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 requir...
详细信息
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 andspatial-temporal 3d hand pose estimation to capture and evaluate Pd patients' 3dhand 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-based3d Pdhanddataset 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-worlddata, 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://***/ZhilinGuo/ST-A2J.
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