MicroXercise: A Micro-Level Comparative and Explainable System for Remote Physical Therapy
Hanchen David Wang, Nibraas Khan, Anna Chen, Nilanjan Sarkar, Pamela, Wisniewski, Meiyi Ma

TL;DR
MicroXercise is an advanced, explainable, micro-motion analysis system for remote physical therapy that improves feedback accuracy and interpretability using multi-dimensional DTW and deep learning explainability techniques.
Contribution
It introduces a micro-level, explainable AI system integrating wearable sensors and multi-dimensional DTW for detailed exercise monitoring in home-based PT.
Findings
39% improvement in Feature Mutual Information (FMI)
42% improvement in Continuity metrics
Enhanced micro-motion detection and feedback clarity
Abstract
Recent global estimates suggest that as many as 2.41 billion individuals have health conditions that would benefit from rehabilitation services. Home-based Physical Therapy (PT) faces significant challenges in providing interactive feedback and meaningful observation for therapists and patients. To fill this gap, we present MicroXercise, which integrates micro-motion analysis with wearable sensors, providing therapists and patients with a comprehensive feedback interface, including video, text, and scores. Crucially, it employs multi-dimensional Dynamic Time Warping (DTW) and attribution-based explainable methods to analyze the existing deep learning neural networks in monitoring exercises, focusing on a high granularity of exercise. This synergistic approach is pivotal, providing output matching the input size to precisely highlight critical subtleties and movements in PT, thus…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsStroke Rehabilitation and Recovery
