Intelligent Development Model of Sports Injury Rehabilitation Equipment Integrating Wearable Biomimetic Structures and Adaptive Materials

Abstract

 By collecting and analyzing sports and physiological data, artificial intelligence (AI) technology can detect abnormal sports patterns in real time and provide customized feedback to enhance the safety of sports injury rehabilitation training. With the rapid growth of demand for sports injury rehabilitation, traditional rehabilitation equipment has limitations such as strong passivity, poor adaptability, and lack of real-time feedback, making it difficult to meet the needs of patients. In this context, intelligent wearable devices have gradually entered the clinical diagnosis and treatment field with their portability, real-time data collection and analysis capabilities. This article focuses on the development of sports injury rehabilitation equipment that integrates wearable biomimetic structures and adaptive materials, and proposes a rehabilitation training anomaly detection model based on deep learning (DL) technology. This model collects real-time motion and physiological data of patients through wearable devices, and analyzes the data using DL algorithms to accurately identify abnormal patterns in rehabilitation training and trigger warning mechanisms in a timely manner, providing customized feedback for patients. The results indicate that the model can efficiently and accurately detect abnormal states in rehabilitation training, significantly improving the safety and personalization level of rehabilitation training.