Construction of sports functional fitness training system based on a data-driven health decision support system
Abstract
The Internet of Things (IoT) paradigm is employed in different sports-related activities for health monitoring and performance assessments. Athlete training based on physical activities and observation is performed using IoT devices and computing systems. Clinical Decision Support (CDS) aims to improve health and health care by providing doctors, employees, patients, or other persons with knowledge and information tailored to each individual’s needs at the right moment. The state of being physically ready to do the actions required by a particular activity (usually a sport). Sports-specific skills that can only be mastered via repeated practice. The problem of consistent performance index management is limited due to large data validations. This article introduces a Reliable Index Assessment Technique (RIAT) for evaluating athlete performances. The physical attributes, such as oxygen level, stamina, tiredness, completion time, speed, etc., are observed using wearable sensors. The observed signals are processed for their appropriate declinations and stagnancy during the training sessions. The training index is constructed based on the declinations and stagnancy identified through an intense federated learning paradigm. This index assessment relies on multilevel training updates to prevent performance assessment inconsistencies. The index construction is made from the multilevel assessment using federated learning updates. This update is validated using the previous index and the currently observed inferences for preventing computation errors. Therefore, the distributed training data is accessed and updated for global indexing through the IoT elements. This technique achieves high precision, an assessment rate under measured computation time, and fewer errors.
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