IoT-based health monitoring of sports personnel through wearables using machine learning technology - Tanna, Rashmin S.; Vithalani, Chandulal H (2023)
The faster growth and expansion of the Internet of Things (IoT) hold a lot of promise in the healthcare industry. Currently, fitness trackers, wireless technologies, and body sensors in sports significantly impact the daily performance and reliability of healthcare systems. In various groups, from elite athletes to patients, wearable gadgets are becoming increasingly important to assess physiological parameters, promote health, and improve exercise adherence This article aims to identify sports medicine clinics and team performance services and improve the use of technology to assist athletes returning to play in a variety of sports. Machine learning approaches are presented for effective optimization to test and monitor the health of athletes. Wearable sensor data from the health IoT is a rich source of information that machine learning can unlock. The key novelty of this work lies in the integration of IoT, wearable devices, and machine learning algorithms to enable comprehensive and continuous health monitoring of sports personnel. The objective is to enhance athletes' well-being, prevent injuries, optimize training programs, and improve overall performance. Therefore, the wearable sensor-based smartwatch on IoT is introduced in this study for the continual health monitoring system for athletes. The machine learning-based ensemble naïve Bayes classifier (ENBC) is used to predict sportsperson health activity. The result of the study shows that the average accuracy of machine learning-based classification was 98.63%, which is high compared to other conventional methods. The proposed machine learning approach described in this study is undoubtedly the most efficient, reliable, and accurate of the other methods. The use of a smartwatch to monitor a person's athletic health is gaining popularity because it is inexpensive, easy to wear, and in line with consumer psychology.
Keywords: Internet of things; health and wellness; Machine learning