The countermovement vertical jump height, flight time, and jump duration are used to assess athletic performance. Force-plate and motion-capture cameras are used to estimate these parameters, yet, their application is limited to dedicated lab environments. Despite the potential of inertial measurement units (IMU) for estimating the jump height, their accuracy has not been validated. This study investigates the accuracy of our proposed method to estimate the jump height using a sacrum-mounted IMU, during countermovement jumping. Eleven individuals performed four jumps each. To obtain the jump height, we transformed the IMU readouts into anatomical planes, and double-integrated the vertical acceleration after correction for zero velocity and vertical displacement. The accuracy of jump height obtained by IMU was compared to force-plate and motion-capture cameras during jumps without arm swing (mean error (standard deviation) of 0.3(2.2) cm and 1.0(3.0) cm, and correlation coefficient of 0.83 and 0.82, respectively) and during jumps with arm swing (−1.1(2.1) cm and 0.5(1.9) cm, and 0.92 and 0.89). The correlation coefficients were high, and the errors were comparable to the difference between the jump height obtained by force-plate and cameras. Therefore, a sacrum-mounted IMU can be recommended for in-field assessment of countermovement jump with and without arm swing.

Upper extremity pain and injury are among the most common musculoskeletal complications manual wheelchair users face. Assessing the temporal parameters of manual wheelchair propulsion, such as propulsion duration, cadence, push duration, and recovery duration, is essential for providing a deep insight into the mobility, level of activity, energy expenditure, and cumulative exposure to repetitive tasks and thus providing personalized feedback. The purpose of this paper is to investigate the use of inertial measurement units (IMUs) to estimate these temporal parameters by identifying the start and end time of hand contact with the push-rim during each propulsion cycle. We presented a model based on data collected from 23 participants (14 males and 9 females, including 9 experienced manual wheelchair users) to guarantee the reliability and generalizability of our method. The obtained outcomes from our IMU-based model were then compared against an instrumented wheelchair (SMARTWheel) as a reference criterion. The results illustrated that our model was able to accurately detect hand contact and hand release and predict temporal parameters, including the push duration and recovery duration in manual wheelchair users, with the mean error ± standard deviation of 10 ± 60 milliseconds and -20 ± 80 milliseconds, respectively. The findings of this study demonstrate the potential of hand-mounted IMUs as a reliable and objective tool for analyzing temporal parameters in manual wheelchair propulsion. IMUs offer significant strides towards inclusivity and accessibility due to their portability and user-friendliness and can democratize health monitoring of manual wheelchair users by making it accessible to a broader range of users compared to traditional technologies.

Hockey skating objective assessment can help coaches detect players’ performance drop early and avoid fatigue-induced injuries. This study aimed to calculate and experimentally validate the 3D angles of lower limb joints of hockey skaters obtained by inertial measurement units and explore the effectiveness of the on-ice distinctive features measured using these wearable sensors in differentiating low- and high-calibre skaters. Twelve able-bodied individuals, six high-calibre and six low-calibre skaters, were recruited to skate forward on a synthetic ice surface. Five IMUs were placed on their dominant leg and pelvis. The 3D lower-limb joint angles were obtained by IMUs and experimentally validated against those obtained by a motion capture system with a maximum root mean square error of 5 deg. Additionally, among twelve joint angle-based distinctive features identified in other on-ice studies, only three were significantly different (p-value < 0.05) between high- and low-calibre skaters in this synthetic ice experiment. This study thus indicated that skating on synthetic ice alters the skating patterns such that the on-ice distinctive features can no longer differentiate between low- and high-calibre skating joint angles. This wearable technology has the potential to help skating coaches keep track of the players’ progress by assessing the skaters’ performance, wheresoever.

One of the main factors in controlling infectious diseases such as COVID-19 is to prevent touching preoral and prenasal regions. Face touching is a habitual behaviour that occurs frequently. Studies showed that people touch their faces 23 times per hour on average. A contaminated hand could transmit the infection to the body by a facial touch. Since controlling this spontaneous habit is not easy, this study aimed to develop and validate a technology to detect and monitor face touch using dynamic time warping (DTW) and KNN (k-nearest neighbours) based on a wrist-mounted inertial measurement unit (IMU) in a controlled environment and natural environment trials. For this purpose, eleven volunteers were recruited and their hand motions were recorded in controlled and natural environment trials using a wrist-mounted IMU. Then the sensitivity, precision, and accuracy of our developed technology in detecting the face touch were evaluated. It was observed that the sensitivity, precision, and accuracy of the DTW-KNN classifier were 91%, 97%, and 85% in controlled environment trials and 79%, 92%, and 79% in natural environment trials (daily life). In conclusion, a wrist-mounted IMU, widely available in smartwatches, could detect the face touch with high sensitivity, precision, and accuracy and can be used as an ambulatory system to detect and monitor face touching as a high-risk habit in daily life.

(1) Background: Accurate detection of propulsion and recovery phases is essential for assessing wheelchair propulsion biomechanics. Different measurement approaches rely on distinct biomechanical signals and may therefore yield systematically different phase durations. (2) Methods: This study compared propulsion and recovery phase durations obtained from a wheel acceleration-based approach (Approach 1: marker-based motion capture) and a hand-acceleration-based approach (Approach 2: hand-mounted IMU using the continuous wavelet transform, CWT) in 33 manual wheelchair users with neurological conditions across three propulsion speeds (slow, preferred, fast). (3) Results: Approach 2 detected longer propulsion phases (bias: +0.13 s, limits of agreement (LoA) ± 0.30 s) and shorter recovery phases (bias: −0.13 s, LoA ± 0.30 s) than Approach 1. Both biases varied with speed conditions and diminished as propulsion speed increased. The later end event detected by Approach 2 is compatible with the hypothesis that hand–pushrim contact can continue after wheel acceleration changes, potentially including non-propulsive contact late in the push phase. However, this mechanism was not directly verified: no independent pushrim-force or hand-contact reference was collected. (4) Conclusions: The two approaches operationalize propulsion phase timing using different signals and should not be assumed to identify identical events. The observed differences limit direct comparison of propulsion parameters across studies unless the measurement approach is reported.