MicroXercise: A Micro-Level Comparative and Explainable System for Remote Physical Therapy
<|reference_start|>MicroXercise: A Micro-Level Comparative and Explainable System for Remote Physical Therapy: 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 transforming complex AI analysis into clear, actionable feedback. By highlighting these micro-motions in different metrics, such as stability and range of motion, MicroXercise significantly enhances the understanding and relevance of feedback for end-users. Comparative performance metrics underscore its effectiveness over traditional methods, such as a 39% and 42% improvement in Feature Mutual Information (FMI) and Continuity. MicroXercise is a step ahead in home-based physical therapy, providing a technologically advanced and intuitively helpful solution to enhance patient care and outcomes.<|reference_end|>
@article{wang2024microxercise:,
    title={MicroXercise: A Micro-Level Comparative and Explainable System for
  Remote Physical Therapy},
    author={Hanchen David Wang, Nibraas Khan, Anna Chen, Nilanjan Sarkar, Pamela
  Wisniewski, Meiyi Ma},
    journal={arXiv preprint arXiv:2408.11837},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.11837},
    primaryClass={cs.LG cs.AI cs.HC eess.SP}
}