Affiliations: [a] Department of Physical Education, Southeast University, Nanjing, Jiangsu, China | [b] Department of Computer Science and Engineering, SRM Institute of Science and Technology, Delhi NCR Campus, Ghaziabad, Uttar Pradesh, India | [c] Department of Information Technology, School of Computing, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, India
Correspondence:
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Corresponding author: Zhi Fang, Department of Physical Education, Southeast University, Nanjing, Jiangsu 210096, China. E-mail: njfzdei@126.com.
Abstract: BACKGROUND: The Internet of Things (IoT) has recently become a prevalent technological culture in the sports training system. Although numerous technologies have grown in the sports training system domain, IoT plays a substantial role in its optimized health data processing framework for athletes during workouts. OBJECTIVE: In this paper, a Dynamic data processing system (DDPS) has been suggested with IoT assistance to explore the conventional design architecture for sports training tracking. Method: To track and estimate sportspersons physical activity in day-to-day living, a new paradigm has been combined with wearable IoT devices for efficient data processing during physical workouts. Uninterrupted observation and review of different sportspersons condition and operations by DDPS helps to assess the sensed data to analyze the sportspersons health condition. Additionally, Deep Neural Network (DNN) has been presented to extract important sports activity features. RESULTS: The numerical results show that the suggested DDPS method enhances the accuracy of 94.3%, an efficiency ratio of 98.2, less delay of 24.6%, error range 28.8%, and energy utilization of 31.2% compared to other existing methods.
