Joint-level kinematic patterns during walking with wearable sensors - study data

Purpose: After stroke, walking is characterized by hemiparetic patterns, quantified with force sensitive walkways and motion capture systems. Some joint-level kinematic patterns of walking also can be obtained with wearable sensors. The purpose of this project was to measure joint-level kinematic patterns during walking with wearable sensors and determine the association with walking speed and endurance in individuals with chronic stroke. Methods: In this cross-sectional observational study, participants donned APDM Opal wearable sensors during walking tests (10-meter walk test or 6-minute walk test). We extracted joint-level kinematic variables of elevation at midswing, circumduction, foot strike angle, and toe off angle. Associations of each variable with walking speed and endurance were tested, and significantly associated variables were entered into a regression model. Results: 68 individuals with chronic stroke were included. We found that the less affected foot strike angle, less affected toe off angle, and more affected toe off angle were significant predictors of walking speed (R2≥0.71, p&lt;0.001). Less affected toe off angle, more affected foot strike angle, and more affected toe off angle were significant predictors of walking endurance (R2≥0.67, p&lt;0.001). Conclusion: We found consistent evidence that greater toe off angle (may reflect greater push-off) and lesser foot strike angle (may reflect lesser foot drop) were important predictors of greater walking speed and endurance. Our results suggest that wearable sensors can provide important information about joint-level kinematic patterns that are important for walking function. This information could help therapists target interventions towards specific deficits or compensatory patterns to improve walking.Data related to publication: Cleland, B.T., Kim, M. &amp; Madhavan, S. The Relation Between Hemiparetic Gait Patterns and Walking Function After Stroke, as Measured with Wearable Sensors. <i>Ann Biomed Eng</i> (2025). https://doi.org/10.1007/s10439-025-03754-7