🤖 AI Summary
This study addresses the limited interpretability of existing digital biomarkers for Parkinson’s disease, which predominantly rely on statistical features without modeling the underlying motivation of typing behavior. The authors propose a novel approach by introducing inverse reinforcement learning to keystroke dynamics analysis, treating each keystroke as a speed-selection action and recovering an interpretable three-parameter reward function that characterizes individual temporal typing patterns. Through rigorous validation—including reward function decomposition, correlation analysis, sensitivity testing, and data leakage controls—they demonstrate that the recovered speed-preference weight exhibits a significant negative correlation with UPDRS-III scores (r = –0.607, p < 0.001). This association remains robust across multiple sub-cohorts and configurations and provides incremental explanatory power beyond raw typing speed (ΔR² = 0.144, p = 0.006).
📝 Abstract
Keystroke dynamics have been explored extensively as a passive digital biomarker for Parkinson's disease (PD), typically by extracting summary statistics from typing timing and training a classifier to discriminate PD from healthy controls. We instead apply inverse reinforcement learning (IRL) to keystroke data, modeling each keystroke as a discrete choice over typing speed and recovering, per subject, an interpretable reward function that explains their observed timing behavior. To our knowledge this is the first application of IRL to keystroke dynamics. On the public neuroQWERTY MIT-CSXPD dataset (85 subjects, 42 with PD), an initial four-parameter reward decomposition (speed, effort, smoothness, hand-alternation cost) was found to suffer severe feature collinearity between two terms ($r=1.000$ in typical contexts); we diagnose and correct this, yielding an identifiable three-parameter model. The recovered speed-preference weight correlates with UPDRS-III severity at $r=-0.607$ ($p<0.001$, $n=42$), replicates independently across two sub-cohorts, is stable across nine sensitivity configurations, and retains a statistically significant contribution beyond raw typing speed alone (incremental $R^2$ from 0.194 to 0.338, $p=0.006$). Two other recovered weights (consistency, hand-alternation) did not survive confound checks and are reported as negative results. We document two implementation bugs found during adversarial code review (session-boundary contamination, a rolling-window data leakage) and show the headline result is materially unchanged after fixing both. We discuss this result in the context of a literature where reported accuracies vary widely between studies (pooled AUC 0.85, I^2=94% in a 2022 meta-analysis), and argue that the validation process itself, not only the correlation coefficient, is part of the contribution.