Explainable AI and Machine Learning Towards Human Gait Deterioration Analysis

TL;DR

This study employs explainable CNN-based machine learning to analyze gait data for detecting cognitive decline in Parkinson's disease, achieving high accuracy and linking gait features to clinical biomarkers and cognitive load effects.

Contribution

It introduces an explainable AI approach for gait analysis in PD, connecting machine learning outputs to clinical observations and identifying key gait features affected by cognitive decline.

Findings

Achieved 98% F1 score in PD severity classification

Achieved 100% F1 score in healthy subject identification

Linked gait deterioration to balance and foot dynamics

Abstract

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.