ReBoot: A SMART SHOE SYSTEM FOR IN-HOME PARKINSON’S MOTOR ASSESSMENTS

Dharma Rane, Jose Miguel Canton Leal, Vignesh Ravichandran, Jeremy Gervais, Anne-Marie Dupre, Christine Clarkin, Susan D’Andrea, Kunal Mankodiya, Dhaval Solanki

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Keywords: Parkinson’s Disease, toe-tapping, leg-agility, Wearable Sensors, In-home Monitoring, Motor Assessment

TL;DR: Use of Sensored shoes for characterizing task performance before and after medication of Individuals with Parkinsons disease

Abstract: Parkinson’s Disease (PD) is a progressive neurodegenerative disorder characterized by both motor and non-motor symptoms, requiring frequent, objective monitoring to optimize clinical management. Traditional in-clinic assessments are limited by infrequent evaluations and subjective ratings. To address this, we present ReBoot, a wearable system designed for in-home, quantitative assessment of lower-limb motor symptoms in persons with PD (PwPD). The ReBoot system integrates force sensors and inertial measurement units (IMUs) into a commercial outsole, interfaced with a user-friendly Raspberry Pi tablet application for guided task execution and data collection. We validated ReBoot’s sensor accuracy against a gold-standard XSens IMU system in ten healthy participants across standard motor tasks (toe tapping and leg agility), showing agreement in key features such as peak amplitude and inter-peak intervals. Subsequently, we conducted a 10-day feasibility study with three PwPD, assessing task performance in ON and OFF medication states within home environments. Analysis revealed that ReBoot could reliably capture medication-related motor fluctuations, with leg agility metrics showing greater sensitivity to dopaminergic states compared to toe tapping. Our results support the feasibility of ReBoot as a low-cost, scalable alternative to lab-based assessments for continuous motor monitoring in PD. These findings highlight ReBoot’s potential to complement existing clinical evaluations, inform personalized treatment strategies, and enable remote symptom tracking, thereby contributing to more responsive and data-driven PD care.

Track: 1. Digital Health Solutions (i.e. sensors and algorithms) for diagnosis, progress, and self-management

NominateReviewer: Dharma Rane ([e-mail])

Submission Number: 104