Ensuring athlete physical fitness using Cyber-Physical Systems (CPS) in training environments.

Background: Sports have been a fundamental component of any culture and legacy for centuries. Athletes are widely regarded as a source of national pride, and their physical well-being is deemed to be of paramount significance. The attainment of optimal performance and injury prevention in athletes is contingent upon physical fitness. Technology integration has implemented Cyber-Physical Systems (CPS) to augment the athletic training milieu.

Objective: The present study introduces an approach for assessing athlete physical fitness in training environments: the Internet of Things (IoT) and CPS-based Physical Fitness Evaluation Method (IoT-CPS-PFEM).

Methods: The IoT-CPS-PFEM employs a range of IoT-connected sensors and devices to observe and assess the physical fitness of athletes. The proposed methodology gathers information on diverse fitness parameters, including heart rate, body temperature, and oxygen saturation. It employs machine learning algorithms to scrutinize and furnish feedback on the athlete's physical fitness status.

Results: The simulation findings illustrate the efficacy of the proposed IoT-CPS-PFEM in identifying the physical fitness levels of athletes, with an average precision of 93%. The method under consideration aims to tackle the existing obstacles of conventional physical fitness assessment techniques, including imprecisions, time lags, and manual data-gathering requirements. The approach of IoT-CPS-PFEM provides the benefits of real-time monitoring, precision, and automation, thereby enhancing an athlete's physical fitness and overall performance to a considerable extent.

Conclusion: The research findings suggest that the implementation of IoT-CPS-PFEM can significantly impact the physical fitness of athletes and enhance the performance of the Indian sports industry in global competitions.