Evaluating Acceptability and Minimal Sensor Configuration for Home-Based Monitoring of Upper Extremity Use After Stroke

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Modell, Matheus M. Pacheco, Arve Opheim, Ann Marie Hestetun-Mandrup, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8604323/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 13 You are reading this latest preprint version Abstract Background : Home rehabilitation for stroke survivors is crucial to promote upper extremity (UE) use and improvement of functional abilities, enhancing independence and quality of life. Wearable sensors enable monitoring of movement trends and progress during home rehabilitation. The feasibility of using sensors in the home of stroke survivors depends both on survivors’ acceptability and on sensors’ accuracy in capturing the movement. The aim of this study was to identify a feasible sensor setup for monitoring stroke survivors’ UE movements during daily activities at home by evaluating 1) survivors’ subjective acceptability of wearing sensors, and 2) the number of sensors necessary to accurately capture the complexity of behavioral data. Methods : Eleven chronic stroke survivors were observed in a natural or simulated home environment, while attempting to attach/detach sensors and wearing them doing self-chosen Activities of Daily Living. Nine inertial measurement unit sensors were placed on participants’ UE and sternum. Acceptability was assessed with a custom-made questionnaire consisting of 14 items scored on a 1–5 Likert scale, and five open-ended questions. Further, information entropy was calculated to determine the minimal number of sensors needed to capture movement complexity. Results : Total acceptability of wearing sensors was high, with a median score of 57 out of 70 (81%; IQR = 11), while usability was moderate, with a median score of 11 out of 20 (55%; IQR = 5.5). Lower usability was also reported in the open-ended responses and observed when participants attempted attaching and detaching sensors. The minimal sensor setup to capture behavior complexity consisted of three-to-four sensors, placed on the sternum, non-affected and affected forearms, with or without affected upper arm. Conclusions : The sensors were found to have high acceptability, although usability challenges remained. The proposed three-to-four sensor setup was sufficient to maintain accuracy of the sensors, while potentially increasing stroke survivors’ acceptability and usability. In turn, this may enhance feasibility and improve stroke survivors’ adherence to wearing sensors at home. Rehabilitation Home rehabilitation Stroke Upper extremity Wearable sensors Feasibility Acceptability Entropy Figures Figure 1 Figure 2 Background Stroke is a leading cause of disability globally [ 1 ], resulting in an estimated global cost of US $ 891 billion in 2017 [ 2 ]. Approximately 50–80% of survivors experience motor impairments in the upper extremities (UEs) after stroke [ 3 ]. These impairments are associated with reduced independency in activities of daily living (ADLs) and a lower health-related quality of life [ 4 ]. Motor rehabilitation after stroke plays a crucial role in promoting improvements of motor functions and reducing the long-term burden of stroke [ 5 , 6 ]. Motor rehabilitation is increasingly being conducted directly in the stroke survivors’ home to meet their needs, reduce the associated cost, and as a direct result of the widespread early hospital discharge [ 7 , 8 ]. Repetitive, task-specific, and varied movements with the UEs are necessary to promote neuroplastic changes that lead to improvements in motor functions and independence in daily activities [ 7 , 9 ]. One of the challenges for home-based motor rehabilitation is the systematic monitoring of stroke survivors’ movements with their affected UEs. The monitoring is essential for tracking the use and improvements of the UE and fine-tuning the rehabilitation plan. It is not only important to assess movements of the affected UE, but also those of the non-affected UE. This is necessary to evaluate the issue of learned non-use [ 10 ], in addition to the trunk movement to identify the presence of compensatory movements [ 11 ]. Wearable sensors can offer a practical strategy for measuring UE movements of stroke survivors in their home [ 12 ]. Inertial measurement unit (IMU), which consists of accelerometer, gyroscope and magnetometer, is the most commonly used wearable sensor for capturing movement of body segments [ 13 ]. IMUs can provide valuable data about the quantity and quality of movements in the affected UE, non-affected UE, as well as the trunk [ 11 ]. The simplest biomechanical model to capture these movements includes the tracking of seven segments (upper arm, forearm, and hand of each extremity, plus the trunk), which requires the use of nine sensors (on each upper arm, forearm, hand, and shoulder, plus the sternum). The use of nine sensors on the stroke survivor’s upper body raises the question of feasibility, as it is likely to demand significant time, cost and personnel, potentially reducing adherence among stroke survivors. It is critical to consider stroke survivors’ perspectives on this issue, as the implementation of sensors in rehabilitation relies not only on measurement accuracy but also on the user’s acceptability. Acceptability refers to “the perception among implementation stakeholders that a given treatment, service, practice, or innovation is agreeable, palatable, or satisfactory” [ 14 , p. 67] and has long been regarded as a key factor in the implementation of new technology [ 15 ]. However, it remains relatively under-prioritized within wearable sensor research [ 16 ]. Acceptability includes subthemes such as usability, wearability, psychological comfort, and willingness to wear sensors. Usability refers to the ease, intuitiveness, and practicality with which a user can use a wearable sensor to achieve its intended purpose [ 17 ], whereas wearability refers to the comfort, fit, and unobtrusiveness of the sensor when it is worn on the body over time [ 18 ]. Wearable sensors, despite being accurate, cannot represent a viable solution if stroke survivors are resistant to wearing sensors while conducting daily life activities in their home. Therefore, including stroke survivors’ perspectives on this issue aligns with the need to move towards sustainable and person-centered care [ 19 ]. Reducing the number of sensors could potentially facilitate stroke survivors’ acceptability of using sensors at home. The reduction of sensor number should be carefully calibrated to ensure the selected sensors still capture the complexity of behavioral data. A potential measure to capture the information content of a physical system is information entropy. Derived from information theory [ 20 ] this is a measure of how much information is needed to describe a system’s set of probable states [ 21 ]. When applied to multisensor systems, information entropy can be used to characterize the number of observable states and their associated probabilities. By iteratively reducing the sensor set, changes in entropy can be quantified, providing a measure of information loss as sensors are removed. This process has been successfully used to evaluate how different sensor setups capture behavioral complexity in stroke survivors’ UE movements [ 22 ]. The aim of this study was to identify a feasible sensor setup for monitoring stroke survivors’ UE movements during daily activities at home. This was achieved through the evaluation of stroke survivors’ subjective acceptability of wearing sensors in a home-like environment and to evaluate the number of sensors necessary to accurately capture the complexity of behavioral data. Methods Participants A total of eleven (n = 11) stroke survivors in the chronic phase (more than 1 year since stroke) were recruited for this study. To be eligible for the study, individuals had physical impairments in the UE as a consequence of stroke, were above 18 years of age, able to comprehend information and instructions given to them, and provide informed consent. Potential participants were contacted at a rehabilitation hospital while participating in an intensive UE training program, or at the hospital outpatient clinic. They were provided with a sheet of general information about the study, emphasizing that their participation was voluntary and they had the possibility to withdraw from the study at any point without providing a reason and without any consequence. Upon confirmation that they understood the purpose and procedure of the study; written informed consent was obtained from all the individuals invited to participate. Procedure The procedure involved participants wearing nine IMU sensors on their upper body while performing ADLs of their choice for one hour in a simulated home environment at a rehabilitation hospital or natural home setting. The first three participants wore sensors in the simulated home-like environment to ensure safety and feasibility of the procedure. The home-like environment included a living room, kitchen, and bathroom. For the remaining eight participants, data collection took place in their own home to increase ecological validity. Data collection took place in Norway between March and April 2025. After the explanation of the procedure, participants were invited to attach nine sensors on their body independently. Three sensors were placed on each arm, one on each shoulder, and one on the sternum (see the materials section). They were instructed on how and where to place the sensors on the different body segments to ensure a correct and non-irritating attachment sequence. The researcher provided manual help when the participants requested it or when they were not able to attach a sensor independently. The number of sensors attached independently and the total time for attaching them was recorded for each participant, consistent with previously published procedures [ 23 , 24 ]. The times were noted in an observation sheet, along with whether participants attached the sensors independently or required any assistance from the researcher. At the end of this sequence, a researcher ensured the correct placement of all sensors and the recording began. As soon as the recording started, participants were instructed to do self-chosen ADLs for one hour. The number and type of self-chosen activities varied largely across participants. Some performed minimal physical activity and spent most of the time conversating with the researcher. Others were more physically active and engaged in activities like preparing and eating a meal, reading, using their phone, and brewing coffee. A few participants performed exercises while wearing sensors (e.g., gait training, sit-to-stand, and exercise games). After one hour of recording, the sensors were turned off, and participants were invited to independently detach all sensors. The participants received advice and guidance during the detachment process and received help if needed. Similar to the attaching procedure, the detachment time, feedback, and assistance was noted in the observation form. Lastly, participants were invited to answer a questionnaire about their acceptability of wearing sensors (Supplementary Table S1 ). A brief introduction was given to them about its purpose and how it should be filled in. The questionnaire was administered like an interview, where the researcher read the questions aloud, while giving the participants the possibility to look at the questions themselves. This approach ensured that the participants understood all items. Materials Sensors The Xsens MTw Awinda IMUs (Xsens Technology B.V., Enschede, The Netherlands), which have been validated for accuracy [ 25 ], were used in this study. The nine IMU sensors were placed as follows: one on the sternum (Thoracic vertebra 8 as level reference), one on each shoulder (scapular spine), one on each upper arm (lateral mid-humerus), one on each forearm (dorsal mid-ulnaris) and one on each hand (dorsal side). The sensors on the sternum and shoulders were attached using the shirt provided by Xsens. For the participants that could not wear the Xsens shirt, the sensor on the sternum was attached using a Velcro strap around the torso and a double-sided tape. Medical tape was used to attach the sensors on the shoulders. The sensors on the arms were attached using Velcro straps and the gloves provided by Xsens for the hands. All sensors were placed according to recommendations and previous studies [ 26 , 27 , 28 , 29 , 30 ]. Wireless transmission from the Xsens sensors allowed real-time recording on a laptop via the MT Manager Software Suite 2022.2. Acceptability questionnaire The acceptability data were collected using a custom-made acceptability questionnaire, informed by a prior pilot study. The pilot study involved two stroke survivors and investigate the suitability of two existing acceptability questionnaires: the system usability scale [ 31 ] and the questionnaire