Heart rate is the most common index to directly monitor the level of physical stress by comparing the subject's heart rate with an appropriate "target heart rate" during exercise. However, heart rate only reveals the cardiac rhythm of the complex cardiovascular changes that take place during exercise. It is essential to get the dynamic response of the heart to exercise with various indices instead of only one single measurement. Based on the rest-workload alternating pattern, this paper screens the sensitive indices of exercise load from electrocardiogram (ECG) rhythm and waveform, including 4 time domain indices and 4 frequency domain indices of heart rate variability (HRV), 3 indices of waveform similarity and 2 indices of high frequency noise. In conclusion, RR interval (heart rate) is a reliable index for the realtime monitoring of exercise intensity, which has strong linear correlation with load intensity. The ECG waveform similarity and HRV indices are useful for the evaluation of exercise load.
Exercise intervention is an important non-pharmacological intervention for various diseases, and establishing precise exercise load assessment techniques can improve the quality of exercise intervention and the efficiency of disease prevention and control. Based on data collection from wearable devices, this study conducts nonlinear optimization and empirical verification of the original "Fitness-Fatigue Model". By constructing a time-varying attenuation function and specific coefficients, this study develops an optimized mathematical model that reflects the nonlinear characteristics of training responses. Thirteen participants underwent 12 weeks of moderate-intensity continuous cycling, three times per week. For each training session, external load (actual work done) and internal load (heart rate variability index) data were collected for each individual to conduct a performance comparison between the optimized model and the original model. The results show that the optimized model demonstrates a significantly improved overall goodness of fit and superior predictive ability. In summary, the findings of this study can support dynamic adjustments to participants' training programs and aid in the prevention and control of chronic diseases.