# Benchmarking Reliability of Deep Learning Models for Pathological Gait Classification

arXiv: https://arxiv.org/abs/2409.13643 · HTML: https://mlsanity.com/paper/2409.13643/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 45 references that are in the map.
- First posted on arXiv 20 Sep 2024.

## Builds on (most cited first, 11 of 11)

- [Do ImageNet Classifiers Generalize to ImageNet?](https://mlsanity.com/paper/1902.10811/) (2019)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Learning to Simulate Complex Physics with Graph Networks](https://mlsanity.com/paper/2002.09405/) (2020)
- [Interaction Networks for Learning about Objects, Relations and Physics](https://mlsanity.com/paper/1612.00222/) (2016)
- [Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction](https://mlsanity.com/paper/2002.11927/) (2020)
- [Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networks](https://mlsanity.com/paper/1706.00527/) (2017)
- [Graph Stacked Hourglass Networks for 3D Human Pose Estimation](https://mlsanity.com/paper/2103.16385/) (2021)
- [Stronger, Faster and More Explainable: A Graph Convolutional Baseline for Skeleton-based Action Recognition](https://mlsanity.com/paper/2010.09978/) (2020)
- [Generalization in Adaptive Data Analysis and Holdout Reuse](https://mlsanity.com/paper/1506.02629/) (2015)
- [Using Learnable Physics for Real-Time Exercise Form Recommendations](https://mlsanity.com/paper/2310.07221/) (2023)
- [Remote Pathological Gait Classification System](https://mlsanity.com/paper/2105.01634/) (2021)

## Built on by (most cited first, 4 of 4)

- [DynSTG-Mamba: Dynamic Spatio-Temporal Graph Mamba with Cross-Graph Knowledge Distillation for Gait Disorders Recognition](https://mlsanity.com/paper/2503.13156/) (2025)
- [Real-Time Feedback and Benchmark Dataset for Isometric Pose Evaluation](https://mlsanity.com/paper/2506.11774/) (2025)
- [Posture-Driven Action Intent Inference for Playing style and Fatigue Assessment](https://mlsanity.com/paper/2507.11642/) (2025)
- [GenGait: A Transformer-Based Model for Human Gait Anomaly Detection and Normative Twin Generation](https://mlsanity.com/paper/2604.01997/) (2026)

## Abstract (arXiv)

Early detection of neurodegenerative disorders is an important open problem, since early diagnosis and treatment may yield a better prognosis. Researchers have recently sought to leverage advances in machine learning algorithms to detect symptoms of altered gait, possibly corresponding to the emergence of neurodegenerative etiologies. However, while several claims of positive and accurate detection have been made in the recent literature, using a variety of sensors and algorithms, solutions are far from being realized in practice. This paper analyzes existing approaches to identify gaps inhibiting translation. Using a set of experiments across three Kinect-simulated and one real Parkinson's patient datasets, we highlight possible sources of errors and generalization failures in these approaches. Based on these observations, we propose our strong baseline called Asynchronous Multi-Stream Graph Convolutional Network (AMS-GCN) that can reliably differentiate multiple categories of pathological gaits across datasets.