Zhou Hongtao, Daud D Maryama Binti Ag
Xi'an University, Xi'an, China.
Faculty of Medicine and Health Sciences, University Malaysia Sabah Kota Kinabalu, Malaysia.
Technol Health Care. 2024;32(4):2599-2618. doi: 10.3233/THC-231435.
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.
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).
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.
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.
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.
几个世纪以来，体育一直是任何文化和遗产的基本组成部分。运动员被广泛视为国家自豪感的来源，他们的身体健康被认为至关重要。运动员达到最佳表现和预防受伤取决于身体素质。技术整合已采用信息物理系统（CPS）来改善运动训练环境。
本研究介绍一种在训练环境中评估运动员身体素质的方法：基于物联网（IoT）和CPS的身体素质评估方法（IoT-CPS-PFEM）。
IoT-CPS-PFEM使用一系列与物联网相连的传感器和设备来观察和评估运动员的身体素质。所提出的方法收集有关各种健康参数的信息，包括心率、体温和血氧饱和度。它采用机器学习算法来审查并提供有关运动员身体健康状况的反馈。
模拟结果表明所提出的IoT-CPS-PFEM在识别运动员身体素质水平方面的有效性，平均精度为93%。所考虑的方法旨在解决传统身体素质评估技术存在的障碍，包括不精确性、时间滞后和人工数据收集要求。IoT-CPS-PFEM方法具有实时监测、精确性和自动化的优点，从而在很大程度上提高运动员的身体素质和整体表现。
研究结果表明，实施IoT-CPS-PFEM可以对运动员的身体素质产生重大影响，并提高印度体育产业在全球比赛中的表现。
