Cross-Graph Relational Knowledge Distillation with Lightweight ST-GCN for Gait Disorder Recognition

Gait recognition is essential for early diagnosis of movement disorders. The integration of new technologies can enhance the early identification of these conditions. Many current studies use Spatio-Temporal Graph Convolutional Networks (ST-GCN) that depend on skeletal data, however, these models often require substantial memory, which limits their use in clinical settings. This work introduces an improved lightweight ST-GCN model that merges temporal convolution, weighted fusion, and GRU units to analyze movement patterns and extract spatio-temporal features for gait classification. Additionally, we present a novel Cross-Graph Relational Knowledge Distillation (CGRKD) technique that transfers both spatial and temporal relationship knowledge from a larger model to a more compact one by using shared memory and relational alignment among skeletal joints. Our CGRKD approach preserves important movement relationships between joints while lowering computational complexity, thus enhancing the model’s suitability for clinical use. Experimental results on the KOA-NM, PD-WALK, and ATAXIA datasets indicate that our method surpasses existing literature methods.