Inverse Reinforcement Learning for Interpretable Keystroke Biomarkers in Parkinson's Disease
Keystroke dynamics offer a passive window into motor function, but existing work extracts aggregate typing statistics and trains classifiers for PD/control discrimination, foregoing interpretability and rarely reporting reliability. We instead apply maximum-entropy inverse reinforcement learning (IRL) to raw keystroke timing, recovering a per-subject speed-preference weight (w_speed) reflecting the implicit cost assigned to fast movement, without any clinical label during fitting. On the neuroQWERTY MIT-CSXPD dataset (85 subjects, 42 PD), we diagnose and correct a feature collinearity failure in an initial four-parameter decomposition, yielding an identifiable three-parameter model. The r...
| Authors | Navin Bondade |
|---|---|
| Primary category | cs.LG |
| Published | 24 June 2026 |
| Last updated at source | 24 July 2026 |
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