Transformer-Based Framework for Motion Capture Denoising and Anomaly Detection in Medical Rehabilitation
It provides a scalable, cost-effective solution for remote rehabilitation, reducing on-site supervision.
The paper tackles noise and missing data in optical motion capture for medical rehabilitation, using a Transformer-based model to denoise and detect abnormal movements, showing superior performance in data reconstruction and anomaly detection on stroke and orthopedic datasets.
This paper proposes an end-to-end deep learning framework integrating optical motion capture with a Transformer-based model to enhance medical rehabilitation. It tackles data noise and missing data caused by occlusion and environmental factors, while detecting abnormal movements in real time to ensure patient safety. Utilizing temporal sequence modeling, our framework denoises and completes motion capture data, improving robustness. Evaluations on stroke and orthopedic rehabilitation datasets show superior performance in data reconstruction and anomaly detection, providing a scalable, cost-effective solution for remote rehabilitation with reduced on-site supervision.