An Automatic Rating Approach Using Machine Learning and Feature Selection for Finger Tapping in MDS-UPDRS Part III
Yi-Hung Chiu, Tung-Kuan Liu, Chih-Ping Yang, Ching‐Fang Chien, Li‐Min Liou, Lung‐Chang Lin, Huei‐Ping Dong, Chen‐Sen Ouyang · 2025
We propose a machine learning-based approach with feature selection for automatic finger tapping rating in the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) Part III assessment. A total of 160 video clips from 80 participants were used. For each video, 2D hand coordinate sequences were extracted using MediaPipe, followed by movement sequence computation, feature extraction, and selection. Clinicians provided severity scores as the gold standard. A LightGBM regressor was trained and evaluated using the resulting dataset of selected feature vectors and corresponding gold standard scores. Experimental results showed that our method achieved superior performance and demonstrated potential for reliable video-based evaluation.