Heart rate monitoring of physical fitness training load based on wavelet transform

The detection of fluctuations is a crucial step in the process of isolating heart rate oscillation signals. This is necessary to circumvent the issue wherein a variety of auditory signals will invariably interfere with the collection of centre velocity signals during physical activity, which has a significant impact on the detection efficiency of automated testing. To obtain heart rate fluctuation signals that are more precise, the paper proposes a method of R-wave identification that is based on heartbeat conversion while the athlete is exercising. The proposed model relies heavily on the technique of wave analysis to filter out the background noise of the heartbeat when the athlete exercises and isolates the R-wave signal of the heartbeat from the background noise. The proposed algorithm is evaluated in terms of noise interference that removes approximately 90% of noise, the heart rate data which is obtained after the noise is normally distributed, the average detection rate of the R-wave algorithm is 98.65%. It has been demonstrated that the detection accuracy of the proposed algorithm is sufficient to meet the requirements of generating heart rate oscillatory signals.