The Rehabilitation Training Simulation of High Difficulty Movement and Sports Strain Site Based on Big Data | Zendy

Xiaojie Zhang | Zendy; Zhengda Ma | Zendy; Sun Yong-ming | Zendy; Yanle Hu | Zendy

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The Rehabilitation Training Simulation of High Difficulty Movement and Sports Strain Site Based on Big Data

Author(s) -

Xiaojie Zhang,

Zhengda Ma,

Sun Yong-ming,

Yanle Hu

Publication year - 2021

Publication title -

journal of healthcare engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.509

H-Index - 29

eISSN - 2040-2309

pISSN - 2040-2295

DOI - 10.1155/2021/2911025

Subject(s) - rehabilitation , artificial neural network , athletes , conformity , computer science , physical medicine and rehabilitation , training (meteorology) , radial basis function , simulation , artificial intelligence , physical therapy , psychology , medicine , social psychology , physics , meteorology

We study the rehabilitation training of damaged parts of ice and snow sports clock and ensure the physical safety of athletes. The results show that the RBF neural network updates the center, weight, and width of the radial basis function, and the predicted maximum compliance is 99%, and the minimum compliance is 93%. After many analysis times, the prediction results show that the difference between the predicted degree of conformity and the actual results is less than 8%. The RBF neural network is trained according to the risk database of sports injury, and the RBF neural network will output corresponding values to realize sports injury estimation. The experimental results show that the designed model has high precision and efficiency.

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