by Auepanwiriyakul et al. [ 32 ]. The pilot participants’ feedback and researchers’ experience highlighted that these questionnaires contained a mix of positive and negative statements, which caused interpretation issues. In addition, some items were not directly relevant to participants’ experience of wearing the sensors, while others were redundant. Therefore, a custom-made questionnaire was developed to improve clarity and relevance, and to avoid redundancy. The custom-made questionnaire was based on both existing questionnaires, retaining relevant items, merging similar items, and rephrasing them to ensure consistent and understandable wording. The final custom-made acceptability questionnaire was comprised of 14 items, covering four subthemes: usability (items 1, 2, 8 & 12), wearability (items 3–6), psychological comfort (items 7, 9, 10 & 11), and willingness to wear sensors (items 13–14). All items were positive statements. Each item was scored on a 1–5 Likert scale: 1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, and 5 = strongly agree. A higher score represented higher perceived acceptability. Internal consistency of the custom-made questionnaire was assessed to evaluate how consistently the items measure the same underlying construct. Further, 5 open-ended questions were included to explore the participants’ view at a deeper level. The participants could freely choose between the English or Norwegian version of the questionnaire. Both versions were reviewed and revised by three individuals fluent in both languages. Data analysis Acceptability and attaching/detaching time of sensors Item scores were summed for each participant to generate a total acceptability score and single scores for the different subthemes (usability, wearability, psychological comfort, and willingness to wear sensors). As the questionnaire consisted of ordinal Likert data, non-parametric statistics (median [Mdn] and interquartile range [IQR]) were computed for the total scores and subtheme scores across participants [ 33 ]. The different subtheme scores were transformed into percentages of their possible ranges to facilitate interpretation. Scores were classified as low (0–33%), moderate (34–66%), and high (67–100%). Mean (M) and standard deviation (SD) were computed for attaching and detaching times. The acceptability data and attaching/detaching times were analyzed using Excel (Microsoft Excel for Microsoft 365 MSO, Version 2503, Redmond, WA, USA) and the Jamovi Project (2025) jamovi (Version 2.6) [Computer Software]. Retrieved from https://www.jamovi.org . Internal consistency of the custom-made questionnaire Internal consistency was assessed using McDonald’s omega (ω). The items measure different constructs (usability, wearability, psychological comfort, and willingness to wear sensors) and McDonald’s omega provides an accurate estimate of how consistently the items measure the same underlying construct. The omega values were defined as very high (≥ 0.9), high (0.80–0.89), acceptable (0.70–0.79), minimal (0.60–0.69) and unacceptable (< 0.60) [ 34 ]. Calculating the minimum number of sensors required The data from each IMU sensor were extracted using the MT Manager 2025.2.0 (Xsens Technology B.V., Enschede, The Netherlands) and processed through a Matlab 2024b (24.2.0.2806996, MathWorks Inc. Natick, MA) script, developed specifically for the current purposes. First, we cleaned the dataset from outliers by determining the M and SD of the three dimensions of acceleration and eliminating time points that were outside (in absolute terms) the M plus-minus six SDs. The data were then filtered by a 4th order 5Hz low pass Butterworth filter. As some sensors had occlusions, only the time periods with data from all sensors were considered. Given some variation in the exact time of data collection for each participant, we decided to use the first 50 minutes of data collection for the analysis. To identify the minimum number of sensors, we performed the following procedure. First, we identified redundant sensors by measuring the mutual information between pairs of sensors (the calculation of mutual information is explained below). From this, we defined a sequential list of sensors that could be potentially removed. Second, using information entropy and participants’ feedback, we calculated a time series of participants’ behavior for each setup of sensors, removing iteratively the redundant sensors. Lastly, we calculated the similarity by using the Cronbach’s α between the full sensor setup and the setups with removed sensors based on participants’ choice and our redundancy analyses. To calculate the entropy, we performed the procedure described in Williams [ 21 ]. Information entropy is formalized as the negative sum of the product of the probability of occurrence of the bins multiplied by the logarithm of base 2 of the same probability of occurrence of the bins ( \(\:-\sum\:\left(p*{\text{log}}_{2}p\right)\) ). In this way, entropy is a measure of how many bits of information are necessary to capture the behavior of a given system. Mutual information, relatedly, captures how much knowing about one system’s behavior (in this case, one sensor) tell us (“informs”) about another system’s behavior (in this case, a second sensor). Formally, \(\:{I}_{s1;s2}={H}_{s1}-{H}_{\left(s1|s2\right)}\) , mutual information of sensor 1 (I s1;s2 ) is the entropy of sensor 1 ( H s1 ) minus the entropy of sensor 1 given the occurrences of sensor 2 ( H s1|s2 ) (conditional entropy); i.e., how much information sensor 2 carries about sensor 1. Given both measures rely on state probabilities, we binned the data into fixed segments (depending only on whether the data referred to acceleration or orientation of the sensor). The bins for acceleration were larger for large values and vice-versa: (only showing the positive values) for values from 0 m/s 2 to 0.9 m/s 2 , the bins were of 0.05 m/s 2 ; for values from 0.9 m/s 2 to 1.5 m/s 2 , the bins were of 0.1 m/s 2 ; for values from 1.5 m/s 2 to 5 m/s 2 , the bins were of 0.5 m/s 2 ; and for values from 5 m/s 2 to 12 m/s 2 , the bins were of 1.0 m/s 2 . The bins for orientation were always of 5º considering the whole range of -180º to 180º. Then, for each time point, a series of six numbers (representing the bins of three accelerations and three orientations) was created and the frequency of these were computed. For our redundancy analyses, we used the whole data set (50 minutes) to quantify the mutual information between sensors. That is, we calculated the entropy per sensor and the conditional entropy per pair of sensors. The mutual information was normalized by dividing the mutual information by the information entropy of the sensor making the values range between 0 and 1. To create the entropy time series (which would later allow for an analysis with Cronbach’s α between setups of sensors), we created blocks of two minutes of data considering the data of all sensors included in the given set (see below) and calculated the information entropy. Information entropy was computed for the full sensor setup as well as for other setups with a reduced number of sensors. The reduced sensor setups were created considering the redundancy analyses and the participants’ feedback. Finally, to check how many sensors were necessary to guarantee the information gathered from all nine sensors, we computed, for each participant, the Cronbach’s α between the entropy from the full sensor setup and the entropy from each alternative sensor setup. Values larger than 0.9 would indicate sufficient similarity to use the alternative setup – the information from the full setup was still observed in the smaller one. Results Acceptability and attaching/detaching time The total acceptability score for wearing all sensors was high (Mdn = 57 out of 70 [81%], IQR = 11) with observed scores ranging from 28 to 68. Subtheme scores were as follows: wearability was high (Mdn = 13 out of 15 [87%], IQR = 3.5); psychological comfort was high (Mdn = 14 out of 15 [93%], IQR = 3.5); willingness to wear the sensors continuously was high (Mdn = 8 out of 10 [80%], IQR = 4.5); and usability was moderate (Mdn = 11 out of 20 [55%], IQR = 5.5). The distribution of answers on the various closed-ended items is shown in Table 1 . Table 1 Responses to individual questionnaire closed-ended items (n = 11). ITEM Mdn (IQR) Strongly disagree 1 (%) Disagree 2 (%) Neutral 3 (%) Agree 4 (%) Strongly agree 5 (%) USABILITY ITEMS 1. The sensors were easy to put on and take off 1 (1.5) 55 18 9 18 0 2. I would be able to use the sensors on my own 2 (2.5) 46 18 9 9 18 8. I found the design of the sensors to be practical 4 (1.5) 0 9 18 27 46 12. I would find it easy to integrate the sensors into my daily life 3 (1.5) 18 27 27 9 18 WEARABILITY ITEMS 3. Wearing the sensors did not limit my ability to move or perform physical activities 5 (0.5) 9 0 0 18 73 4. The sensors did not interfere with washing or going to the toilet 5 (3) 27 0 9 0 64 5. The sensors were comfortable to wear 5 (1) 9 0 9 27 55 6. I did not experience any itchiness or skin irritations while wearing the sensors 5 (0) 0 9 0 0 91 PSYCHOLOGICAL COMFORT ITEMS 7. I found the appearance of the sensors to be appealing 5 (1.5) 9 0 18 9 64 9. I would feel comfortable wearing the sensors even if they were visible to family members/ close friends 5 (1) 0 9 9 18 64 10. I would feel comfortable wearing the sensors even if they were visible to acquaintances 5 (1.5) 9 0 18 9 64 11. I did not feel anxious while wearing the sensors 5 (0) 0 0 0 0 100 WILLINGNESS TO WEAR ITEMS 13. I would be willing to wear the sensors continuously for long term use if beneficial for my rehabilitation 4 (2.5) 0 27 9 18 46 14. I would be willing to wear the sensors in my daily life if recommended by a therapist 4 (2) 0 27 0 36 36 Median and interquartile range are shown for each item, along with the percentage of participants choosing each response category (1 = strongly disagree to 5 = strongly agree). Only two participants were able to attach all sensors themselves, with a mean attaching time of 241 s (SD = 85). Of the remaining nine participants, seven were able to independently attach the sensors on the affected UE, while only one managed to attach the sensors on the non-affected UE themselves. Only three participants detached all sensors independently, with a mean detaching time of 50 s (SD = 10). Of the remaining eight participants, seven managed to detach the sensors on the affected UE themselves, while only one participant independently detached the sensors on the non-affected UE. Descriptive statistics of attachment and detachment times are shown in Supplementary Tables S2 and S3. The open-ended responses showed that some participants liked the purpose of wearing the sensors in home rehabilitation (n = 4), while others were neutral (n = 5). There were contrasting perceptions of wearability. Seven participants provided positive remarks related to comfort, size, and design, whereas eight expressed negative views about wearing the full setup of sensors for longer periods. Further, eight participants disliked the attaching of sensors, with one noting: “it is impossible with this many sensors”. Although most of the participants considered all the sensors acceptable, the hand sensors received the most negative comments (n = 5). The distribution of themes to the different items, along with detailed descriptions, is provided in Supplementary Table S4. In summary, the overall acceptability of wearing the full sensor setup was high, but usability was moderate. This was reflected in the general inability of participants to attach all the sensors independently and the high attaching and detaching times, as well as in the open-ended responses, which indicated that the number of sensors was too high, with the sensors on the hand restricting movements. Internal consistency of the custom-made questionnaire The internal consistency of the acceptability items was very high (ω = 0.913), after item 11 (“I did not feel anxious”) was excluded due to lack of variance. The internal consistency of the wearability items was acceptable (ω = 0.740). Item 6 (“I did not experience itchiness or skin irritations”) was found to have a low item-total correlation (r = 0.033) with the rest of the wearability items, and when excluded, the internal consistency was found to be high (ω = 0.852). The psychological comfort items were tested for internal consistency, but item 11 was excluded due to lack of variance. The internal consistency of the remaining items was found to be acceptable (ω = 0.768). The internal consistency of items regarding willingness to integrate the sensors into daily life was very high (ω = 0.970). The internal consistency of the usability items was high (ω = 0.856). A correlation heatmap of all the items in the custom-made acceptability questionnaire is shown in Supplementary Fig. S1 . Number of sensors The participants demonstrated qualitative differences in both the amount and type of movement while wearing the sensors. While some participants mostly remained seated and talked to the researchers, others played a videogame or prepared and ate a meal. This variability affects the potential number of sensors required as more elaborate movements (with differential motion of each segment) requires more sensors to be captured and vice-versa. Figure 1 shows an example of the redundancy network for two different participants, illustrating how a given sensor “informs” about others. It is observed that, for some cases, a given sensor is highly related, and thus “informative”, about others (sternum for participant 4) while, in other cases, the sensors are clustered and differentiated in terms of information (participant 5). The participants exemplify an active person (participant 5) and a more sedentary person (participant 4). Table 2 shows the average normalized mutual information values between sensors considering all participants. The highest values were for the shoulder sensors between each other (0.11), both shoulders with the sternum (non-affected: 0.08; affected: 0.09), affected hand and affected forearm (0.08), non-affected hand and non-affected forearm (0.09). Table 2 Averaged normalized mutual information between sensors. Sensors ST NaH NaFA NaUA NaSh AH AFA AUA NaH 0.02 NaFA 0.02 0.09 NaUA 0.03 0.04 0.07 NaSh 0.08 0.02 0.03 0.04 AH 0.03 0.02 0.02 0.02 0.03 AFA 0.03 0.02 0.02 0.02 0.03 0.08 AUA 0.05 0.02 0.02 0.03 0.04 0.05 0.06 Ash 0.09 0.02 0.02 0.03 0.11 0.03 0.03 0.07 Legend: ST: Sternum; Na: Non-affected; A: Affected; H: Hand; FA: Forearm; UA: Upper Arm; Sh: Shoulder Considering these results and participants’ feedback, the sensors on the shoulders, on the hands, and on the forearms are candidates to be removed. Accordingly, we derived the following sensor setups (1) the full setup of sensors (nine sensors); (2) Without Shoulders setup (WSh): sternum, non-affected and affected hands, forearms, upper arms (seven sensors); (3) Without Hands setup (WH): sternum, non-affected and affected forearms, upper arms (five sensors); (4) Without Non-affected Upper Arm setup (WNaUA): sternum, non-affected and affected forearms, affected upper arm (four sensors); (5) Without Affected Upper Arm setup (WAUA): sternum, non-affected and affected forearms (three sensors); (6) Without Sternum setup (WSt): non-affected and affected forearms (two sensors); (7) Non-affected Forearm setup (NaFA): non-affected forearm (one sensor); (8) Affected Forearm setup (AFA): affected forearm (one sensor). Most setups also preserved the need to track trunk movements as a common source of compensations [ 35 ]. Additionally, we emphasized the affected limb as this would be the object for movement analysis and tracking. The Cronbach’s α of the alternative setups in relation to the full sensor setup showed that the number of sensors required to capture behavioral complexity differed across participants (Fig. 2 ). The WNaUA setup with four sensors (sternum, non-affected and affected forearms, affected upper arm) was necessary for the most active participant (P5 in Fig. 2 ) while the WAUA setup (sternum, non-affected and affected forearms) was enough for the other participants. Discussion The aim of this study was to identify a feasible sensor setup for monitoring stroke survivors’ UE movements during daily activities at home. This was achieved through the evaluation of stroke survivors’ acceptability of wearing sensors in a home or home-like environment and the number of sensors necessary to accurately capture the complexity of behavioral data. The first finding, based on the quantitative and qualitative acceptability results, was that although the full sensor setup had high acceptability among participants, usability was repeatedly shown to be low-to-moderate. Participants could not attach all these sensors on their own and did not like the high number of sensors. In short, a nine-sensor setup in stroke survivors’ home-like environment was not feasible in our selected sample. The second finding, based on the entropy results, was that three-to-four sensors were sufficient to accurately capture the complexity of behavioral data. These sensor setups can increase the feasibility of monitoring daily living movements of stroke survivors in the home environment. Comparison with existing literature This study aligns with previous research [ 32 , 36 , 37 , 38 ] showing stroke survivors’ high acceptability but moderate usability in wearing sensors during ADLs. Similar to our findings, these previous studies reported that participants valued the purpose of wearing sensors to promote use and function, but had mixed opinions regarding wearability and usability, including preferences for smaller sensors. Consistent with our findings, Bishop et al. [ 23 ] found that most participants were unable to attach sensors to their non-affected upper extremity, highlighting usability challenges especially on the non-affected side. This reflects that participants had a real-life experience of attaching and detaching the sensors independently, highlighting practical challenges that might not be captured in studies where sensors are attached by researchers. Together, these findings suggest that while stroke survivors generally appreciate the potential benefits of wearable sensors, practical aspects such as amount, size, placement, and ease of handling remain important factors for improving usability. A previous study employing similar procedures has shown that four IMU sensors were enough to capture the behavior of stroke survivors when performing 30 upper-limb tasks (including simple gestures, reaching, and manipulating objects) [ 22 ]. This was done by using an initial setup of 10 sensors, considering tasks performed with affected and non-affected limbs separately, and considering only accelerations (derived from the accelerometer in the IMU sensors). While the resultant number of sensors were equal to what we observed here, the specific setup of sensors was dependent on the performing limb (prioritizing affected or non-affected segments) and the setup of sensors were different: they included the trunk (similar to the sternum measure used here), upper arm, forearm and hand of the acting arm. Thus, it might be that if stroke survivors are asked to perform more elaborate unimanual tasks (as analyzed in Pacheco et al. [ 22 ]) or are more active in their daily activities (shown in the example by Participant 5), more sensors might be required. Practical implications While a nine-sensor IMU setup would be ideal for a comprehensive capture of daily movements in stroke survivors at home, such a setup is not only burdensome for the stroke survivor, their family, and the health care system, but also likely to be unfeasible. Low feasibility would in turn reduce adherence, meaning that even highly accurate sensor setups become ineffective if they cannot be implemented into stroke survivors’ daily lives. Designing sensor setups for capturing UE movements involves a careful balance between the number of sensors, acceptability among stroke survivors, and the amount of movement data. For example, trunk movements could be measured with three sensors (one on each shoulder and one on the sternum), but a single sternum sensor is sufficient to capture stroke survivors’ compensatory trunk movements. Similarly, the hand sensors were reported to be uncomfortable for several participants, but these are not essential as long as the forearm sensors are maintained; the same applies to the upper arm sensors. The remaining two forearm sensors would still allow accurate measurements of quantity and quality of movements in each UE, as well as use in affected UE versus non-affected UE. These trade-offs offer concrete ways to simplify the sensor setup, maintain measurement accuracy, increase acceptability among stroke survivors, and make it feasible for home-based stroke rehabilitation. This sensor setup offers practitioners a practical solution for tracking stroke survivors’ daily movements – a crucial measure for evaluating progress and tailoring home-based rehabilitation. Strengths and limitations This is one of the first studies to investigate the feasibility of using wearable sensors to capture stroke survivors’ movement in a home or home-like environment, combining end-users’ acceptability and movement-derived data. This approach ensured ecological validity by allowing participants to wear the sensors in their home or in a home-like environment. Furthermore, a custom-made acceptability questionnaire was developed to accurately assess stroke survivors’ acceptability of wearing sensors in their home. The limitations of this study included a small sample size of eleven participants with no recorded impairment level in their UE. The measurement time was limited to one hour, which may not reflect full-day usage of the sensors in the home environment. Accuracy assessments were limited to entropy measures, and future studies should include kinematics. Future directions Future studies should investigate the proposed sensor setup in a larger sample over an extended measurement period in the home environment to verify its accuracy and to re-evaluate acceptability using the revised custom-made questionnaire, allowing direct comparison with the present study. On a broader level, future work should continue to identify ways to improve the feasibility of wearable sensor technology in stroke survivors’ home environments. To enhance usability and acceptability, one potential direction is the development of sensors made by nanotechnology and to embed them into everyday clothing. Such an approach may increase stroke survivors’ comfort and flexibility, as well as making the sensors appear more “normal” for users [ 39 , 40 ]. Further, easier attaching and detaching methods should be investigated to increase the usability, for instance: a sensor-integrated sleeve [ 41 ], or e-textiles [ 42 ]. Adherence among stroke survivors may be enhanced through engaging solutions, including smartphone applications or a smartwatches that encourage physical activity by delivering movement reminders, which have earlier been positively received by stroke survivors [ 36 , 38 ]. Individualized sensor setups could be valuable for future research. Our entropy results suggest that people with a larger quantity and more varied movements may need more sensors, while those with less or uniform movements may need fewer, potentially improving usability and acceptability. Previous studies also suggest that personalization may enhance feasibility, adherence, and meaningful data capture [ 11 ]. Conclusion This study showed that although a nine-sensor setup is ideal in terms of accuracy and had high acceptability among the participants, its usability was low to moderate. Entropy analysis indicated that using three-to-four sensors (both forearms, sternum, with or without affected upper arm) are sufficient to capture the complexity of behavioral data in stroke survivors in a home-like environment. Reducing the number of sensors would likely improve usability, thereby promoting acceptability and ultimately enhancing feasibility and adherence. Future studies should validate this sensor setup in a larger sample of stroke survivors with documented levels of impairment observed in a home environment and continue to explore strategies for further improving wearable sensor technology. Abbreviations ADLs Activities of Daily Living AFA Affected Forearm setup: one sensor – affected forearm IMU Inertial Measurement Unit IQR Interquartile Range M Mean Mdn Median n Sample size NAFA Non-affected Forearm setup: one sensor – non-affected forearm REK Regional Committee for Medical and Health Research Ethics SD Standard Deviation UE Upper Extremity WAUA Without Affected Upper Arm setup: three sensors – sternum, non-affected and affected forearms WH Without Hands setup: five sensors – sternum, non-affected and affected forearms, upper arms WNaUA Without Non-affected Upper Arm setup: four sensors – sternum, non-affected and affected forearms, affected upper arm WSh Without Shoulders setup: seven sensors – sternum, non-affected and affected hands, forearms, upper arms WSt Without Sternum setup: two sensors – non-affected and affected forearms Declarations Ethics approval and consent to participate The study was assessed by the regional committee for medical and health research ethics in South-Eastern Norway (REK), and was not classified as clinical research as the aim was not to gain new knowledge about health and disease, thus did not fall under the Law of Health Research in Norway (Lov om medisinsk og helsefaglig forskning (helseforskningsloven) - Lovdata), and did not require formal ethical approval from REK (Reg nr. 844958). All observations, field notes, and questionnaire responses were fully anonymized and treated confidentially throughout the research process. No identifiable personal information was collected, and all data were stored and presented in a way that ensured participants could not be recognized at any stage. All ethical principles were adhered to, and informed consent was obtained from all participants. Consent for publication The two photographed participants provided consent for publication. Availability of data and materials The datasets supporting the conclusions of this article are available in the Open Science Framework repository (https://osf.io/rnysa/overview?view_only=7352733640f54cf496ae1c716fce2710) and are included within the article and its additional file(s). Competing interests The authors declare no competing interests. Funding This study was conducted as part of the PEER-HOMEcare project, funded by the Norwegian Research Council (ID 350985), the Portuguese Foundation for Science and Technology (THCS/0002/2023), under the framework of the co-fund partnership of Transforming Health and Care Systems, THCS, (GA N° 101095654) of the EU Horizon Europe Research and Innovation Programme. The funding bodies had no involvement in any of the study stages. Authors' contributions All authors contributed to the conceptualization of the study; BSM collected the data; BSM and MMP analyzed the data; all authors interpreted the data; BSM, MMP, LO drafted the manuscript; all authors reviewed, edited, and approved the manuscript. All authors have read and agreed to the published version of the manuscript. Acknowledgements We would like to thank Tonje Hexeberg Marka and Dennis Elvegaard for their contribution in the data collection process. 