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Pose-based Tremor Classification for Parkinson’s Disease Diagnosis from Video

Zhang, Xiatian; Zhang, Haozheng; Shum, Hubert P.H

Pose-based Tremor Classification for Parkinson’s Disease Diagnosis from Video Thumbnail


Haozheng Zhang
PGR Student Doctor of Philosophy


Parkinson’s disease (PD) is a progressive neurodegenerative disorder that results in a variety of motor dysfunction symptoms, including tremors, bradykinesia, rigidity and postural instability. The diagnosis of PD mainly relies on clinical experience rather than a definite medical test, and the diagnostic accuracy is only about 73-84% since it is challenged by the subjective opinions or experiences of different medical experts. Therefore, an efficient and interpretable automatic PD diagnosis system is valuable for supporting clinicians with more robust diagnostic decision-making. To this end, we propose to classify Parkinson’s tremor since it is one of the most predominant symptoms of PD with strong generalizability. Different from other computer-aided time and resourceconsuming Parkinson’s Tremor (PT) classification systems that rely on wearable sensors, we propose SPAPNet, which only requires consumergrade non-intrusive video recording of camera-facing human movements as input to provide undiagnosed patients with low-cost PT classification results as a PD warning sign. For the first time, we propose to use a novel attention module with a lightweight pyramidal channel-squeezing-fusion architecture to extract relevant PT information and filter the noise efficiently. This design aids in improving both classification performance and system interpretability. Experimental results show that our system outperforms state-of-the-arts by achieving a balanced accuracy of 90.9% and an F1-score of 90.6% in classifying PT with the non-PT class.


Zhang, X., Zhang, H., & Shum, H. P. (2022). Pose-based Tremor Classification for Parkinson’s Disease Diagnosis from Video. .

Conference Name MICCAI '22: The 25th International Conference on Medical Image Computing and Computer Assisted Intervention
Conference Location Singapore
Start Date Sep 18, 2022
End Date Sep 22, 2022
Acceptance Date Jun 16, 2022
Online Publication Date Sep 16, 2022
Publication Date 2022
Deposit Date Jul 1, 2022
Publicly Available Date Sep 17, 2023
Pages 489-499
Series Title Lecture Notes in Computer Science
Series ISSN 0302-9743
Public URL


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