Resilient Multi-Layer LSTM Digital Twin Framework for Athlete Logistics and Safety in 6G

Abstract

The development of 6G communication technologies provides new possibilities for the integration of advanced intelligence, connectivity, and security in sports and athlete management. In this paper, a Federated AI and Privacy-Preserving Digital Twin system (FPP-DT) is presented to optimize athlete logistics, performance monitoring and safety in real time. Dynamic virtual models of the athlete (digital twins) are used to gather physiological, environmental and logistical data by means of wearable sensors and smart devices. Such replicas support predictive analytics, proactive risk assessment, and adaptive decision-making, thereby optimizing training, transportation, and emergency response. Federated AI prevents the centralization of raw biometric and other personal data by training a model using the sensors of athletes’ devices and clubs, as well as the medical institutions. Privacy protection under collaborative learning is further improved using security techniques, such as differential privacy and secure multi-party computation. The ultra-low latency and extremely high reliability aspects of 6G networks provide the seamless connectivity needed for real-time digital twins’ synchronization and stimulus-free transmission of information between athletes, coaches, medical staff, and event organizers. Thus, the system addresses the key problem areas in the management of athletes in terms of constant control over safety, the proper organization of logistics, and performance forecasting reinforcement, without compromising the privacy of the personal information. This can also be applied to international sporting events such as the Olympics, professional sports associations and training programs, where intelligent and safe systems are highly applicable to the safety of the athletes.