Interpretable machine learning comprehensive human gait deterioration analysis

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.

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Publication Details

Journal
Frontiers in Neuroinformatics
Published
2024-08-23
DOI
https://doi.org/10.3389/fninf.2024.1451529
Citations
10
Primary Topic
Balance, Gait, and Falls Prevention
Type
article
Field-Weighted Citation Impact
11.45

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article

Interpretable machine learning comprehensive human gait deterioration analysis

Abdullah Alharthi
10 citations
Frontiers in Neuroinformatics
Balance, Gait, and Falls Prevention
11.45
article

Interpretable machine learning comprehensive human gait deterioration analysis

Abdullah Alharthi
article en
10 citations

Abstract

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.

Frontiers in NeuroinformaticsVol. 18
King Khalid University (SA)
King Khalid University
Openalex Percentile: Top 1%
Balance, Gait, and Falls Prevention
11.45
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