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

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 models.

RESULTS

Here we show that significant differences in goal-directed movement features exist between the groups. Additionally, several of these features strongly correlate with clinical scores. Classification models accurately distinguish between HD, pHD, and CTR individuals, achieving a balanced accuracy of 67% and a recall of 0.72 for the HD group. Regression models effectively predict clinical scores.

CONCLUSIONS

This study demonstrates the potential of wearable sensors and machine learning to monitor upper limb function in Huntington's disease, offering a tool for early detection, remote monitoring, and assessing treatment efficacy in clinical trials.