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Automatic Detection Pipeline for Accessing the Motor Severity of Parkinson’s Disease in Finger Tapping and Postural Stability
Ning Yang; De-Feng Liu; Tao Liu; Tianyuan Han; Pingyue Zhang; Xuenan Xu; Siyu Lou; Huan-guang Liu; An-Chao Yang; Cheng Dong; Mang I Vai; Sio Hang Pun; Jian-Guo Zhang
Source PublicationIEEE Access

Parkinson’s disease (PD) is a nervous disorder that can cause motor impairment. PD severity assessment based on a series of motor movements illustrated in Unified Parkinson’s Disease Rating Scale (UPDRS) is an important part of clinical PD diagnosis. However, the current quantifying method heavily relies on human observation, which is time-consuming and subjective. Therefore, automatic severity estimation stemming from machine learning methods is receiving increasing amount of research attention. However, these advances are still limited by data availability and interpretability. In this paper, we release a large PD motor dataset of over 300 real PD patients collected under doctors’ instructions and propose a pipeline to automatically quantify the motor severity of PD in finger tapping and postural stability. These two selected movements are representative of local and global motor control, exhibiting great clinical importance. The pipeline contains three-stage: pose estimation, domain knowledge extraction, and classifification stage. The pose estimation uses deep-learning-based methods to extract 21 and 17 key points for fifinger tapping and postural stability respectively. The domain knowledge extraction stage extracts several explicit featurespre-defifined by experienced neuro-physicians. Finally, a classififier is trained to infer PD severity under MDS UPDRS. To combine deep-learning-based features from pose estimation and domain features from the expert, the pipeline achieves a better trade-off between the model effificiency and clinical interpretability. Experiments show that our method achieves a micro average f1-score of 88%, 84%, and 84%, respectively on left fifinger tapping, right fifinger tapping, and postural stability, outperforming previous methods by a large margin. In addition, involving expert knowledge in the feature extraction stage greatly improves our model’s interpretability, which is essential in automatic PD detection.

KeywordActivity Recognition Deep Learning Finger Tapping Neural Network Parkinson’s Disease Pose Estimation Postural Stability
URLView the original
Indexed BySCIE
Funding ProjectHardware Time Triggered System for Biomedical Engineering Applications ; Integrated circuits development for closed loop optogenetic neural control withreal time electrode diagnosis
WOS Research AreaComputer Science ; Engineering ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS IDWOS:000818794100001
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionFaculty of Science and Technology
Corresponding AuthorNing Yang; Sio Hang Pun; Jian-Guo Zhang
Affiliation1.University of Macau
2.Capital Medical University
3.Shanghai Jiao Tong University
4.Shanghai Jiao Tong University
5.Shanghai Jiao Tong University
6.Shanghai Jiao Tong University
7.Shanghai Jiao Tong University
8.Capital Medical University
9.Capital Medical University
10.Jinan University
11.University of Macau
12.University of Macau
13.Capital Medical University
Recommended Citation
GB/T 7714
Ning Yang,De-Feng Liu,Tao Liu,et al. Automatic Detection Pipeline for Accessing the Motor Severity of Parkinson’s Disease in Finger Tapping and Postural Stability[J]. IEEE Access,2022,10:66961.
APA Ning Yang,De-Feng Liu,Tao Liu,Tianyuan Han,Pingyue Zhang,Xuenan Xu,Siyu Lou,Huan-guang Liu,An-Chao Yang,Cheng Dong,Mang I Vai,Sio Hang Pun,&Jian-Guo Zhang.(2022).Automatic Detection Pipeline for Accessing the Motor Severity of Parkinson’s Disease in Finger Tapping and Postural Stability.IEEE Access,10,66961.
MLA Ning Yang,et al."Automatic Detection Pipeline for Accessing the Motor Severity of Parkinson’s Disease in Finger Tapping and Postural Stability".IEEE Access 10(2022):66961.
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