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ORIGINAL RESEARCH article

Front. Neuroinform.
Volume 18 - 2024 | doi: 10.3389/fninf.2024.1451529
This article is part of the Research Topic Neuro-detection: Advancements in Pattern Detection and Segmentation Techniques in Neuroscience View all 5 articles

Interpretable Machine Learning Comprehensive Human Gait Deterioration Analysis

Provisionally accepted
Abdullah Alharthi Abdullah Alharthi *
  • King Khalid University, Abha, Saudi Arabia

The final, formatted version of the article will be published soon.

    In this study, we investigate the impact of cognitive decline conditions on gait performance, drawing connections between gait deterioration in Parkinson's Disease (PD) and healthy individuals dual tasking. We employ Explainable Artificial Intelligence (XAI) to interpret the intricate patterns in gait dynamics influenced by cognitive loads. Our findings reveal significant alterations in gait parameters under cognitive decline conditions, highlighting the distinctive patterns associated with PDrelated gait impairment and those induced by multitasking in healthy subjects. Through advanced XAI techniques, we decipher the underlying features contributing to gait changes, providing insights into specific aspects affected by cognitive decline. This approach facilitates a comprehensive understanding of the complexities involved in gait control and offers a valuable framework for assessing gait deterioration in diverse scenarios. Furthermore, our study establishes a novel perspective on gait analysis, demonstrating the applicability of XAI in elucidating the shared characteristics of gait disturbances in PD and dual-task scenarios in healthy individuals. The interpretability offered by XAI enhances our ability to discern subtle variations in gait patterns, contributing to a more nuanced comprehension of the factors influencing gait dynamics in PD and dual-task conditions, emphasizing the role of XAI in unraveling the intricacies of gait control. The insights gained from this study not only enhance our understanding of gait deterioration but also underscore the potential of XAI as a valuable tool in gait analysis across diverse populations and contexts.

    Keywords: Deep convolutional neural networks (CNN), deep learning, Ground reaction forces (GRF), Gait, Interpretable neural networks, Parkinson's disease, perturbation

    Received: 19 Jun 2024; Accepted: 29 Jul 2024.

    Copyright: © 2024 Alharthi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Abdullah Alharthi, King Khalid University, Abha, Saudi Arabia

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