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Identifying the type and number of sensors required to capture behavioral complexity in daily activities in chronic stroke patients. Gait Posture. 2025;121:174-5. Bishop L, Demers M, Rowe J, Zondervan D, Winstein CJ. A Novel, Wearable Inertial Measurement Unit for Stroke Survivors: Validity, Acceptability, and Usability. Arch. Phys. Med. Rehabil. 2024;105(6):1142-50. Lambelet C, Temiraliuly D, Siegenthaler M, Wirth M, Woolley DG, Lambercy O, et al. Characterization and wearability evaluation of a fully portable wrist exoskeleton for unsupervised training after stroke. J. Neuroeng. Rehabil. 2020;17(1). Robert-Lachaine X, Mecheri H, Larue C, Plamondon A. Validation of inertial measurement units with an optoelectronic system for whole-body motion analysis. Med. Biol. Eng. Comput. 2017;55(4):609-19. Held JPO, Klaassen B, Eenhoorn A, Beijnum B-JFV, Buurke JH, Veltink PH, et al. Inertial Sensor Measurements of Upper-Limb Kinematics in Stroke Patients in Clinic and Home Environment. Front Bioeng Biotechnol. 2018;6. Höglund G, Grip H, Öhberg F. The importance of inertial measurement unit placement in assessing upper limb motion. Med. Eng. Phys. 2021;92:1-9. Longhi M, Donati D, Mantovani M, Casarotti S, Calbucci L, Puglisi G, et al. Feasibility of Shoulder Kinematics Assessment Using Magnetic Inertial Measurement Units in Hemiplegic Patients after Stroke: A Pilot Study. Appl Sci. 2023;13(21):11900. Klaassen B, Van Beijnum B-J, Weusthof M, Hofs D, Van Meulen F, Droog E, et al. A Full Body Sensing System for Monitoring Stroke Patients in a Home Environment. Springer International Publishing; 2015. p. 378-93. Lorussi F, Carbonaro N, De Rossi D, Paradiso R, Veltink P, Tognetti A. Wearable Textile Platform for Assessing Stroke Patient Treatment in Daily Life Conditions. Front Bioeng Biotechnol. 2016;4. Brooke J. SUS: A 'Quick and Dirty' Usability Scale. Usability Evaluation In Industry1996. p. 207–12. Auepanwiriyakul C, Waibel S, Songa J, Bentley P, Faisal AA. Accuracy and Acceptability of Wearable Motion Tracking for Inpatient Monitoring Using Smartwatches. Sensors. 2020;20(24):7313. Jamieson S. Likert scales: how to (ab)use them. Med. Educ. 2004;38(12):1217-8. Cohen L, Manion L, Morrison K. Statistical significance, effect size and statistical power. Routledge; 2017. p. 739-52. Levin MF, Kleim JA, Wolf SL. What Do Motor “Recovery” and “Compensation” Mean in Patients Following Stroke? Neurorehabil. Neural Repair. 2009;23(4):313-9. Whitford M, Schearer E, Rowlett M. Effects of in home high dose accelerometer-based feedback on perceived and actual use in participants chronic post-stroke. Physiother. Theory Pract. 2020;36(7):799-809. Langerak AJ, Regterschot GRH, Evers M, Van Beijnum B-JF, Meskers CGM, Selles RW, et al. A Sensor-Based Feedback Device Stimulating Daily Life Upper Extremity Activity in Stroke Patients: A Feasibility Study. Sensors. 2023;23(13):5868. Toh SFM, Gonzalez PC, Fong KNK. Usability of a wearable device for home-based upper limb telerehabilitation in persons with stroke: A mixed-methods study. Digit Health. 2023;9:205520762311537. Wang Q, De Baets L, Timmermans A, Chen W, Giacolini L, Matheve T, et al. Motor Control Training for the Shoulder with Smart Garments. Sensors. 2017a;17(7):1687. Wang Q, Markopoulos P, Yu B, Chen W, Timmermans A. Interactive wearable systems for upper body rehabilitation: a systematic review. J. Neuroeng. Rehabil. 2017b;14(1). Ploderer B, Fong J, Withana A, Klaic M, Nair S, Crocher V, et al. ArmSleeve. 2016:700-11. Khan MA, Saibene M, Das R, Brunner I, Puthusserypady S. Emergence of flexible technology in developing advanced systems for post-stroke rehabilitation: a comprehensive review. J Neural Eng. 2021;18(6):061003. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Supplementary materials Supplementary table S1. The custom-made acceptability questionnaire Supplementary table S2. How much time each participant spent detaching the sensors Supplementary table S3. How much time each participant spent detaching the sensors Supplementary table S4. Distribution of answer themes on the open-ended items in the custom-made acceptability questionnaire Supplementary fig. S1. Correlation heatmap of all the items in the custom-made acceptability questionnaire Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 13 Apr, 2026 Reviews received at journal 13 Apr, 2026 Reviews received at journal 03 Apr, 2026 Reviews received at journal 10 Mar, 2026 Reviews received at journal 03 Mar, 2026 Reviewers agreed at journal 02 Mar, 2026 Reviewers agreed at journal 02 Mar, 2026 Reviewers agreed at journal 28 Feb, 2026 Reviewers agreed at journal 08 Feb, 2026 Reviewers invited by journal 03 Feb, 2026 Editor assigned by journal 15 Jan, 2026 Submission checks completed at journal 15 Jan, 2026 First submitted to journal 14 Jan, 2026 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8604323","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":585955239,"identity":"5de0f5ef-d6a5-494c-904b-a785375dd100","order_by":0,"name":"Børge S. Modell","email":"","orcid":"","institution":"Norwegian School of Sport Sciences","correspondingAuthor":false,"prefix":"","firstName":"Børge","middleName":"S.","lastName":"Modell","suffix":""},{"id":585955240,"identity":"4e19d13f-ed2e-4a7f-acd0-4bb782b6f14e","order_by":1,"name":"Matheus M. Pacheco","email":"","orcid":"","institution":"Universidade de São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Matheus","middleName":"M.","lastName":"Pacheco","suffix":""},{"id":585955241,"identity":"4644d8b5-f956-4300-97b7-041536319c42","order_by":2,"name":"Arve Opheim","email":"","orcid":"","institution":"Sunnaas sykehus","correspondingAuthor":false,"prefix":"","firstName":"Arve","middleName":"","lastName":"Opheim","suffix":""},{"id":585955242,"identity":"3f823416-738d-464b-b595-351aa67284e5","order_by":3,"name":"Ann Marie Hestetun-Mandrup","email":"","orcid":"","institution":"Sunnaas sykehus","correspondingAuthor":false,"prefix":"","firstName":"Ann","middleName":"Marie","lastName":"Hestetun-Mandrup","suffix":""},{"id":585955243,"identity":"ed8c06f5-4432-453a-8ff1-e9c87e7f4060","order_by":4,"name":"Marianne Løvstad","email":"","orcid":"","institution":"Sunnaas sykehus","correspondingAuthor":false,"prefix":"","firstName":"Marianne","middleName":"","lastName":"Løvstad","suffix":""},{"id":585955244,"identity":"f8a270e5-8ff7-4423-bb71-1edb48f9e2f6","order_by":5,"name":"Hege P. 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Rudd","email":"","orcid":"","institution":"Norwegian School of Sport Sciences","correspondingAuthor":false,"prefix":"","firstName":"James","middleName":"R.","lastName":"Rudd","suffix":""},{"id":585955247,"identity":"2bcd4a86-2e0b-4af4-acd8-3a2b1d067ca7","order_by":7,"name":"Luca Oppici","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYBACPhiDjYGx8QGYJQHEPHi0sCFpaTYgTQuILUGcFonkhx8YKu7k8bEfbqu6UXFHXn52A+ODt234tKQZSzCceVbMxpPYdjvnzDPDDXcOMBvOxaslh0GCse1wYhuQvJ3bdphxg0QCmzQvfi3MP2BaioFa7OfPSGD/TUALG9wWZqCWxIYbCWzMeLXwPDOzSID4pVk658zh5A03Epsl55zDrYWfPfnxjQ/AEJNvP/7wc07FYdv5M5IPfnhThlsLGCQwHEhA4jI2EFAPBihaRsEoGAWjYBSgAgBczFAX+gGLRgAAAABJRU5ErkJggg==","orcid":"","institution":"Norwegian School of Sport Sciences","correspondingAuthor":true,"prefix":"","firstName":"Luca","middleName":"","lastName":"Oppici","suffix":""}],"badges":[],"createdAt":"2026-01-14 18:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8604323/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8604323/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102207922,"identity":"8eaaf57f-5f18-418c-baed-d7ab35ecf311","added_by":"auto","created_at":"2026-02-09 12:07:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":102812,"visible":true,"origin":"","legend":"\u003cp\u003eExamples of redundancy networks for two exemplary participants. The circles refer to different sensors, and the yellow lines thickness relate to the amount of normalized mutual information between sensors.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8604323/v1/3cbc621cc8a07f789bcb4d53.png"},{"id":102207923,"identity":"b065a876-c07c-4f21-9823-1a112bc42e7a","added_by":"auto","created_at":"2026-02-09 12:07:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":124492,"visible":true,"origin":"","legend":"\u003cp\u003eCronbach’s \u003cem\u003eα\u003c/em\u003e across participants of the different sensor setups in relation to the full sensor setup. WSh (Without Shoulders setup): seven sensors – sternum, non-affected and affected hands, forearms, upper arms; WH (Without Hands setup): five sensors – sternum, non-affected and affected forearms, upper arms; WNaUA (Without Non-affected Upper Arm setup): four sensors – sternum, non-affected and affected forearms, affected upper arm; WAUA (Without Affected Upper Arm setup): three sensors – sternum, non-affected and affected forearms; WSt (Without Sternum setup): two sensors – non-affected and affected forearms; NaFA (Non-affected Forearm setup): one sensor – non-affected forearm; AFA (Affected Forearm setup): one sensor – affected forearm.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8604323/v1/37fe00d0b191031d839396a5.png"},{"id":102296808,"identity":"c30511b3-69e7-4107-9bb2-f9717b870d78","added_by":"auto","created_at":"2026-02-10 10:21:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1186479,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8604323/v1/223dafdc-1e05-4f35-a9f2-ebd2594264d7.pdf"},{"id":102207924,"identity":"4f4ef3ac-53a0-4a86-a496-24d200288ec5","added_by":"auto","created_at":"2026-02-09 12:07:12","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":418539,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary table S1. The custom-made acceptability questionnaire\u003c/p\u003e\n\u003cp\u003eSupplementary table S2. How much time each participant spent detaching the sensors\u003c/p\u003e\n\u003cp\u003eSupplementary table S3. How much time each participant spent detaching the sensors\u003c/p\u003e\n\u003cp\u003eSupplementary table S4. Distribution of answer themes on the open-ended items in the custom-made acceptability questionnaire\u003c/p\u003e\n\u003cp\u003eSupplementary fig. S1. Correlation heatmap of all the items in the custom-made acceptability questionnaire\u003c/p\u003e","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8604323/v1/fe1cb42b7f780dbdd561de27.