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

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. 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. 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. 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. People with Huntington’s disease can have difficulty moving, experiencing involuntary movement of the limbs. This study aimed to better understand how Huntington’s disease affects hand movements and whether wearable devices can be used to monitor this. Individuals with Huntington’s disease, those at risk of it, and healthy participants wore a small wearable device on their wrist for a week to track their hand movements during daily activities. We used advanced computer models to analyze data and predict disease severity. The main finding was that the device could clearly distinguish between people with Huntington’s disease, those at risk, and healthy people, helping to predict symptoms. This research shows that wearable technology could be used to monitor disease and the effect of treatments in the future. Nunes et al. predict Huntington’s disease score by applying deep learning to readings from a wrist-worn sensor obtained over a 7 day period. Differences are seen in goal-directed movement that correlate with clinical score.