Engineers meet clinicians: augmenting Parkinson's disease patients to gather information for gait rehabilitation

Abstract

Many people with Parkinson's disease suffer from freezing of gait, a debilitating temporary inability to pursue walking. Rehabilitation with wearable technology is promising. State of the art approaches face difficulties in providing the needed bio-feedback with a sufficient low-latency and high accuracy, as they rely solely on the crude analysis of movement patterns allowed by commercial motion sensors. Yet the medical literature hints at more sophisticated approaches. In this work we present our first step to address this with a rich multimodal approach combining physical and physiological sensors. We present the experimental recordings including 35 motion and 3 physiological sensors we conducted on 18 patients, collecting 23 hours of data. We provide best practices to ensure a robust data collection that considers real requirements for real world patients. To this end we show evidence from a user questionnaire that the system is low-invasive and that a multimodal view can leverage cross modal correlations for detection or even prediction of gait freeze episodes.