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluating Acceptability and Minimal Sensor Configuration for Home-Based Monitoring of Upper Extremity Use After Stroke","fulltext":[{"header":"Background","content":"\u003cp\u003eStroke is a leading cause of disability globally [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], resulting in an estimated global cost of US\u003cspan\u003e$\u003c/span\u003e891\u0026nbsp;billion in 2017 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Approximately 50\u0026ndash;80% of survivors experience motor impairments in the upper extremities (UEs) after stroke [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These impairments are associated with reduced independency in activities of daily living (ADLs) and a lower health-related quality of life [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Motor rehabilitation after stroke plays a crucial role in promoting improvements of motor functions and reducing the long-term burden of stroke [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMotor rehabilitation is increasingly being conducted directly in the stroke survivors\u0026rsquo; home to meet their needs, reduce the associated cost, and as a direct result of the widespread early hospital discharge [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Repetitive, task-specific, and varied movements with the UEs are necessary to promote neuroplastic changes that lead to improvements in motor functions and independence in daily activities [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. One of the challenges for home-based motor rehabilitation is the systematic monitoring of stroke survivors\u0026rsquo; movements with their affected UEs. The monitoring is essential for tracking the use and improvements of the UE and fine-tuning the rehabilitation plan. It is not only important to assess movements of the affected UE, but also those of the non-affected UE. This is necessary to evaluate the issue of learned non-use [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], in addition to the trunk movement to identify the presence of compensatory movements [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWearable sensors can offer a practical strategy for measuring UE movements of stroke survivors in their home [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Inertial measurement unit (IMU), which consists of accelerometer, gyroscope and magnetometer, is the most commonly used wearable sensor for capturing movement of body segments [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. IMUs can provide valuable data about the quantity and quality of movements in the affected UE, non-affected UE, as well as the trunk [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The simplest biomechanical model to capture these movements includes the tracking of seven segments (upper arm, forearm, and hand of each extremity, plus the trunk), which requires the use of nine sensors (on each upper arm, forearm, hand, and shoulder, plus the sternum).\u003c/p\u003e \u003cp\u003eThe use of nine sensors on the stroke survivor\u0026rsquo;s upper body raises the question of feasibility, as it is likely to demand significant time, cost and personnel, potentially reducing adherence among stroke survivors. It is critical to consider stroke survivors\u0026rsquo; perspectives on this issue, as the implementation of sensors in rehabilitation relies not only on measurement accuracy but also on the user\u0026rsquo;s acceptability. Acceptability refers to \u0026ldquo;the perception among implementation stakeholders that a given treatment, service, practice, or innovation is agreeable, palatable, or satisfactory\u0026rdquo; [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, p. 67] and has long been regarded as a key factor in the implementation of new technology [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, it remains relatively under-prioritized within wearable sensor research [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Acceptability includes subthemes such as usability, wearability, psychological comfort, and willingness to wear sensors. Usability refers to the ease, intuitiveness, and practicality with which a user can use a wearable sensor to achieve its intended purpose [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], whereas wearability refers to the comfort, fit, and unobtrusiveness of the sensor when it is worn on the body over time [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Wearable sensors, despite being accurate, cannot represent a viable solution if stroke survivors are resistant to wearing sensors while conducting daily life activities in their home. Therefore, including stroke survivors\u0026rsquo; perspectives on this issue aligns with the need to move towards sustainable and person-centered care [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eReducing the number of sensors could potentially facilitate stroke survivors\u0026rsquo; acceptability of using sensors at home. The reduction of sensor number should be carefully calibrated to ensure the selected sensors still capture the complexity of behavioral data. A potential measure to capture the information content of a physical system is information entropy. Derived from information theory [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] this is a measure of how much information is needed to describe a system\u0026rsquo;s set of probable states [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. When applied to multisensor systems, information entropy can be used to characterize the number of observable states and their associated probabilities. By iteratively reducing the sensor set, changes in entropy can be quantified, providing a measure of information loss as sensors are removed. This process has been successfully used to evaluate how different sensor setups capture behavioral complexity in stroke survivors\u0026rsquo; UE movements [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe aim of this study was to identify a feasible sensor setup for monitoring stroke survivors\u0026rsquo; UE movements during daily activities at home. This was achieved through the evaluation of stroke survivors\u0026rsquo; subjective acceptability of wearing sensors in a home-like environment and to evaluate the number of sensors necessary to accurately capture the complexity of behavioral data.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eA total of eleven (n\u0026thinsp;=\u0026thinsp;11) stroke survivors in the chronic phase (more than 1 year since stroke) were recruited for this study. To be eligible for the study, individuals had physical impairments in the UE as a consequence of stroke, were above 18 years of age, able to comprehend information and instructions given to them, and provide informed consent. Potential participants were contacted at a rehabilitation hospital while participating in an intensive UE training program, or at the hospital outpatient clinic. They were provided with a sheet of general information about the study, emphasizing that their participation was voluntary and they had the possibility to withdraw from the study at any point without providing a reason and without any consequence. Upon confirmation that they understood the purpose and procedure of the study; written informed consent was obtained from all the individuals invited to participate.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProcedure\u003c/h3\u003e\n\u003cp\u003eThe procedure involved participants wearing nine IMU sensors on their upper body while performing ADLs of their choice for one hour in a simulated home environment at a rehabilitation hospital or natural home setting. The first three participants wore sensors in the simulated home-like environment to ensure safety and feasibility of the procedure. The home-like environment included a living room, kitchen, and bathroom. For the remaining eight participants, data collection took place in their own home to increase ecological validity. Data collection took place in Norway between March and April 2025.\u003c/p\u003e \u003cp\u003eAfter the explanation of the procedure, participants were invited to attach nine sensors on their body independently. Three sensors were placed on each arm, one on each shoulder, and one on the sternum (see the materials section). They were instructed on how and where to place the sensors on the different body segments to ensure a correct and non-irritating attachment sequence. The researcher provided manual help when the participants requested it or when they were not able to attach a sensor independently. The number of sensors attached independently and the total time for attaching them was recorded for each participant, consistent with previously published procedures [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The times were noted in an observation sheet, along with whether participants attached the sensors independently or required any assistance from the researcher. At the end of this sequence, a researcher ensured the correct placement of all sensors and the recording began.\u003c/p\u003e \u003cp\u003eAs soon as the recording started, participants were instructed to do self-chosen ADLs for one hour. The number and type of self-chosen activities varied largely across participants. Some performed minimal physical activity and spent most of the time conversating with the researcher. Others were more physically active and engaged in activities like preparing and eating a meal, reading, using their phone, and brewing coffee. A few participants performed exercises while wearing sensors (e.g., gait training, sit-to-stand, and exercise games). After one hour of recording, the sensors were turned off, and participants were invited to independently detach all sensors. The participants received advice and guidance during the detachment process and received help if needed. Similar to the attaching procedure, the detachment time, feedback, and assistance was noted in the observation form.\u003c/p\u003e \u003cp\u003eLastly, participants were invited to answer a questionnaire about their acceptability of wearing sensors (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). A brief introduction was given to them about its purpose and how it should be filled in. The questionnaire was administered like an interview, where the researcher read the questions aloud, while giving the participants the possibility to look at the questions themselves. This approach ensured that the participants understood all items.\u003c/p\u003e\n\u003ch3\u003eMaterials\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSensors\u003c/h2\u003e \u003cp\u003eThe Xsens MTw Awinda IMUs (Xsens Technology B.V., Enschede, The Netherlands), which have been validated for accuracy [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], were used in this study. The nine IMU sensors were placed as follows: one on the sternum (Thoracic vertebra 8 as level reference), one on each shoulder (scapular spine), one on each upper arm (lateral mid-humerus), one on each forearm (dorsal mid-ulnaris) and one on each hand (dorsal side). The sensors on the sternum and shoulders were attached using the shirt provided by Xsens. For the participants that could not wear the Xsens shirt, the sensor on the sternum was attached using a Velcro strap around the torso and a double-sided tape. Medical tape was used to attach the sensors on the shoulders. The sensors on the arms were attached using Velcro straps and the gloves provided by Xsens for the hands. All sensors were placed according to recommendations and previous studies [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Wireless transmission from the Xsens sensors allowed real-time recording on a laptop via the MT Manager Software Suite 2022.2.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAcceptability questionnaire\u003c/h3\u003e\n\u003cp\u003eThe acceptability data were collected using a custom-made acceptability questionnaire, informed by a prior pilot study. The pilot study involved two stroke survivors and investigate the suitability of two existing acceptability questionnaires: the system usability scale [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and the questionnaire by Auepanwiriyakul et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The pilot participants\u0026rsquo; feedback and researchers\u0026rsquo; experience highlighted that these questionnaires contained a mix of positive and negative statements, which caused interpretation issues. In addition, some items were not directly relevant to participants\u0026rsquo; experience of wearing the sensors, while others were redundant. Therefore, a custom-made questionnaire was developed to improve clarity and relevance, and to avoid redundancy. The custom-made questionnaire was based on both existing questionnaires, retaining relevant items, merging similar items, and rephrasing them to ensure consistent and understandable wording.\u003c/p\u003e \u003cp\u003eThe final custom-made acceptability questionnaire was comprised of 14 items, covering four subthemes: usability (items 1, 2, 8 \u0026amp; 12), wearability (items 3\u0026ndash;6), psychological comfort (items 7, 9, 10 \u0026amp; 11), and willingness to wear sensors (items 13\u0026ndash;14). All items were positive statements. Each item was scored on a 1\u0026ndash;5 Likert scale: 1\u0026thinsp;=\u0026thinsp;strongly disagree, 2\u0026thinsp;=\u0026thinsp;disagree, 3\u0026thinsp;=\u0026thinsp;neutral, 4\u0026thinsp;=\u0026thinsp;agree, and 5\u0026thinsp;=\u0026thinsp;strongly agree. A higher score represented higher perceived acceptability. Internal consistency of the custom-made questionnaire was assessed to evaluate how consistently the items measure the same underlying construct. Further, 5 open-ended questions were included to explore the participants\u0026rsquo; view at a deeper level. The participants could freely choose between the English or Norwegian version of the questionnaire. Both versions were reviewed and revised by three individuals fluent in both languages.