Task-specific machine learning system for automatic assessment of multi-limb bradykinesia in Parkinson's disease

Bradykinesia, a cardinal physical dysfunction of Parkinson's disease (PD), is generally evaluated by Section III of the Movement Disorders Society-sponsored revision of the unified Parkinson's disease rating scale (MDS-UPDRS). The evaluation process requires the supervision of clinicians; therefore, the results may be subjective and increase clinicians' workload.

To compensate for these drawbacks, this study proposes a task-specific machine learning system that incorporates wearable sensors to automatically monitor bradykinesia. Initially, this study enhanced the peak detection algorithm by considering specific motion characteristics, such as movement amplitude, to make it more adaptable to individualized signals. Furthermore, a task-specific score prediction system was proposed, incorporating the optimal sensor placement positions and appropriate score prediction algorithms. Specifically, seven machine learning models and four ensemble methods were explored for each of the five MDS-UPDRS III tasks.

The system was tested in 21 patients and eight control individuals. The performance of the system was evaluated under three scenarios: precise prediction (0 vs. 1 vs. 2 vs. 3), abnormal/normal [0 vs. (1, 2, 3)], and normal-moderate/severe [(0,1,2) vs. 3], with the highest average F1 score reaching 0.8722 and the lowest total root mean square error at 0.3214 among tasks in critical status prediction. Furthermore, this study, through statistical analysis, suggested specific scoring features tailored to each task.

This study demonstrated the feasibility of accurately and automatically monitoring bradykinesia of PD.