EDA-Based Arousal Analysis During Gamified Leg Agility in Parkinson's Disease: A Cross-Subject XAI Approach

Leg Agility (LA) is a standard motor task for motor assessment in Parkinson's disease (PD), and recent work has explored its gamification to sustain patient engagement in unsupervised rehabilitation scenarios. However, the effect of this gamification on autonomic arousal during task execution has not been objectively characterised. This paper investigates electrodermal activity (EDA) arousal patterns in 33 PD patients performing traditional LA and two difficulty levels of a gamified Bouncing Ball (BB) exercise. A JMIM-based feature selection pipeline fed a Leave-One-Subject-Out XGBoost classifier with SHAP explainability, complemented by Wicoxon testing with Benjamini-Hochberg FDR correction and per-leg-session monotonic trend analysis. The transition from LA to the game (BB1) is robustly detectable, with several spectral features at p < .001 and showing a monotonically increasing EDA trend in 70–80% of sessions. In contrast, discriminating between difficulty levels (BB1 vs BB2) yields only 61% accuracy, despite significant group-level statistical differences, indicating that difficulty-related arousal changes are present at the group level but heterogeneous across individuals. Inter-subject SHAP variance analysis identifies a rank divergence between feature importance and patient-to-patient variability in the BB1 vs BB2 comparison, a direct XAI signature of idiosyncratic responses, and pinpoints cvxPrmax as the most population-consistent biomarker. These findings suggest that gamification reliably increases autonomic arousal in PD patients, while difficulty modulation requires personalised rather than universal calibration, with implications for real-time adaptive game design in motor rehabilitation.