Multi-Modal Sensing for Propulsion Estimation in People Post-Stroke Across Speeds.

ABSTRACT: Gait rehabilitation is critical for regaining locomotor independence after neuromotor injuries like stroke. Rehabilitation literature indicates the need for such therapy to continue beyond the clinic in order to maintain motor function and support recovery. However, implementing community-based rehabilitation requires the ability to monitor gait in the real-world with clinically relevant accuracies. Despite advances in machine learning, achieving this performance with single sensing modalities has been challenging using wearable sensors like inertial measurement units (IMUs) and pressure insoles. Here, we investigate the benefits of multi-modal sensing by integrating IMU and insole data to develop individualized machine learning models in people post-stroke that estimate propulsion, a key

SUBMITTER: Swaminathan K 

PROVIDER: S-EPMC12276930 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature