Radiology

Pathology

Disease

Population

Motor symptoms

Accelerometer

Operating system

Machine learning

Wearable computer

Engineering

Embedded system

Physical therapy

Artificial intelligence

Operations management

Wrist

Continuous monitoring

Parkinson's disease

Computer science

Activities of daily living

Resting tremor

Medicine

Environmental health

Physical medicine and rehabilitation

npj Digital Medicine

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.

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