Explainable Gait Abnormality Detection Using Dual-Dataset CNN-LSTM Models

Parth Agarwal; Sangaa Chatterjee; Md Faisal Kabir; Suman Saha

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arXiv · 2509.16472

Explainable Gait Abnormality Detection Using Dual-Dataset CNN-LSTM Models

Parth Agarwal·Sangaa Chatterjee·Md Faisal Kabir·Suman Saha

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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.

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Parth Agarwal, Sangaa Chatterjee, Md Faisal Kabir, Suman Saha. 2025-09-19. Explainable Gait Abnormality Detection Using Dual-Dataset CNN-LSTM Models. https://arxiv.org/abs/2509.16472

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