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eAcceptability and attaching/detaching time of sensors\u003c/h2\u003e \u003cp\u003eItem scores were summed for each participant to generate a total acceptability score and single scores for the different subthemes (usability, wearability, psychological comfort, and willingness to wear sensors). As the questionnaire consisted of ordinal Likert data, non-parametric statistics (median [Mdn] and interquartile range [IQR]) were computed for the total scores and subtheme scores across participants [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The different subtheme scores were transformed into percentages of their possible ranges to facilitate interpretation. Scores were classified as low (0\u0026ndash;33%), moderate (34\u0026ndash;66%), and high (67\u0026ndash;100%). Mean (M) and standard deviation (SD) were computed for attaching and detaching times. The acceptability data and attaching/detaching times were analyzed using Excel (Microsoft Excel for Microsoft 365 MSO, Version 2503, Redmond, WA, USA) and the Jamovi Project (2025) \u003cem\u003ejamovi\u003c/em\u003e (Version 2.6) [Computer Software]. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.jamovi.org\u003c/span\u003e\u003cspan address=\"https://www.jamovi.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eInternal consistency of the custom-made questionnaire\u003c/h3\u003e\n\u003cp\u003eInternal consistency was assessed using McDonald\u0026rsquo;s omega (ω). The items measure different constructs (usability, wearability, psychological comfort, and willingness to wear sensors) and McDonald\u0026rsquo;s omega provides an accurate estimate of how consistently the items measure the same underlying construct. The omega values were defined as very high (\u0026ge;\u0026thinsp;0.9), high (0.80\u0026ndash;0.89), acceptable (0.70\u0026ndash;0.79), minimal (0.60\u0026ndash;0.69) and unacceptable (\u0026lt;\u0026thinsp;0.60) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCalculating the minimum number of sensors required\u003c/h2\u003e \u003cp\u003eThe data from each IMU sensor were extracted using the MT Manager 2025.2.0 (Xsens Technology B.V., Enschede, The Netherlands) and processed through a Matlab 2024b (24.2.0.2806996, MathWorks Inc. Natick, MA) script, developed specifically for the current purposes. First, we cleaned the dataset from outliers by determining the M and SD of the three dimensions of acceleration and eliminating time points that were outside (in absolute terms) the M plus-minus six SDs. The data were then filtered by a 4th order 5Hz low pass Butterworth filter. As some sensors had occlusions, only the time periods with data from all sensors were considered. Given some variation in the exact time of data collection for each participant, we decided to use the first 50 minutes of data collection for the analysis.\u003c/p\u003e \u003cp\u003eTo identify the minimum number of sensors, we performed the following procedure. First, we identified redundant sensors by measuring the mutual information between pairs of sensors (the calculation of mutual information is explained below). From this, we defined a sequential list of sensors that could be potentially removed. Second, using information entropy and participants\u0026rsquo; feedback, we calculated a time series of participants\u0026rsquo; behavior for each setup of sensors, removing iteratively the redundant sensors. Lastly, we calculated the similarity by using the Cronbach\u0026rsquo;s \u003cem\u003eα\u003c/em\u003e between the full sensor setup and the setups with removed sensors based on participants\u0026rsquo; choice and our redundancy analyses.\u003c/p\u003e \u003cp\u003eTo calculate the entropy, we performed the procedure described in Williams [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Information entropy is formalized as the negative sum of the product of the probability of occurrence of the bins multiplied by the logarithm of base 2 of the same probability of occurrence of the bins (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:-\\sum\\:\\left(p*{\\text{log}}_{2}p\\right)\\)\u003c/span\u003e\u003c/span\u003e). In this way, entropy is a measure of how many \u003cem\u003ebits\u003c/em\u003e of information are necessary to capture the behavior of a given system. Mutual information, relatedly, captures how much knowing about one system\u0026rsquo;s behavior (in this case, one sensor) tell us (\u0026ldquo;informs\u0026rdquo;) about another system\u0026rsquo;s behavior (in this case, a second sensor). Formally, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{I}_{s1;s2}={H}_{s1}-{H}_{\\left(s1|s2\\right)}\\)\u003c/span\u003e\u003c/span\u003e, mutual information of sensor 1 (I\u003csub\u003es1;s2\u003c/sub\u003e) is the entropy of sensor 1 (\u003cem\u003eH\u003c/em\u003e\u003csub\u003es1\u003c/sub\u003e) minus the entropy of sensor 1 \u003cem\u003egiven\u003c/em\u003e the occurrences of sensor 2 (\u003cem\u003eH\u003c/em\u003e\u003csub\u003es1|s2\u003c/sub\u003e) (conditional entropy); i.e., how much information sensor 2 carries about sensor 1.\u003c/p\u003e \u003cp\u003eGiven both measures rely on state probabilities, we binned the data into fixed segments (depending only on whether the data referred to acceleration or orientation of the sensor). The bins for acceleration were larger for large values and vice-versa: (only showing the positive values) for values from 0 m/s\u003csup\u003e2\u003c/sup\u003e to 0.9 m/s\u003csup\u003e2\u003c/sup\u003e, the bins were of 0.05 m/s\u003csup\u003e2\u003c/sup\u003e; for values from 0.9 m/s\u003csup\u003e2\u003c/sup\u003e to 1.5 m/s\u003csup\u003e2\u003c/sup\u003e, the bins were of 0.1 m/s\u003csup\u003e2\u003c/sup\u003e; for values from 1.5 m/s\u003csup\u003e2\u003c/sup\u003e to 5 m/s\u003csup\u003e2\u003c/sup\u003e, the bins were of 0.5 m/s\u003csup\u003e2\u003c/sup\u003e; and for values from 5 m/s\u003csup\u003e2\u003c/sup\u003e to 12 m/s\u003csup\u003e2\u003c/sup\u003e, the bins were of 1.0 m/s\u003csup\u003e2\u003c/sup\u003e. The bins for orientation were always of 5\u0026ordm; considering the whole range of -180\u0026ordm; to 180\u0026ordm;. Then, for each time point, a series of six numbers (representing the bins of three accelerations and three orientations) was created and the frequency of these were computed.\u003c/p\u003e \u003cp\u003eFor our redundancy analyses, we used the whole data set (50 minutes) to quantify the mutual information between sensors. That is, we calculated the entropy per sensor and the conditional entropy per pair of sensors. The mutual information was normalized by dividing the mutual information by the information entropy of the sensor making the values range between 0 and 1. To create the entropy time series (which would later allow for an analysis with Cronbach\u0026rsquo;s \u003cem\u003eα\u003c/em\u003e between setups of sensors), we created blocks of two minutes of data considering the data of all sensors included in the given set (see below) and calculated the information entropy. Information entropy was computed for the full sensor setup as well as for other setups with a reduced number of sensors. The reduced sensor setups were created considering the redundancy analyses and the participants\u0026rsquo; feedback.\u003c/p\u003e \u003cp\u003eFinally, to check how many sensors were necessary to guarantee the information gathered from all nine sensors, we computed, for each participant, the Cronbach\u0026rsquo;s \u003cem\u003eα\u003c/em\u003e between the entropy from the full sensor setup and the entropy from each alternative sensor setup. Values larger than 0.9 would indicate sufficient similarity to use the alternative setup \u0026ndash; the information from the full setup was still observed in the smaller one.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAcceptability and attaching/detaching time\u003c/h2\u003e \u003cp\u003eThe total acceptability score for wearing all sensors was high (Mdn\u0026thinsp;=\u0026thinsp;57 out of 70 [81%], IQR\u0026thinsp;=\u0026thinsp;11) with observed scores ranging from 28 to 68. Subtheme scores were as follows: wearability was high (Mdn\u0026thinsp;=\u0026thinsp;13 out of 15 [87%], IQR\u0026thinsp;=\u0026thinsp;3.5); psychological comfort was high (Mdn\u0026thinsp;=\u0026thinsp;14 out of 15 [93%], IQR\u0026thinsp;=\u0026thinsp;3.5); willingness to wear the sensors continuously was high (Mdn\u0026thinsp;=\u0026thinsp;8 out of 10 [80%], IQR\u0026thinsp;=\u0026thinsp;4.5); and usability was moderate (Mdn\u0026thinsp;=\u0026thinsp;11 out of 20 [55%], IQR\u0026thinsp;=\u0026thinsp;5.5). The distribution of answers on the various closed-ended items is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eResponses to individual questionnaire closed-ended items (n\u0026thinsp;=\u0026thinsp;11).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eITEM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMdn (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStrongly disagree 1 (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDisagree 2 (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNeutral 3 (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAgree 4 (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStrongly agree 5 (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSABILITY ITEMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. The sensors were easy to put on and take off\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. I would be able to use the sensors on my own\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8. I found the design of the sensors to be practical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12. I would find it easy to integrate the sensors into my daily life\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWEARABILITY ITEMS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. Wearing the sensors did not limit my ability to move or perform physical activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. The sensors did not interfere with washing or going to the toilet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5. The sensors were comfortable to wear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6. I did not experience any itchiness or skin irritations while wearing the sensors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePSYCHOLOGICAL COMFORT ITEMS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7. I found the appearance of the sensors to be appealing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9. I would feel comfortable wearing the sensors even if they were visible to family members/ close friends\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10. I would feel comfortable wearing the sensors even if they were visible to acquaintances\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11. I did not feel anxious while wearing the sensors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWILLINGNESS TO WEAR ITEMS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13. I would be willing to wear the sensors continuously for long term use if beneficial for my rehabilitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14. I would be willing to wear the sensors in my daily life if recommended by a therapist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMedian and interquartile range are shown for each item, along with the percentage of participants choosing each response category (1\u0026thinsp;=\u0026thinsp;strongly disagree to 5\u0026thinsp;=\u0026thinsp;strongly agree).\u003c/p\u003e \u003cp\u003eOnly two participants were able to attach all sensors themselves, with a mean attaching time of 241 s (SD\u0026thinsp;=\u0026thinsp;85). Of the remaining nine participants, seven were able to independently attach the sensors on the affected UE, while only one managed to attach the sensors on the non-affected UE themselves. Only three participants detached all sensors independently, with a mean detaching time of 50 s (SD\u0026thinsp;=\u0026thinsp;10). Of the remaining eight participants, seven managed to detach the sensors on the affected UE themselves, while only one participant independently detached the sensors on the non-affected UE. Descriptive statistics of attachment and detachment times are shown in Supplementary Tables S2 and S3.\u003c/p\u003e \u003cp\u003eThe open-ended responses showed that some participants liked the purpose of wearing the sensors in home rehabilitation (n\u0026thinsp;=\u0026thinsp;4), while others were neutral (n\u0026thinsp;=\u0026thinsp;5). There were contrasting perceptions of wearability. Seven participants provided positive remarks related to comfort, size, and design, whereas eight expressed negative views about wearing the full setup of sensors for longer periods. Further, eight participants disliked the attaching of sensors, with one noting: \u0026ldquo;it is impossible with this many sensors\u0026rdquo;. Although most of the participants considered all the sensors acceptable, the hand sensors received the most negative comments (n\u0026thinsp;=\u0026thinsp;5). The distribution of themes to the different items, along with detailed descriptions, is provided in Supplementary Table S4.