Implementation of Photoplethysmography Measurement System Based on Convolution Neural Network for Personalized Exercise Intensity

With increasing interest in health, many people are exercising to lose weight, prevent disease, and improve cardiorespiratory function. For effective exercise, users should proceed with appropriate intensity depending on their physical strength. The system implemented in this paper classifies exercise intensity according to PPG signal using CNN training model for objective exercise intensity classification. The PPG signal was measured after exercise through the PPG sensor, and the training data set was constructed that based on the interval between P-peaks. The training data set is trained on the CNN model to classify the three exercise intensity according to the PPG signal. In order to analyze the accuracy of the implemented CNN training model, the performance evaluation of the classification evaluation metrics and the exercise intensity classification monitoring system was performed. First, the performance evaluation of the CNN model for classifying exercise intensity was conducted. In the performance evaluation, the classification evaluation metrics was calculated according to the training result, the recall rate representing the percentage of successful prediction among the actual correct answers, the precision representing the actual correct answer rate among the predicted data, and the F 1 score representing the harmonic average of recall and precision were confirmed. As a result of CNN training model classification evaluation metrics, it was the accuracy was 99.3%, the recall rate was 99.9%, the precision was 99.8%, and the F 1 score was 99.4%. Second, to evaluate the performance of the exercise intensity classification monitoring system, jump rope experiment was conducted with 5 subjects. The experiment measured PPG at the end of each set after low, moderate, and high intensity jump rope. The classification accuracy was analyzed by entering the measured PPG data into the CNN model 50 times each. As a result of the experiment, the accuracy of low intensity was 98%, moderate intensity was 93.6%, and high intensity was 97.6%, confirming a total accuracy of 96.4%. Some errors are thought to have occurred due to the fact that the data located at the boundary line between the exercise intensity was classified incorrectly. In future studies, we would like to conduct a study of exercise intensity monitoring system that can be applied to various exercises by measuring acceleration signals for each exercise together.