Wearable sensors for Parkinson's disease: which data are worth collecting for training symptom detection models.

ABSTRACT: Machine learning algorithms that use data streams captured from soft wearable sensors have the potential to automatically detect PD symptoms and inform clinicians about the progression of disease. However, these algorithms must be trained with annotated data from clinical experts who can recognize symptoms, and collecting such data are costly. Understanding how many sensors and how much labeled data are required is key to successfully deploying these models outside of the clinic. Here we recorded movement data using 6 flexible wearable sensors in 20 individuals with PD over the course of multiple clinical assessments conducted on 1 day and repeated 2 weeks later. Participants performed 13 common tasks, such as walking or typing, and a clinician rated the severity of symptoms (bradykinesia

SUBMITTER: Lonini L 

PROVIDER: S-EPMC6550186 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature