Implicit Detection of Motor Impairment in Parkinson's Disease from Everyday Smartphone Interactions.

In this work, we explored the feasibility and accuracy of detecting motor impairment in Parkinson's disease (PD) via implicitly sensing and analyzing users' everyday interactions with their smartphones. Through a 42 subjects study, our approach achieved an overall accuracy of 88.1% (90.0%/86.4% sensitivity/specificity) in discriminating PD subjects from age-matched healthy controls. The performance was comparable to the alternating-finger-tapping (AFT) test, a well-established PD motor test in clinical settings. We believe that the implicit and transparent nature of our approach can enable and inspire rich design opportunities of ubiquitous, objective, and convenient systems for PD diagnosis as well as post-diagnosis monitoring.