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.
Introduction: The traditional lower extremity muscle strength training consists mainly of resistance training, where training intensity is gradually increased, targeting strength gain. Objective: Study the effect of different vibration frequencies on muscle strength training of tennis players' knee joints. Methods: Using PHYSIO-PLATE vibration training platform, tennis players of a Beijing team were subjected to different frequencies of strength training with vibrational stimulation; after eight weeks of systematic strength training, the vibration frequencies were 30Hz and 45Hz, with amplitude of 7mm. Results: After the experiment, the relative peak torque and total work of the knee extensor muscles in subjects in groups I and II were significantly improved (P<0.05), generating a significant increase in rapid maximal power start. Conclusion: The vibrational stimulation addition to muscle strength training can effectively enhance its effect, including characteristics such as maximal strength, rapid strength, and muscular endurance with a relatively small load. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.
A traffic signal control system based on parallel simulation technology is designed in this article. Based on the second-level acquisition of intersection data, this system builds a control algorithm matching rule base based on offline algorithm adaptive analysis. In addition, the system can select the signal control algorithm that matches the real-time traffic status online, and realize the online evaluation of the control algorithm through the online parallel simulation and control algorithm evaluation platform, and provide a decision basis for further implementation of the algorithm. This system improves some existing deficiencies in signal control systems, and also provides decision support for the application of complex control algorithms. The system is applied to the control of actual signalized intersections. Practice shows that the system can better adapt to the actual situation of signalized intersections in District, improve the capacity of intersections and the efficiency of the entire road network.
In this paper, multiple sensors are used to track human physiological parameters during physical exercise, and data information fusion technology is used to extract useful information for monitoring and analyzing the effects of physical exercise. This paper explores the interaction and developmental dynamics of multisensor information fusion technology and physical exercise data monitoring based on the interrelationship and interpenetration between the two. The design ideas and principles that should be followed for the software designed in this study are discussed from the perspective of the portable design of measurement instruments and the perspective of multisensor information fusion, and then, the overall architecture and each functional module are studied to propose a scientific and reasonable design model. The general methodological model to be followed for the development of this resource is designed, and the basic development process of the model is explained and discussed, especially the requirement analysis and structural design, and how to build the development environment are explained in detail; secondly, based on the course unit development process in this model, we clarify the limitations of the system through meticulous analysis of the measurement results, which provides a solid foundation for the next step of system optimization. Finally, with a focus on future development, we elaborate on the potential possible role and development trend of multisensor information fusion in the future period. In this paper, we propose to apply the multisensor data fusion algorithm to the monitoring, analysis, and evaluation of the effect of physical exercise, by collecting multiple human physiological parameters during physical exercise through multiple sensors and performing data fusion processing on the collected physiological parameters to finally evaluate the effect of physical exercise.
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