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

haozheng zhang, edmond s. l. ho, xiatian zhang and hubert p. h. shum
Proceedings of the 2022 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2022

Citation: 1##

Pose-based Tremor Classification for Parkinson’s Disease Diagnosis from Video
## Citation counts from Google Scholar as of 2022

Abstract

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 resource-consuming Parkinson’s Tremor (PT) classification systems that rely on wearable sensors, we propose SPAPNet, which only requires consumer-grade 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.

Downloads

YouTube

Citations

BibTeX

@inproceedings{zhang22posebased,
 author={Zhang, Haozheng and Ho, Edmond S. L. and Zhang, Xiatian and Shum, Hubert P. H.},
 booktitle={Proceedings of the 2022 International Conference on Medical Image Computing and Computer Assisted Intervention},
 series={MICCAI '22},
 title={Pose-based Tremor Classification for Parkinson’s Disease Diagnosis from Video},
 year={2022},
 pages={489--499},
 numpages={11},
 doi={10.1007/978-3-031-16440-8_47},
 isbn={978-3-031-16439-2},
 location={Singapore, Singapore},
}

RIS

TY  - CONF
AU  - Zhang, Haozheng
AU  - Ho, Edmond S. L.
AU  - Zhang, Xiatian
AU  - Shum, Hubert P. H.
T2  - Proceedings of the 2022 International Conference on Medical Image Computing and Computer Assisted Intervention
TI  - Pose-based Tremor Classification for Parkinson’s Disease Diagnosis from Video
PY  - 2022
SP  - 489
EP  - 499
DO  - 10.1007/978-3-031-16440-8_47
SN  - 978-3-031-16439-2
ER  - 

Plain Text

Haozheng Zhang, Edmond S. L. Ho, Xiatian Zhang and Hubert P. H. Shum, "Pose-based Tremor Classification for Parkinson’s Disease Diagnosis from Video," in MICCAI '22: Proceedings of the 2022 International Conference on Medical Image Computing and Computer Assisted Intervention, pp. 489-499, Singapore, Singapore, 2022.

Similar Research

Haozheng Zhang, Hubert P. H. Shum and Edmond S. L. Ho, "Cerebral Palsy Prediction with Frequency Attention Informed Graph Convolutional Networks", Proceedings of the 2022 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2022
Haozheng Zhang, Edmond S. L. Ho and Hubert P. H. Shum, "CP-AGCN: Pytorch-based Attention Informed Graph Convolutional Network for Identifying Infants at Risk of Cerebral Palsy", Software Impacts (SIMPAC), 2022
Manli Zhu, Qianhui Men, Edmond S. L. Ho, Howard Leung and Hubert P. H. Shum, "Interpreting Deep Learning based Cerebral Palsy Prediction with Channel Attention", Proceedings of the 2021 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), 2021

 

 
 

Last updated on 24 September 2022, RSS Feeds