Prediction of exhaustion threshold in aerobic exercise - Zulkifli, Ahmad @ Manap (2021)
Physical exhaustion is an extreme state of fatigue suffered due to overstrain in physical activity. The worst thing that might happen due to exhaustion is sudden death. The risk of death is higher among two contradicting groups based on the amount of exercise namely sedentary people and elite athletes. However, the lack of awareness in identifying exhaustion level is the main reason for this problem. Therefore, the objective of this study was to develop a wearable device that can be used to generate prediction model for the level of exhaustion threshold based on ECG and EMG signals using the artificial neural network (ANN) system. A total of 30 healthy men (age: 23 ± 4 years old) with a sedentary lifestyle were selected for this study. The subjects were required to perform the fatigue-induced exercise by running on the treadmill for a maximum of 20 minutes. The speed intensity of the treadmill was increased every two minutes from 4km/h to l 2km/h. A wearable device prototype was developed to measure the ECG and EMG signals during the test on treadmill. The device was tested and validated using the commercial devices available in the market. The maximum threshold features from both ECG and EMG signals were compared for each stage according to the rating of perceived exertion (RPE) using the Borg scale and Edward's method. Then, the measurement features were classified based on the level of exhaustion from training, validation, and test random samples using the machine learning model of ANN. Significant changes in heart rate and blood pressure were observed during exercise, whereas body temperature did not show any distinguishable impact. The developed wearable device for the ECG and EMG measurements were successfully validated where the results were consistent with less than 5% errors. The exhaustion condition can be detected based on the low HRV distribution on 0.32 R-R intervals. The EMG power spectrum for median frequency shifted from high to low frequency by 33% reduction. The predictive model that combined the ECG and EMG feature resulted in a better performance compared to the single evaluation with 91.02% accuracy. In conclusion, this study will benefit in determining exhaustion threshold during aerobic exercise, thus, reducing the risk of sudden death. v