# Digital Gait Biomarkers for Parkinson’s Disease: Subject-Wise Validated Explainable AI Framework Using Vertical Ground Reaction Force Signals

## TL;DR

This study introduces an AI framework using gait data to detect Parkinson's disease and track its progression with high accuracy and explainability.

## Contribution

A novel two-stage explainable AI framework using vertical ground reaction force signals for PD detection and severity estimation is proposed.

## Key findings

- The FCNN-Transformer model achieved the highest mean AUC of 0.940 for PD detection.
- XGBoost regression predicted H&Y severity with a Spearman correlation of 0.921 and low error rates.
- Gait indicators declined four years before PD diagnosis in a longitudinal cohort, supporting early detection.

## 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, p < 0.05). Stability-based explainable AI using Integrated Gradients consistently identified variability-related VGRF features as the most informative, which were also significantly different between groups at the data level (p < 0.001, FDR-corrected). In the second stage, XGBoost regression was applied to PD subjects to predict continuous H&Y severity, achieving strong correlation with clinical grades (Spearman ρ = 0.921, p < 0.001), low error (MAE = 0.158, RMSE = 0.241), and high determination (R2 = 0.953). This shows that gait-based features are a sensitive enough signal to continuously quantify disease progression. In addition, in the TREND prospective longitudinal cohort (n = 696), wearable walking indicators differed significantly from those of non-patients prior to diagnosis, and a decline in walking pace was observed approximately four years before Parkinson’s disease diagnosis, providing the basis for early screening and monitoring using gait-based digital biomarkers. These results demonstrate that gait-based digital biomarkers can objectively quantify both PD presence and disease progression. The proposed framework provides a reproducible, explainable, and clinically interpretable AI-based decision support approach for PD assessment.