{"ID":2869060,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.16472","arxiv_id":"2509.16472","title":"Explainable Gait Abnormality Detection Using Dual-Dataset CNN-LSTM Models","abstract":"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.","short_abstract":"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)...","url_abs":"https://arxiv.org/abs/2509.16472","url_pdf":"https://arxiv.org/pdf/2509.16472v1","authors":"[\"Parth Agarwal\",\"Sangaa Chatterjee\",\"Md Faisal Kabir\",\"Suman Saha\"]","published":"2025-09-19T23:53:45Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}