Multisign Health Monitoring Technology of Athletes Based on Artificial Intelligence

Abstract

In order to improve the monitoring efficiency of physical fitness characteristics of athletes in training state, this paper proposes the research on athletes’ multisign health monitoring technology based on artificial intelligence sensor technology. Taking six excellent boxers as the experimental objects, four weeks of heavy load training and 10 training courses per week were arranged from six weeks before the competition to two weeks before the competition. The 4-week training is divided into two stages. From the first week to the fourth week, the training amount remains unchanged and the training intensity increases. The artificial intelligence sensor technology is used to design the athlete training state pulse test system. The athlete training state pulse sensor is responsible for collecting the athlete training state pulse data. The athlete pulse sensor in this system adopts zn17-kg200 pulse vibration sensor as the pulse sampling sensor. After 4 weeks of heavy load training, before and after adjusting for rest, venous blood was taken fasting on the next morning. White blood cells (WBC), red blood cells (RBC), hemoglobin (hemoglobin, Hb), testosterone (hemoglobin, H), cortisol (cortisol), and testosterone/cortisol ratio were measured. The experimental results showed that T and T / C of athletes were significantly lower than those before training ( P < 0.01 ), 37.99% and 52.69%, respectively. C and morning pulse were significantly higher than those before training ( P < 0.05 ), with an increase range of 32.39% and 20.39%. There was no statistical significance in the changes of WBC, RBC, and Hb. HRV indexes lnLF and LF/HF were significantly higher than those before training. Athletes carry the designed portable athlete training state pulse test for heavy load training and display the current training state pulse data of each athlete in real time. The experimental data show that the system designed in this paper can monitor the pulse information of athletes in the training state in real time. The monitoring has good real-time result and accuracy and can realize the abnormal pulse alarm, which has a certain practical value.