Muscle Strength Enhancement and Metabolic Acceleration of College Students in Traditional Archery Programs Based on Functional Training

Abstract

At the present stage, the evaluation of human motor function is mainly semi-quantitative, which only evaluates the overall motor function from the degree of joint mobility and muscle status, but the size of muscle strength cannot be obtained by direct measurement. In this paper, we take the muscle activation degree and joint angle of college students in traditional archery programs based on “functional training” as the experimental objects and establish a muscle strength prediction model based on a generalized dynamic fuzzy neural network (GD-FNN). By analyzing the relationship between the surface electromyographic signals and muscle strength under elbow flexion and extension, we selected suitable parameters as the sample data of the fuzzy neural network and proposed a learning algorithm based on the variable sliding window of the GD-FNN. The predicted muscle force was compared with the desired muscle force of the main flexion and extension muscles through the subjects’ elbow flexion exercise and extension exercise. The normalized root-mean-square error between the predicted and actual muscle strength of the algorithm in this paper is less than 0.2. Compared with the maximal strength test, the peak extensor moments (right) and peak extensor moments (left) of the college athletes before and after the functional training, the mean values increased by 30.6 N.m and 42.39 N.m, respectively. Compared with the metabolism of the students during the ordinary training of 2.74 met based on the “functional training” of traditional archery increased, students’ metabolism to 5.03 met. It shows that functional training is favorable to the muscle strength of traditional archery college students and has a positive effect on the metabolic capacity of the body.

Keywords

• gd-fnn
• muscle strength prediction
• variable sliding window
• electromyographic signal
• metabolism
• 97d60