A Simple Yet Effective Hand Pose Tremor Classification Algorithm To Diagnosis Parkinsons Disease

Parkinsons disease (PD) is a brain disorder that causes unintended or uncontrollable movements. The diagnosis of the disease mainly relies on clinical test rather than a definite medical test, and the diagnostic accuracy is only about 80%. Thus, an efficient and explainable automatic PD diagnosis system is valuable for supporting clinicians with more robust diagnostic decision-making. We present a novel way to classify Parkinsons disease from RGB videos, which is contactless and accurate. Our new algorithm is SimpleHandFormer, which only requires non-intrusive RGB-video recordings of the candidates as input. Our algorithm is motivated by the observation that tremors (shaking movement) will cause the hand keypoint’s location changes, and focusing on the change of the keypoints would improve the performance. For the first time, we propose to use a novel frame–keypoint different attention to effectively detect tremors in the human hands. This design aids in improving both binary classification performance and algorithm explanation. Experimental results show that our system outperforms state-of-the-arts by achieving a balanced accuracy of 93.9% and an F1-score of 92.6% in classifying Parkinson’s Disease.