Intelligent Data Transfer Technique for 6G Cyber Physical Model Enabled Teaching System

Medical decision-making may be aided by machine learning technology at the clinical and diagnostic levels. For example, one of the most important aspects of preventing and minimising damage while in motion is sports injury prediction. Despite substantial efforts, the current approach is constrained by its incapacity to pinpoint predictors for sports injuries. The risk of harm to athletes is an important factor to take into account when planning methods for the prevention of work-related accidents and the reduction of associated hazards. A variety of indicators are being assessed in order to determine the risk factors for injuries in various ways. Consequently, this study suggests a Deep Learning Model (DLM) that leverages the Internet of Things (IoT) and the concept of Cloud to Thing continuum—extending the cloud with low-latency, energy-efficient devices closer to the data sources situated at the network edge—for the purpose of detecting sports injuries. The body area network’s Internet of Things (IoT) sensors gather vital information for the diagnosis of sports injuries, while cloud computing provides adaptable computer system resources and processing capacity. This work improves the medical rehabilitation system for sports, investigates the brain damage monitoring framework, and forecasts brain injury using an ideal neural network. The suggested model’s performance is evaluated and contrasted with existing models using the metrics accuracy, precision, recall, and F1-score.