An Improved Neural Network Model for Early Detection of Joint Injuries in Tai Chi Sports

Medical experts and academics are progressively becoming aware of knee injuries faced by tai chi practitioners in China. This occurs when the knee is bent with weight on the foot turned during Tai Chi. We propose an enhanced convolutional neural network (CNN) technique for early warning of joint inj...

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Bibliographic Details
Published in:Mobile information systems 2022-07, Vol.2022, p.1-8
Main Authors: Shao, Zhe, Zhong, Yuanyuan, Yu, Zhida, Chu, Xingping
Format: Article
Language:English
Subjects:
Artificial neural networks
Cultural heritage
Datasets
Diagnostic systems
Exercise
Fault diagnosis
Feature extraction
Injuries
Joint and ligament injuries
Joints (anatomy)
Knee
Martial arts
Middle age
Physical fitness
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Summary:Medical experts and academics are progressively becoming aware of knee injuries faced by tai chi practitioners in China. This occurs when the knee is bent with weight on the foot turned during Tai Chi. We propose an enhanced convolutional neural network (CNN) technique for early warning of joint injury risk during Tai Chi exercise in this research. This improved neural network approach can detect the risk of knee joint injury in practitioners early on to aid in early precaution and treatment. A multiscale feature extraction module is developed by performing several scales of convolutional layer extraction on the input data features and then combining the results to maximize the amount of feature information included in the extracted joint data. The results revealed that in experiments on a Taiwanese Tai Chi community dataset, the proposed method had an average diagnostic accuracy greater than 90 percent, significantly higher than the average diagnostic accuracy of the comparison methods on the dataset.
ISSN:1574-017X
1875-905X
DOI:10.1155/2022/8476803