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.
Authors
- Abdullah Alharthi (ORCID: https://orcid.org/0000-0001-6776-7503)
Institutions
- King Khalid University (SA)
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
Funders
- King Khalid University