\u003c/p\u003e \u003cp\u003eIn summary, the overall acceptability of wearing the full sensor setup was high, but usability was moderate. This was reflected in the general inability of participants to attach all the sensors independently and the high attaching and detaching times, as well as in the open-ended responses, which indicated that the number of sensors was too high, with the sensors on the hand restricting movements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eInternal consistency of the custom-made questionnaire\u003c/h2\u003e \u003cp\u003eThe internal consistency of the acceptability items was very high (ω\u0026thinsp;=\u0026thinsp;0.913), after item 11 (\u0026ldquo;I did not feel anxious\u0026rdquo;) was excluded due to lack of variance. The internal consistency of the wearability items was acceptable (ω\u0026thinsp;=\u0026thinsp;0.740). Item 6 (\u0026ldquo;I did not experience itchiness or skin irritations\u0026rdquo;) was found to have a low item-total correlation (r\u0026thinsp;=\u0026thinsp;0.033) with the rest of the wearability items, and when excluded, the internal consistency was found to be high (ω\u0026thinsp;=\u0026thinsp;0.852). The psychological comfort items were tested for internal consistency, but item 11 was excluded due to lack of variance. The internal consistency of the remaining items was found to be acceptable (ω\u0026thinsp;=\u0026thinsp;0.768). The internal consistency of items regarding willingness to integrate the sensors into daily life was very high (ω\u0026thinsp;=\u0026thinsp;0.970). The internal consistency of the usability items was high (ω\u0026thinsp;=\u0026thinsp;0.856). A correlation heatmap of all the items in the custom-made acceptability questionnaire is shown in Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eNumber of sensors\u003c/h2\u003e \u003cp\u003eThe participants demonstrated qualitative differences in both the amount and type of movement while wearing the sensors. While some participants mostly remained seated and talked to the researchers, others played a videogame or prepared and ate a meal. This variability affects the potential number of sensors required as more elaborate movements (with differential motion of each segment) requires more sensors to be captured and vice-versa.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows an example of the redundancy network for two different participants, illustrating how a given sensor \u0026ldquo;informs\u0026rdquo; about others. It is observed that, for some cases, a given sensor is highly related, and thus \u0026ldquo;informative\u0026rdquo;, about others (sternum for participant 4) while, in other cases, the sensors are clustered and differentiated in terms of information (participant 5). The participants exemplify an active person (participant 5) and a more sedentary person (participant 4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the average normalized mutual information values between sensors considering all participants. The highest values were for the shoulder sensors between each other (0.11), both shoulders with the sternum (non-affected: 0.08; affected: 0.09), affected hand and affected forearm (0.08), non-affected hand and non-affected forearm (0.09).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAveraged normalized mutual information between sensors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eST\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNaH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNaFA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNaUA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNaSh\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAFA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAUA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNaH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNaFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNaUA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNaSh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eLegend: ST: Sternum; Na: Non-affected; A: Affected; H: Hand; FA: Forearm; UA: Upper Arm; Sh: Shoulder\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eConsidering these results and participants\u0026rsquo; feedback, the sensors on the shoulders, on the hands, and on the forearms are candidates to be removed. Accordingly, we derived the following sensor setups (1) the full setup of sensors (nine sensors); (2) Without Shoulders setup (WSh): sternum, non-affected and affected hands, forearms, upper arms (seven sensors); (3) Without Hands setup (WH): sternum, non-affected and affected forearms, upper arms (five sensors); (4) Without Non-affected Upper Arm setup (WNaUA): sternum, non-affected and affected forearms, affected upper arm (four sensors); (5) Without Affected Upper Arm setup (WAUA): sternum, non-affected and affected forearms (three sensors); (6) Without Sternum setup (WSt): non-affected and affected forearms (two sensors); (7) Non-affected Forearm setup (NaFA): non-affected forearm (one sensor); (8) Affected Forearm setup (AFA): affected forearm (one sensor). Most setups also preserved the need to track trunk movements as a common source of compensations [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Additionally, we emphasized the affected limb as this would be the object for movement analysis and tracking.\u003c/p\u003e \u003cp\u003eThe Cronbach\u0026rsquo;s \u003cem\u003eα\u003c/em\u003e of the alternative setups in relation to the full sensor setup showed that the number of sensors required to capture behavioral complexity differed across participants (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The WNaUA setup with four sensors (sternum, non-affected and affected forearms, affected upper arm) was necessary for the most active participant (P5 in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) while the WAUA setup (sternum, non-affected and affected forearms) was enough for the other participants.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe aim of this study was to identify a feasible sensor setup for monitoring stroke survivors\u0026rsquo; UE movements during daily activities at home. This was achieved through the evaluation of stroke survivors\u0026rsquo; acceptability of wearing sensors in a home or home-like environment and the number of sensors necessary to accurately capture the complexity of behavioral data. The first finding, based on the quantitative and qualitative acceptability results, was that although the full sensor setup had high acceptability among participants, usability was repeatedly shown to be low-to-moderate. Participants could not attach all these sensors on their own and did not like the high number of sensors. In short, a nine-sensor setup in stroke survivors\u0026rsquo; home-like environment was not feasible in our selected sample. The second finding, based on the entropy results, was that three-to-four sensors were sufficient to accurately capture the complexity of behavioral data. These sensor setups can increase the feasibility of monitoring daily living movements of stroke survivors in the home environment.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eComparison with existing literature\u003c/h2\u003e \u003cp\u003eThis study aligns with previous research [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] showing stroke survivors\u0026rsquo; high acceptability but moderate usability in wearing sensors during ADLs. Similar to our findings, these previous studies reported that participants valued the purpose of wearing sensors to promote use and function, but had mixed opinions regarding wearability and usability, including preferences for smaller sensors. Consistent with our findings, Bishop et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] found that most participants were unable to attach sensors to their non-affected upper extremity, highlighting usability challenges especially on the non-affected side. This reflects that participants had a real-life experience of attaching and detaching the sensors independently, highlighting practical challenges that might not be captured in studies where sensors are attached by researchers. Together, these findings suggest that while stroke survivors generally appreciate the potential benefits of wearable sensors, practical aspects such as amount, size, placement, and ease of handling remain important factors for improving usability.\u003c/p\u003e \u003cp\u003eA previous study employing similar procedures has shown that four IMU sensors were enough to capture the behavior of stroke survivors when performing 30 upper-limb tasks (including simple gestures, reaching, and manipulating objects) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This was done by using an initial setup of 10 sensors, considering tasks performed with affected and non-affected limbs separately, and considering only accelerations (derived from the accelerometer in the IMU sensors). While the resultant number of sensors were equal to what we observed here, the specific setup of sensors was dependent on the performing limb (prioritizing affected or non-affected segments) and the setup of sensors were different: they included the trunk (similar to the sternum measure used here), upper arm, forearm and hand of the acting arm. Thus, it might be that if stroke survivors are asked to perform more elaborate unimanual tasks (as analyzed in Pacheco et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]) or are more active in their daily activities (shown in the example by Participant 5), more sensors might be required.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ePractical implications\u003c/h2\u003e \u003cp\u003eWhile a nine-sensor IMU setup would be ideal for a comprehensive capture of daily movements in stroke survivors at home, such a setup is not only burdensome for the stroke survivor, their family, and the health care system, but also likely to be unfeasible. Low feasibility would in turn reduce adherence, meaning that even highly accurate sensor setups become ineffective if they cannot be implemented into stroke survivors\u0026rsquo; daily lives. Designing sensor setups for capturing UE movements involves a careful balance between the number of sensors, acceptability among stroke survivors, and the amount of movement data. For example, trunk movements could be measured with three sensors (one on each shoulder and one on the sternum), but a single sternum sensor is sufficient to capture stroke survivors\u0026rsquo; compensatory trunk movements. Similarly, the hand sensors were reported to be uncomfortable for several participants, but these are not essential as long as the forearm sensors are maintained; the same applies to the upper arm sensors. The remaining two forearm sensors would still allow accurate measurements of quantity and quality of movements in each UE, as well as use in affected UE versus non-affected UE.\u003c/p\u003e \u003cp\u003eThese trade-offs offer concrete ways to simplify the sensor setup, maintain measurement accuracy, increase acceptability among stroke survivors, and make it feasible for home-based stroke rehabilitation. This sensor setup offers practitioners a practical solution for tracking stroke survivors\u0026rsquo; daily movements \u0026ndash; a crucial measure for evaluating progress and tailoring home-based rehabilitation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThis is one of the first studies to investigate the feasibility of using wearable sensors to capture stroke survivors\u0026rsquo; movement in a home or home-like environment, combining end-users\u0026rsquo; acceptability and movement-derived data. This approach ensured ecological validity by allowing participants to wear the sensors in their home or in a home-like environment. Furthermore, a custom-made acceptability questionnaire was developed to accurately assess stroke survivors\u0026rsquo; acceptability of wearing sensors in their home. The limitations of this study included a small sample size of eleven participants with no recorded impairment level in their UE. The measurement time was limited to one hour, which may not reflect full-day usage of the sensors in the home environment. Accuracy assessments were limited to entropy measures, and future studies should include kinematics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eFuture directions\u003c/h2\u003e \u003cp\u003eFuture studies should investigate the proposed sensor setup in a larger sample over an extended measurement period in the home environment to verify its accuracy and to re-evaluate acceptability using the revised custom-made questionnaire, allowing direct comparison with the present study. On a broader level, future work should continue to identify ways to improve the feasibility of wearable sensor technology in stroke survivors\u0026rsquo; home environments. To enhance usability and acceptability, one potential direction is the development of sensors made