Abstract

Athletes health monitoring plays a vital role because the changes in their heart rate reduce their physical activity and contribution. The changes in athlete activities cause developing risk that affects their outcome. Therefore, athletes' heart rates should be monitored frequently to minimize the risk factors and improve their health. This work uses wearable sensor devices to monitor their health condition continuously. The wearable devices on their health record their Electrocardiogram (ECG), which is transferred to the health care centre. With the help of the ECG, this work Sportsperson Heart Rate Monitoring (HRMS-SP) is created. The gathered ECG information is processed using the Fuzzy Clustering (FC) algorithm to predict the Heart Rate Variability (HRV). According to the HRV value, athlete's mental stress level and their sports contribution were also investigated to minimize the computation complexity. In addition, the wearable device-based collected information was investigated using the fuzzy and big data analytics used to monitor people frequently. The predicted information is used to monitor, treat, prevent, and predict the sports person's activities effectively. During the analysis, Hadoop, Visualization, and data mining processes are applied to extract the health information from large datasets that are used to improve the athlete health monitoring systems.