Using wearable sensors and machine learning to assess upper limb function in Huntington's disease.

ABSTRACT:

Background

Huntington's disease, a neurodegenerative disorder, impairs both upper and lower limb function, typically assessed in clinical settings. However, wearable sensors offer the opportunity to monitor real-world data that complements clinical assessments, providing a more comprehensive understanding of disease symptoms.

Methods

In this study, we monitor upper limb function in individuals with Huntington's disease (HD, n = 16), prodromal HD (pHD, n = 7), and controls (CTR, n = 16) using a wrist-worn wearable sensor over a 7-day period. Goal-directed hand movements are detected through a deep learning model, and kinematic features of each movement are analyzed. The collected data is used to predict disease groups and clinical scores using statistical and machine learning

SUBMITTER: Nunes AS 

PROVIDER: S-EPMC11861259 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature