Identification of motor progression in Parkinson's disease using wearable sensors and machine learning.

ABSTRACT: Wearable devices offer the potential to track motor symptoms in neurological disorders. Kinematic data used together with machine learning algorithms can accurately identify people living with movement disorders and the severity of their motor symptoms. In this study we aimed to establish whether a combination of wearable sensor data and machine learning algorithms with automatic feature selection can estimate the clinical rating scale and whether it is possible to monitor the motor symptom progression longitudinally, for people with Parkinson's Disease. Seventy-four patients visited the lab seven times at 3-month intervals. Their walking (2-minutes) and postural sway (30-seconds,eyes-closed) were recorded using six Inertial Measurement Unit sensors. Simple linear regression and Random For

SUBMITTER: Sotirakis C 

PROVIDER: S-EPMC10560243 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature