Using Wearable Device Data for Step Measurement On Parkinson’s Disease Population

Music City Center 

Parkinson's disease (PD) is a progressive neurodegenerative disorder with various motor symptoms. Home detection and monitoring of such symptoms prove to be valuable, as it enables more constant monitoring at patient's convenience. Wearable devices equipped with inertial measurement unit (IMU) sensors are particularly essential in objective symptoms progression monitoring at home. Some literatures identify gait features, which characterize a person's walking or running movement, as important predictors for detecting PD symptoms. Such gait features can be derived from IMU signals. In this work, we propose a step measurement methodology using convolutional neural network architecture, which is an integral step in deriving important gait features. With the limited accessibility to such gait features, an open-source step-measurement model that translates raw IMU signals into gait features would be valuable to researchers in Parkinson's disease. We demonstrate the use of the proposed model through the WearGait-PD dataset.

Digital health

Convolutional Neural Network

Parkinson's Disease

Wearable Device

Inertial Measurement Unit

Time-series 

Section on Medical Devices and Diagnostics