Automatic Detection Pipeline for Accessing the Motor Severity of Parkinson’s Disease in Finger Tapping and Postural Stability-Reference-Cited by-同舟云学术

Automatic Detection Pipeline for Accessing the Motor Severity of Parkinson’s Disease in Finger Tapping and Postural Stability

Published:2022 Issue: Volume:10 Page:66961-66973
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Yang Ning¹^ORCID,Liu De-Feng²,Liu Tao³^ORCID,Han Tianyuan³,Zhang Pingyue³^ORCID,Xu Xuenan³^ORCID,Lou Siyu³^ORCID,Liu Huan-Guang²,Yang An-Chao²,Dong Cheng⁴,Vai Mang I.¹,Pun Sio Hang⁵^ORCID,Zhang Jian-Guo²^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau, China

2. Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China

3. MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China

4. School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai, China

5. State Key Laboratory of Analog and Mixed-Signal VLSI, University of Macau, Taipa, Macau, China

Funder

National Key Research and Development Program of China

Shenzhen-Hong Kong-Macau S&T Program (Category C) of SZSTI

University of Macau

The Science and Technology Development Fund, Macau SAR

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09796513.pdf?arnumber=9796513

Reference50 articles.

1. Realtime and Robust Hand Tracking from Depth

2. Latent Regression Forest: Structured Estimation of 3D Articulated Hand Posture

3. MODEC: Multimodal Decomposable Models for Human Pose Estimation

4. Vision-Based Finger Tapping Test in Patients With Parkinson’s Disease via Spatial-Temporal 3D Hand Pose Estimation

5. Automated assessment of Parkinsonian finger-tapping tests through a vision-based fine-grained classification model

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