Mobile Application Framework for Monitoring Target Heart Rate Zone During Physical Exercise Using Deep Learning

As excessive physical exercise may increase the risk of injury, causes fatigue, and may lead to death, an athlete's performance and health must be monitored. Monitoring the intensity of training using Rating of Perceived Exertion (RPE) method during physical exercise has been widely used by athletes and personal trainers in order to understand the effects of training intensity on their health and performance. In relation to this, the intensity of the exercise can be determined using the RPE method and athlete's Target Heart Rate (THR) zone. Therefore, this study proposes a mobile application to monitor the THR zone of athletes automatically during physical exercise sessions using the Faster Region Based Convolutional Neural Network (R-CNN) algorithm. The algorithm has been widely used in facial recognition and has a high recognition rate and accuracy. This study implements a modified waterfall model as the methodology. The developed application will benefit athletes and trainers as they will gain immediate feedback during their physical exercise sessions.