Carbon nanomaterials intelligent wearable devices for real-time athlete monitoring and performance tracking

ABSTRACT Carbon nanomaterials have revolutionized wearable technology by enabling the development of lightweight, flexible, and highly sensitive devices for real-time monitoring of athletes and performance tracking. These devices provide valuable insights into athletes’ physiological and biomechanical parameters, aiding in the optimization of performance and the prevention of injuries. However, existing wearable systems often suffer from limited sensitivity, data inaccuracies, and a lack of personalized feedback, which hinder their effectiveness in supporting elite athletic performance. This study proposes an Artificial Intelligence-Driven Personalized Athlete Monitoring System (AI-PAMS) to monitor and track the performance of athletes. The system integrates carbon nanomaterial-based sensors with advanced AI algorithms to ensure accurate data collection, real-time analysis, and actionable insights tailored to individual athletes. AI-PAMS incorporates noise reduction for sensor data, machine learning for predictive analysis, and adaptive feedback systems for personalized recommendations. The proposed method enhances usability in professional sports training by providing real-time dashboards, automated alerts, and adaptive training insights to improve athlete performance and reduce the risk of injuries. Findings demonstrate that AI-PAMS achieves higher accuracy, improved data reliability, and superior adaptability compared to traditional methods, making it an ideal solution for next-generation sports monitoring. The system is tested on a group of athletes under varied conditions, and performance is evaluated across metrics such as accuracy (97.23%), data reliability (95.83%), and adaptability (94.67%).