Knowledge Distillation with Enhanced Lightweight Stgcn for Gait Disorders Recognition

Abstract

Gait recognition is essential for the early diagnosis and monitoring of movement disorders such as Knee Osteoarthritis (KOA) and Parkinson’s Disease (PD). This study presents a new method for skeleton-based gait recognition. Our approach combines Spatio-Temporal Graph Convolutional Networks (STGCN) and Long Short-Term Memory (LSTM) layers to analyze movement data. The STGCN blocks capture spatial and temporal relationships between human joints, while the LSTM layers enhance the model’s ability to recognize long-term gait patterns. By incorporating knowledge distillation, our method effectively transfers insights from a complex teacher model to a streamlined student model, improving both accuracy and computational efficiency. We conducted our evaluations on two public datasets for KOA and PD. The results show that our approach outperforms state-of-the-art performance, offering a reliable tool for the clinical assessment and monitoring of gait-related disorders.