair user mobility during daily life in people with SCI Kathylee Pinnock Branford , Meegan G. Van Straaten , Omid Jahanian , Melissa M. B. Morrow , Stephen M. Cain bioRxiv 2025.04.02.646948; doi: https://doi.org/10.1101/2025.04.02.646948 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Bioengineering Subject Areas All Articles Animal Behavior and Cognition (7635) Biochemistry (17690) Bioengineering (13892) Bioinformatics (41936) Biophysics (21451) Cancer Biology (18588) Cell Biology (25499) Clinical Trials (138) Developmental Biology (13378) Ecology (19899) Epidemiology (2067) Evolutionary Biology (24320) Genetics (15609) Genomics (22506) Immunology (17736) Microbiology (40394) Molecular Biology (17181) Neuroscience (88603) Paleontology (666) Pathology (2832) Pharmacology and Toxicology (4824) Physiology (7641) Plant Biology (15152) Scientific Communication and Education (2045) Synthetic Biology (4294) Systems Biology (9825) Zoology (2271)