by nanotechnology and to embed them into everyday clothing. Such an approach may increase stroke survivors\u0026rsquo; comfort and flexibility, as well as making the sensors appear more \u0026ldquo;normal\u0026rdquo; for users [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Further, easier attaching and detaching methods should be investigated to increase the usability, for instance: a sensor-integrated sleeve [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], or e-textiles [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdherence among stroke survivors may be enhanced through engaging solutions, including smartphone applications or a smartwatches that encourage physical activity by delivering movement reminders, which have earlier been positively received by stroke survivors [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Individualized sensor setups could be valuable for future research. Our entropy results suggest that people with a larger quantity and more varied movements may need more sensors, while those with less or uniform movements may need fewer, potentially improving usability and acceptability. Previous studies also suggest that personalization may enhance feasibility, adherence, and meaningful data capture [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study showed that although a nine-sensor setup is ideal in terms of accuracy and had high acceptability among the participants, its usability was low to moderate. Entropy analysis indicated that using three-to-four sensors (both forearms, sternum, with or without affected upper arm) are sufficient to capture the complexity of behavioral data in stroke survivors in a home-like environment. Reducing the number of sensors would likely improve usability, thereby promoting acceptability and ultimately enhancing feasibility and adherence. Future studies should validate this sensor setup in a larger sample of stroke survivors with documented levels of impairment observed in a home environment and continue to explore strategies for further improving wearable sensor technology.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eADLs\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Activities of Daily Living\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAFA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Affected Forearm setup: one sensor – affected forearm\u003c/p\u003e\n\u003cp\u003eIMU\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Inertial Measurement Unit\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIQR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Interquartile Range\u003c/p\u003e\n\u003cp\u003eM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Mean\u003c/p\u003e\n\u003cp\u003eMdn\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Median\u003c/p\u003e\n\u003cp\u003en\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Sample size\u003c/p\u003e\n\u003cp\u003eNAFA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Non-affected Forearm setup: one sensor – non-affected forearm\u003c/p\u003e\n\u003cp\u003eREK\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Regional Committee for Medical and Health Research Ethics\u003c/p\u003e\n\u003cp\u003eSD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Standard Deviation\u003c/p\u003e\n\u003cp\u003eUE\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Upper Extremity\u003c/p\u003e\n\u003cp\u003eWAUA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Without Affected Upper Arm setup: three sensors – sternum, non-affected and affected forearms\u003c/p\u003e\n\u003cp\u003eWH\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Without Hands setup: five sensors – sternum, non-affected and affected forearms, upper arms\u003c/p\u003e\n\u003cp\u003eWNaUA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Without Non-affected Upper Arm setup: four sensors – sternum, non-affected and affected forearms, affected upper arm\u003c/p\u003e\n\u003cp\u003eWSh\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Without Shoulders setup: seven sensors – sternum, non-affected and affected hands, forearms, upper arms\u003c/p\u003e\n\u003cp\u003eWSt\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Without Sternum setup: two sensors – non-affected and affected forearms\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was assessed by the regional committee for medical and health research ethics in South-Eastern Norway (REK), and was not classified as clinical research as the aim was not to gain new knowledge about health and disease, thus did not fall under the Law of Health Research in Norway (Lov om medisinsk og helsefaglig forskning (helseforskningsloven) - Lovdata), and did not require formal ethical approval from REK (Reg nr. 844958). All observations, field notes, and questionnaire responses were fully anonymized and treated confidentially throughout the research process. No identifiable personal information was collected, and all data were stored and presented in a way that ensured participants could not be recognized at any stage. All ethical principles were adhered to, and informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe two photographed participants provided consent for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the conclusions of this article are available in the Open Science Framework repository (https://osf.io/rnysa/overview?view_only=7352733640f54cf496ae1c716fce2710) and are included within the article and its additional file(s).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted as part of the PEER-HOMEcare project, funded by the Norwegian Research Council (ID 350985), the Portuguese Foundation for Science and Technology (THCS/0002/2023), under the framework of the co-fund partnership of Transforming Health and Care Systems, THCS, (GA N\u0026deg; 101095654) of the EU Horizon Europe Research and Innovation Programme. The funding bodies had no involvement in any of the study stages.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the conceptualization of the study; BSM collected the data; BSM and MMP analyzed the data; all authors interpreted the data; BSM, MMP, LO drafted the manuscript; all authors reviewed, edited, and approved the manuscript. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Tonje Hexeberg Marka and Dennis Elvegaard for their contribution in the data collection process.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFeigin VL, Abate MD, Abate YH, Abd Elhafeez S, Abd-Allah F, Abdelalim A, et al. 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Theory Pract. 2020;36(7):799-809.\u003c/li\u003e\n\u003cli\u003eLangerak AJ, Regterschot GRH, Evers M, Van Beijnum B-JF, Meskers CGM, Selles RW, et al. A Sensor-Based Feedback Device Stimulating Daily Life Upper Extremity Activity in Stroke Patients: A Feasibility Study. Sensors. 2023;23(13):5868.\u003c/li\u003e\n\u003cli\u003eToh SFM, Gonzalez PC, Fong KNK. Usability of a wearable device for home-based upper limb telerehabilitation in persons with stroke: A mixed-methods study. Digit Health. 2023;9:205520762311537.\u003c/li\u003e\n\u003cli\u003eWang Q, De Baets L, Timmermans A, Chen W, Giacolini L, Matheve T, et al. Motor Control Training for the Shoulder with Smart Garments. Sensors. 2017a;17(7):1687.\u003c/li\u003e\n\u003cli\u003eWang Q, Markopoulos P, Yu B, Chen W, Timmermans A. Interactive wearable systems for upper body rehabilitation: a systematic review. J. Neuroeng. Rehabil. 2017b;14(1).\u003c/li\u003e\n\u003cli\u003ePloderer B, Fong J, Withana A, Klaic M, Nair S, Crocher V, et al. ArmSleeve. 2016:700-11.\u003c/li\u003e\n\u003cli\u003eKhan MA, Saibene M, Das R, Brunner I, Puthusserypady S. Emergence of flexible technology in developing advanced systems for post-stroke rehabilitation: a comprehensive review. J Neural Eng. 2021;18(6):061003.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuroengineering-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jner","sideBox":"Learn more about [Journal of NeuroEngineering and Rehabilitation](http://jneuroengrehab.biomedcentral.com/)","snPcode":"12984","submissionUrl":"https://submission.nature.com/new-submission/12984/3","title":"Journal of NeuroEngineering and Rehabilitation","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Rehabilitation, Home rehabilitation, Stroke, Upper extremity, Wearable sensors, Feasibility, Acceptability, Entropy","lastPublishedDoi":"10.21203/rs.3.rs-8604323/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8604323/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Home rehabilitation for stroke survivors is crucial to promote upper extremity (UE) use and improvement of functional abilities, enhancing independence and quality of life. Wearable sensors enable monitoring of movement trends and progress during home rehabilitation. The feasibility of using sensors in the home of stroke survivors depends both on survivors’ acceptability and on sensors’ accuracy in capturing the movement. The aim of this study was to identify a feasible sensor setup for monitoring stroke survivors’ UE movements during daily activities at home by evaluating 1) survivors’ subjective acceptability of wearing sensors, and 2) the number of sensors necessary to accurately capture the complexity of behavioral data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Eleven chronic stroke survivors were observed in a natural or simulated home environment, while attempting to attach/detach sensors and wearing them doing self-chosen Activities of Daily Living. Nine inertial measurement unit sensors were placed on participants’ UE and sternum. Acceptability was assessed with a custom-made questionnaire consisting of 14 items scored on a 1–5 Likert scale, and five open-ended questions. Further, information entropy was calculated to determine the minimal number of sensors needed to capture movement complexity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Total acceptability of wearing sensors was high, with a median score of 57 out of 70 (81%; IQR = 11), while usability was moderate, with a median score of 11 out of 20 (55%; IQR = 5.5). Lower usability was also reported in the open-ended responses and observed when participants attempted attaching and detaching sensors. The minimal sensor setup to capture behavior complexity consisted of three-to-four sensors, placed on the sternum, non-affected and affected forearms, with or without affected upper arm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: The sensors were found to have high acceptability, although usability challenges remained. The proposed three-to-four sensor setup was sufficient to maintain accuracy of the sensors, while potentially increasing stroke survivors’ acceptability and usability. In turn, this may enhance feasibility and improve stroke survivors’ adherence to wearing sensors at home.\u003c/p\u003e","manuscriptTitle":"Evaluating Acceptability and Minimal Sensor Configuration for Home-Based Monitoring of Upper Extremity Use After Stroke","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-09 12:07:05","doi":"10.21203/rs.3.rs-8604323/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-13T12:15:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-13T10:01:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-03T15:38:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-10T15:00:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-03T16:37:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"306837506240934091639441355298298265655","date":"2026-03-02T23:00:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"144809958373705559908083515726601279787","date":"2026-03-02T10:26:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191388757159437939483920489318181139310","date":"2026-03-01T01:28:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"92360493966665196588910917525347830856","date":"2026-02-08T13:45:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-03T18:32:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-15T08:35:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-15T08:34:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of NeuroEngineering and Rehabilitation","date":"2026-01-14T17:53:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuroengineering-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jner","sideBox":"Learn more about [Journal of NeuroEngineering and Rehabilitation](http://jneuroengrehab.biomedcentral.com/)","snPcode":"12984","submissionUrl":"https://submission.nature.com/new-submission/12984/3","title":"Journal of NeuroEngineering and Rehabilitation","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e6740345-a613-4ed7-80d2-e2296fdb9b55","owner":[],"postedDate":"February 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T12:25:50+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-09 12:07:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8604323","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8604323","identity":"rs-8604323","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}