A Deep-Learning-Based Method for Gaining More Biomechanical Parameters with Fewer Sensors in Fast and Complex Movements-Reference-Cited by-同舟云学术

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A Deep-Learning-Based Method for Gaining More Biomechanical Parameters with Fewer Sensors in Fast and Complex Movements

Published:2022-07-17 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)
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Author:

Wang Ye¹,Shan Gongbing²,Li Hua³,Feng Ruliang¹,Zhang Yilan¹,Li Guanglin¹,Wang Lin¹

Affiliation:

1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences,CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems,Shenzhen,China,518055

2. University of Lethbridge,Department of Kinesiology & Physical Education,Lethbridge,AB,Canada,T1K3M4

3. University of Lethbridge,Department of Mathematics & Computer Science,Lethbridge,AB,Canada,ABT1K3M4

Funder

National Natural Science Foundation of China

Chinese Academy of Sciences

Natural Sciences and Engineering Research Council of Canada

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9872150/9872152/09872240.pdf?arnumber=9872240

Reference17 articles.

1. Obtaining Vital Distances Using Wearable Inertial Measurement Unit for Real-Time, Biomechanical Feedback Training in Hammer-Throw

2. Hammer Throw: A Pilot Study for a Novel Digital-Route for Diagnosing and Improving Its Throw Quality

3. Deep learning;lecun;Nature,2015

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