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

arXiv: https://arxiv.org/abs/2509.16472 · HTML: https://mlsanity.com/paper/2509.16472/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 19 Sep 2025.

## Builds on (most cited first, 2 of 2)

- [A Comprehensive Survey on Deep Gait Recognition: Algorithms, Datasets and Challenges](https://mlsanity.com/paper/2206.13732/) (2022)
- [A Bidirectional Siamese Recurrent Neural Network for Accurate Gait Recognition Using Body Landmarks](https://mlsanity.com/paper/2412.03498/) (2024)

## Abstract (arXiv)

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.