Advancing Wearable-Based Upper-Limb Stroke Recovery Assessment to the Clinic: A Comparison of Movement Segmentation Strategies

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Abstract
Continuous, objective, and precise upper-limb motor assessments are essential for realizing the vision of precision rehabilitation for stroke survivors. Wearable inertial sensors have emerged as a promising solution, enabling the analysis of motor performance in real-world settings. Recent studies have introduced two movement segmentation methods—anatomical segmentation and linear segmentation—for processing wearable inertial data to monitor post-stroke upper-limb motor recovery, each grounded in distinct theories of motor control and behavior. These methods differ in their practical implications for clinical use: linear segmentation requires only a single wearable device on the stroke-affected wrist, while anatomical segmentation necessitates an additional sensor on the sternum. This study seeks to systematically compare the clinimetric performance of these two approaches, taking into account their differences in practicality, to provide insights into their effective integration into clinical practice. 17 stroke survivors were equipped with inertial sensors on the trunk and the stroke-affected wrist while performing activities of daily living in a simulated apartment setting. Acceleration time-series from wrist movements were decomposed into movement segments using each movement segmentation approach. Reliable features were extracted from the movement segments, and supervised regression models were trained to establish concurrent validity against existing clinical measures. Anatomical segmentation demonstrated strong concurrent validity against existing clinical measures but may face challenges for continuous use due to the need for multiple sensors. Linear segmentation, on the other hand, provided slightly reduced but acceptable performance in motor deficit assessment while offering the advantage of requiring only a single wrist-worn sensor.
Funding Information
  • NIH (5R01EB027777)
  • NSF-NIH Smart and Connected Health Program (R01HD114147)

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