Vision-Based Finger Tapping Test in Patients With Parkinson's Disease via Spatial-Temporal 3D Hand Pose Estimation

• Published in: IEEE journal of biomedical and health informatics Vol. 26; no. 8; pp. 3848 - 3859
• Main Authors: Guo, Zhilin, Zeng, Weiqi, Yu, Taidong, Xu, Yan, Xiao, Yang, Cao, Xuebing, Cao, Zhiguo
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 3D kinematic feature; Cameras; Deep learning; depth camera; Diseases; Evaluation; Feature extraction; finger tapping test; Hand; Machine learning; MDS-UPDRS; Motor task performance; Movement disorders; Neurodegenerative diseases; Parkinson's disease; Patients; Pose estimation; spatial-temporal 3D hand pose estimation; SVM; Three dimensional motion; Three-dimensional displays; Thumb; Wearable sensors
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2022.3162386

Finger tapping test is crucial for diagnosing Parkinson's Disease (PD), but manual visual evaluations can result in score discrepancy due to clinicians' subjectivity. Moreover, applying wearable sensors requires making physical contact and may hinder PD patient's raw movement patterns. Accordingly, a novel computer-vision approach is proposed using depth camera and spatial-temporal 3D hand pose estimation to capture and evaluate PD patients' 3D hand movement. Within this approach, a temporal encoding module is leveraged to extend A2J's deep learning framework to counter the pose jittering problem, and a pose refinement process is utilized to alleviate dependency on massive data. Additionally, the first vision-based 3D PD hand dataset of 112 hand samples from 48 PD patients and 11 control subjects is constructed, fully annotated by qualified physicians under clinical settings. Testing on this real-world data, this new model achieves 81.2% classification accuracy, even surpassing that of individual clinicians in comparison, fully demonstrating this proposition's effectiveness. The demo video can be accessed at https://github.com/ZhilinGuo/ST-A2J .