Time-warping Augmentation and ST-GCN for Physical Rehabilitation Exercise Assessment

This paper presents an effective and scalable framework for evaluating physical rehabilitation exercises, with a focus on enhancing prediction accuracy and providing clinically meaningful feedback to support patient recovery. The proposed method comprises three key components. First, Time-Warping Augmentation is introduced to simulate variations in movement speed, improving the model’s robustness to diverse exercise tempos. Second, a feature extraction pipeline is designed, integrating spatial enhancements via channel and joint attention mechanisms with Multi-Scale Temporal Convolutional Blocks to capture temporal dynamics at multiple resolutions. Third, a sequence modeling module is employed, incorporating joint-wise feature aggregation, positional encoding, bidirectional LSTM, and temporal attention pooling to extract fine-grained temporal patterns. The framework is evaluated on the KIMORE datasets, achieving competitive performance. Notably, it achieves a 21.62% reduction in Mean Absolute Deviation (MAD) compared to existing methods. These results underscore the potential of the proposed system for real-world telerehabilitation applications, enabling reliable and automated assessment in unsupervised environments.