Explainable Gait Abnormality Detection Using Dual-Dataset CNN-LSTM Models
By: Parth Agarwal , Sangaa Chatterjee , Md Faisal Kabir and more
Potential Business Impact:
Helps doctors spot walking problems by watching how you move.
Gait is a key indicator in diagnosing movement disorders, but most models lack interpretability and rely on single datasets. We propose a dual-branch CNN-LSTM framework a 1D branch on joint-based features from GAVD and a 3D branch on silhouettes from OU-MVLP. Interpretability is provided by SHAP (temporal attributions) and Grad-CAM (spatial localization).On held-out sets, the system achieves 98.6% accuracy with strong recall and F1. This approach advances explainable gait analysis across both clinical and biometric domains.
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