AUTHOR=Alharthi Abdullah S. 

TITLE=Interpretable machine learning comprehensive human gait deterioration analysis

JOURNAL=Frontiers in Neuroinformatics

VOLUME=Volume 18 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2024.1451529

DOI=10.3389/fninf.2024.1451529

ISSN=1662-5196

ABSTRACT=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.