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.

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.

Objective To study the clinical efficacy of computer-assisted stereotactic brain transplantation of human retinal pigment epithelium (hRPE) cells into the patients with Parkinson disease (PD). Methods Under the guidance of computed X-ray tomography and magnetic resonance imaging image mergence, 4 × 106 hRPE cells were transplanted into the putamen and ventriculus laterlis of 17 cases of PD by stereotactic surgery. The transplantation sites were contralateral to the side of main symptoms and signs. The curative efficacy were observed at the 7th day, 1st month, and 3rd month after the transplantation. Results The contralateral symptoms were ameliorated continuously after the transplantation. Three months after the surgery, the total effective rate of cell transplantation was 88. 2 %, and 82. 4 % of the cases got significant improvement. The cases that got ipsilateral improvement soon after the surgery gave a total effective rate as high as 88. 2 % at the 3rd month during follow-up period, and 64. 7% among these cases improved significantly. Only a minority of cases had transient dizziness and hemiparesis, but the duration was short. Conclusion The therapy, computer-assisted stereotactic transplantation of hRPE ceils in the treatment of PD, is safe and efficient.