PERFECT: Personalized Exercise Recommendation Framework and architECTure

Background There are indisputable health benefits to physical activity (PA). By collecting and displaying individual exercise behaviors via wearable trackers, the Internet of Things (IoT) and mobile health (mHealth) have made it possible to correlate users’ physiological data and daily activity information with their fitness requirements. Objective This study aimed to recommend personalized exercise to non-pregnant subjects to increase their physical activity level. Methods We developed smartphone and smartwatch applications to collect, monitor, and recommend exercises using a contextual multi-arm bandit framework. Twenty female college students were recruited to test this mHealth exercise program. Results Our findings indicated an increase in daily exercise duration ( P < .001), with average satisfaction scores for the walking and recommendation system components of 4.31 (0.60) and 3.69 (0.95), respectively, on a scale of 1 to 5. In addition, participants’ confidence in their capacity to complete the suggested walking exercises safely and the study’s ability to satisfy their needs for physical activity both received average scores of over 4. Conclusions A new era of mHealth systems has been ushered in by developments in the Internet of Things and wearable devices. Personalization of physical activity recommendations using such wearables has the potential to improve user engagement and performance. In this paper, we presented an exercise recommendation system based on reinforcement learning that uses biomarkers and the user’s context to recommend a unique walking exercise that enhances the user’s aerobic capacity.