Role of Sensors Based on Machine Learning Health Monitoring in Athletes' Wearable Heart Rate Monitoring

As domestic athletes and other sports become more and more popular, the health protection of athletes has gradually attracted people's attention, and the heart rate monitoring of machine learning sensors has gradually been understood by people. In order to in-depth study the current status of the role of machine learning sensors in wearable heart rate monitoring, this article uses the new and old sensor comparison method, the field survey method and the human sample test method, collects samples, analyzes the machine learning sensor, and streamlines the algorithm. And create a sensor for wearable heart rate monitoring. In studying the innovation and improvement of the comparison between the new sensor and the old one, this paper uses the shimmer node on the Tiny-OS platform to collect a total of 36 sets of heart rate data of three men and three women in different exercise states, and uses two types of algorithms to calculate the results. It shows that the calculation time of the algorithm proposed in the paper is 0.21, the traditional electrocardiogram (ECG) algorithm is 0.28, and the new algorithm has lower time complexity. Research on the accuracy of the sensor in practical application shows that the recognition rate of riding a bicycle is almost close to 1.00, which fully meets the 0.96 recognition requirement of this system. Although, the single recognition rate of standing still did not reach 0.95. However, the total average recognition rate of the nine sports is 0.971, which is already above 0.96, which proves that the average recognition rate of the monitoring system is above 96%. The system is considered to be a basic design success. It is basically realized that starting from the wearable heart rate monitoring, a machine learning sensor that can be put into large-scale application is designed.