Dataset construction and dual-branch model for gait recognition in musculoskeletal disorder diagnosis

Existing gait datasets primarily target identity recognition, lacking specialized gait data resources for medical diagnosis that cover multiple musculoskeletal diseases. To address this, a musculoskeletal disease gait dataset GaitMed oriented towards medical scenarios was independently designed and collected, covering 6 common disease types and including multi-view and multi-modal data, providing a data foundation for research on gait-based disease identification, classification, and rehabilitation assessment. The dual-branch gait recognition model MSGait, designed based on this dataset, achieves 81.99% Rank-1 accuracy and 96.27% Rank-3 accuracy in disease classification tasks by fusing silhouette and human parsing features, validating the application potential of gait recognition in medical diagnosis.