# Explainable AI and Machine Learning Towards Human Gait Deterioration Analysis

arXiv: https://arxiv.org/abs/2306.07165 · HTML: https://mlsanity.com/paper/2306.07165/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 68 references that are in the map.
- First posted on arXiv 12 Jun 2023.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [Evaluating the visualization of what a Deep Neural Network has learned](https://mlsanity.com/paper/1509.06321/) (2015)
- [Methods for Interpreting and Understanding Deep Neural Networks](https://mlsanity.com/paper/1706.07979/) (2017)
- [Towards Best Practice in Explaining Neural Network Decisions with LRP](https://mlsanity.com/paper/1910.09840/) (2019)
- [iNNvestigate neural networks!](https://mlsanity.com/paper/1808.04260/) (2018)
- [Deep Learning-Based Gait Recognition Using Smartphones in the Wild](https://mlsanity.com/paper/1811.00338/) (2018)
- [Understanding and Comparing Deep Neural Networks for Age and Gender Classification](https://mlsanity.com/paper/1708.07689/) (2017)
- [Explaining the Unique Nature of Individual Gait Patterns with Deep Learning](https://mlsanity.com/paper/1808.04308/) (2018)
- [How do Convolutional Neural Networks Learn Design?](https://mlsanity.com/paper/1808.08402/) (2018)

## Abstract (arXiv)

Gait analysis, an expanding research area, employs non invasive sensors and machine learning techniques for a range of applicatio ns. In this study, we concentrate on gait analysis for detecting cognitive decline in Parkinson's disease (PD) and under dual task conditions. Using convolutional neural networks (CNNs) and explainable machine learning, we objectively analyze gait data and associate findings with clinically relevant biomarkers. This is accomplished by connecting machine learning outputs to decisions based on human visual observations or derived quantitative gait parameters, which are tested and routinely implemented in curr ent healthcare practice. Our analysis of gait deterioration due to cognitive decline in PD enables robust results using the proposed methods for assessing PD severity from ground reaction force (GRF) data. We achieved classification accuracies of 98% F1 sc ores for each PhysioNet.org dataset and 95.5% F1 scores for the combined PhysioNet dataset. By linking clinically observable features to the model outputs, we demonstrate the impact of PD severity on gait. Furthermore, we explore the significance of cognit ive load in healthy gait analysis, resulting in robust classification accuracies of 100% F1 scores for subject identity verification. We also identify weaker features crucial for model predictions using Layer Wise Relevance Propagation. A notable finding o f this study reveals that cognitive deterioration's effect on gait influences body balance and foot landing/lifting dynamics in both classification cases: cognitive load in healthy gait and cognitive decline in PD gait.