Development of digital biomarkers for resting tremor and bradykinesia using a wrist-worn wearable device

TLDR

Objective assessment of Parkinson’s disease symptoms in daily life can improve disease management and accelerate therapy development, yet current methods rely on multiple devices or prescribed tasks and lack open, continuously valid approaches for free‑living monitoring. The study aims to develop a minimal‑sensor system that continuously captures clinically meaningful motor symptom severity in free‑living conditions. The authors use a single wrist‑worn accelerometer and a hierarchical heuristic‑machine‑learning pipeline to process raw data epochs for continuous tremor and bradykinesia monitoring. Sensor‑derived continuous measures of resting tremor and bradykinesia show good to strong agreement with clinical assessments and can discriminate treatment‑related motor state changes.

Abstract

Abstract Objective assessment of Parkinson’s disease symptoms during daily life can help improve disease management and accelerate the development of new therapies. However, many current approaches require the use of multiple devices, or performance of prescribed motor activities, which makes them ill-suited for free-living conditions. Furthermore, there is a lack of open methods that have demonstrated both criterion and discriminative validity for continuous objective assessment of motor symptoms in this population. Hence, there is a need for systems that can reduce patient burden by using a minimal sensor setup while continuously capturing clinically meaningful measures of motor symptom severity under free-living conditions. We propose a method that sequentially processes epochs of raw sensor data from a single wrist-worn accelerometer by using heuristic and machine learning models in a hierarchical framework to provide continuous monitoring of tremor and bradykinesia. Results show that sensor derived continuous measures of resting tremor and bradykinesia achieve good to strong agreement with clinical assessment of symptom severity and are able to discriminate between treatment-related changes in motor states.