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The improvements in health habits, technological advances, and the proliferation of healthy living activities have contributed to the comprehensive extension of sports person health assessment. Since the internet of things (IoT) device requires energy‐optimized wearable devices, it has been observed that the demanding factors include energy efficiency factor in Sports Person Health Monitoring wearable device. Hence in this paper, IoT‐based Hierarchical Health Monitoring Model (IoT‐HHMM) is proposed to improve the efficiency factor by minimizing the energy consumption to achieve effective assessment of sports person health monitoring wearables. The complexity of limited resources and usage of energy is optimized by introducing the Optimal Energy‐Efficient Resource Assignment Algorithm. Likewise, a cloud computing technique is implemented using Probabilistic Radial Basis Function Neural Network to ensure effective prediction and classification in healthcare data management, which is considered as a significant factor in wearable IoT devices for Sports Person Health Monitoring. The result indicates that the proposed IoT‐HHMM achieves a high accuracy ratio of 98.4%, a sensitivity ratio of 92.5%, a performance ratio of 96.7% when compared to traditional approaches.
Qiu et al. (Sat,) studied this question.