Real-Time Quantitative Analysis of Rehabilitation Training Movement Quality Integrating Surface Electromyography Signals and RGB-D Video

Abstract

The accurate assessment of movement quality in rehabilitation training is a critical challenge in both clinical and home-based settings. Traditional methods often rely on subjective evaluations or limited sensor data, which can lead to inconsistent and inaccurate assessments. This paper introduces a novel methodology that integrates surface electromyography (sEMG) signals with RGB-D video data to provide a comprehensive, real-time analysis of rehabilitation movement quality. The proposed framework leverages the Manifold-driven Event Forecaster, a sophisticated model that captures the intricate biomechanical and neuromuscular aspects of human motion. The methodology is composed of three primary components: a Nonlinear Constraint Optimizer that aligns multimodal data onto a shared manifold, an Agent-based Temporal Segmenter that partitions data into meaningful temporal segments, and a Probabilistic Quality Predictor that quantifies movement quality with an uncertainty-aware approach. The framework employs advanced strategies such as manifold alignment refinement and uncertainty-aware modeling to enhance robustness and interpretability. These innovations enable the system to make accurate, real-time predictions of movement quality, effectively addressing challenges such as variability in patient performance and sensor noise. Experimental results demonstrate the efficacy of the proposed approach, showing significant improvements in the precision and reliability of movement quality assessments. This comprehensive solution has the potential to transform rehabilitation practices by providing objective, data-driven insights into patient progress and therapy effectiveness, ultimately contributing to improved patient outcomes and more personalized rehabilitation programs.