Influence of the Athlete’s Training Physical State Test Based on the Principle of Artificial Intelligence Sensor

Abstract

Artificial intelligence is the study of the laws of human intelligent activities, the construction of artificial systems with certain intelligence, and the study of how to make computers perform tasks that required human intelligence in the past. Basic Theories, Methods, and Techniques. With the maturity of the highly integrated hardware technology, the rapid development of sensor technology provides a material basis for the study of sports states based on smart terminals, and the mature model method theory provides a theoretical research basis for the research. The working principle of the sensor is to convert the specific measured signal into a certain “available signal” according to a certain rule through the sensitive element and the conversion element and output it to meet the requirements of information transmission, processing, recording, display, and control. In order to deeply study the application of artificial intelligence sensor theory in athlete training physical condition testing, this article uses theoretical analysis method, formula image combination method, and real person survey method, collects samples, analyzes artificial intelligence sensor, and streamlines the algorithm. In studying the accuracy of smart sensors on athlete training tests, a total of 9 athletes, 4 females, and 5 males are selected. Each experimenter performed three actions of running, jumping, and squatting 10 times, with an interval of more than 20 s. The results showed that the recognition accuracy of running in this paper was 98.51%, and the recognition accuracy of jumping and squatting was 92.59% and 93.33%; we have achieved more than 92% recognition rate for the three kinds of actions. On further study of the real-time performance of the sensor, the average response time of the algorithm is the average value obtained from 80 experimental records in this paper. The average response time of the algorithm proposed in this paper is within 1.5 s. Since the falling process occurs within 2.1 s, the recognition algorithm proposed in this paper has high real-time performance. It is basically realized that starting from the theory of artificial intelligence sensors, a high-precision sensor that can be applied to athlete training is designed.