ArticleBioengineering (Basel, Switzerland)2026
Digital Gait Biomarkers for Parkinson's Disease: Subject-Wise Validated Explainable AI Framework Using Vertical Ground Reaction Force Signals.
Article in Bioengineering (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- CNN-GRU-KAN: A Novel Multi-Branch Framework for Parkinson's Disease Detection Based on Gait Classification.Bioengineering (Basel, Switzerland) · 2026Article
- Quantitative Analysis of Timed Up and Go Metrics Across Parkinson's Disease Severity and Their Clinical Correlations.Diagnostics (Basel, Switzerland) · 2026Article
- Passive sensing of gait and medication-related fluctuations in Parkinson's disease.Journal of neuroengineering and rehabilitation · 2026Article
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Authors and funding
3 authors.
Funding
Abstract
Parkinson's disease (PD) is associated with progressive gait deterioration; however, widely used clinical scales such as the Hoehn & Yahr (H&Y) stage are limited in capturing continuous severity changes due to subjectivity and discrete grading. This study proposes a two-stage explainable AI framework using vertical ground reaction force (VGRF) signals to achieve reproducible PD detection and continuous severity estimation. In the first stage, three deep learning models, temporal convolutional network (TCN), BiGRU with attention, and FCNN-Transformer, were trained using windowed VGRF signals under repeated subject-wise data segmentation. All models achieved high discrimination performance (AUC ≥ 0.93), with FCNN-Transformer showing the highest mean AUC (0.940) and statistically superior performance (paired Wilcoxon test,
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Registered trials
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