Transformer-Based Framework for Motion Capture Denoising and Anomaly Detection in Medical Rehabilitation

TL;DR

This paper introduces a Transformer-based deep learning framework that denoises motion capture data and detects anomalies in real time to improve safety and effectiveness in medical rehabilitation.

Contribution

It presents a novel end-to-end model integrating optical motion capture with Transformers for robust data processing and anomaly detection in rehabilitation settings.

Findings

Outperforms existing methods in data reconstruction accuracy

Achieves high precision in real-time anomaly detection

Enhances remote rehabilitation effectiveness

Abstract

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.