Analysis of Body Behavior Characteristics after Sports Training Based on Convolution Neural Network.

Xinliang Zhou,Shantian Wen

doi:10.1155/2021/7006541

Abstract

The use of artificial intelligence technology to analyze human behavior is one of the key research topics in the world. In order to detect and analyze the characteristics of human body behavior after training, a detection model combined with a convolutional neural network (CNN) is proposed. Firstly, the human skeleton suggestion model is established to analyze the driving mode of the human body in motion. Secondly, the number of layers and neurons in CNN are set according to the skeleton feature map. Then, the output information is classified according to the fatigue degree according to the body state after exercise. Finally, the training and performance test of the model are carried out, and the effect of the body behavior feature detection model in use is analyzed. The results show that the CNN designed in the study shows high accuracy and low loss rate in training and testing and also has high accuracy in the practical application of fatigue degree recognition after human training. According to the subjective evaluation of volunteers, the overall average evaluation is more than 9 points. The above results show that the designed convolution neural network-based detection model of body behavior characteristics after training has good performance and is feasible and practical, which has guiding significance for the design of sports training and training schemes.

Highlights

Body behavior is one of the main characteristics in the process of human activity
Nakandala et al [2] proposed a sensor-based behavior recognition system based on a deep recursive neural network (RNN), which integrates data from ECG, accelerometer, magnetometer, and other individual sensors to identify human behavior
Bakshi [7] proposed a new human activity recognition structure based on multisensor data. e wearable sensor human activity recognition based on the imaging time series was proposed. e image recognition was carried out by computer vision technology. e results showed that the system has better accuracy and F1 value

Summary

Introduction

Body behavior is one of the main characteristics in the process of human activity. With the development of science and technology, the research on human behavior has attracted a lot of researchers’ attention worldwide. e current research methods mainly include attitude perception and action recognition [1]. Bakshi [7] proposed a new human activity recognition structure based on multisensor data. E wearable sensor human activity recognition based on the imaging time series was proposed. Based on CNN, Hur et al [13] proposed an efficient method for human activity identification, which accurately infers the correlation between the signal values of three-dimensional continuous sensors, and through experimental analysis, it is concluded that the accuracy of the method in the test data set is higher than other advanced methods. Teng et al [15] proposed a hierarchical CNN with local loss It can realize human activity recognition using CNN based on local loss in ubiquitous and wearable computing. We can realize the reasonable training mode and improve the training level

Detection of Body Behavior Characteristics after Sports Training Based on CNN

Application Analysis of Monitoring Model for Body Behavior Characteristics

Findings

Conclusion