Fatigue gait recognition of athletes based on fish swarm algorithm
- VernacularTitle:基于鱼群算法对运动者疲劳步态的动作识别
- Author:
Jian ZHANG
1
;
Feng CAI
;
Tingwen LI
;
Pengbo REN
Author Information
- Publication Type:Journal Article
- Keywords: fish swarm algorithm; athletes; convolutional neural network; fatigue gait; gait energy diagram; convolutional kernel; kinetic energy distribution; normalized autocorrelation function; engineered tissue construction
- From: Chinese Journal of Tissue Engineering Research 2025;29(30):6489-6498
- CountryChina
- Language:Chinese
- Abstract: BACKGROUND:Gait movements are one of the important characteristics exhibited by athletes during exercise,reflecting their physical condition and athletic ability.In a state of fatigue,athletes may exhibit abnormal gait movements,such as reduced stride and body shaking,which can cause harm to their bodies.OBJECTIVE:To promote technological progress in the field of sports science by applying advanced algorithms and data analysis techniques to the training practice of athletes,so as to further improve the recognition accuracy of gait movements under sports fatigue.METHODS:A gait recognition method for athletes in fatigue state was based on fish swarm algorithm.By utilizing the normalized autocorrelation function and the principle of motion energy distribution,a single cycle gait energy map of athletes was obtained.Singular value decomposition was used to transform the image to highlight visual differences,generating a gait energy map of athletes.A convolutional neural network was used to construct a gait action recognition model,and the parameters of the model were solved using the fish swarm algorithm to improve the accuracy and efficiency of fatigue gait action recognition.RESULTS AND CONCLUSION:(1)The fish swarm algorithm had a small loss value in gait action recognition,and could accurately and quickly identify the gait actions of athletes,and dynamically monitor their physical fatigue.(2)The research on fatigue gait recognition of athletes based on fish swarm algorithm could effectively identify the gait movements of athletes in fatigue state and achieve accurate capture of subtle gait changes.(3)The system stability of this method is good,which can reduce the volatility of experimental test results and improve recognition efficiency,can more effectively manage sports fatigue and prevent sports injuries.In addition,when the gait characteristics of normal people change significantly,the system can give an early warning,indicating that the individual may be in a state of fatigue and need to rest or adjust the intensity of activity.
