Heel-Tapping-Based Parkinson's Disease Progression Monitoring Using Smart Insoles

Parkinson's Disease (PD) is a progressive neurodegenerative disorder marked by motor and non-motor symptoms, which complicate both diagnosis and disease monitoring. Traditional subjective assessments, such as the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) and Hoehn and Yahr scale, often fall short of capturing subtle motor variations across PD stages as well as require expertise and thus the accessibility is limited in daily life. In this study, we evaluated the feasibility of using in-phase (IP) and anti-phase (AP) heel- and toe-tapping as biomarkers to monitor PD progression objectively. Motion data were collected from 40 participants (28 patients with PD, 12 age-matched healthy controls) using a pair of smart insoles with embedded accelerometers. Our results show that the heel IP yielded the largest number of stage-discriminative features, with 96 out of 112 extracted features showing significant differences across PD stages and achieved up to 92% classification accuracy using supervised machine learning classifiers, particularly Random Forest and Neural Network. Clustering analyses (KMeans and Gaussian Mixture Model) further supported stage-specific grouping patterns, with chi-square significance p <0.0001. These findings suggest that heel IP can serve as a sensitive and practical assessment for PD stage classification. Paired with smart insoles and a tapping game, this can be used as an assistive tool to monitor PD disease progression for at home monitoring in daily life.