An IoT-Based Injury Prediction and Sports Rehabilitation for Martial Art Students in Colleges Using RNN Model

Abstract

Sports rehabilitation focuses on the restoration of physical function and performance of martial arts students and athletes by assisting them in the recovery process during injuries. Each athlete’s injury is unique and requires personalized treatment. The conventional approaches lack tailored feedback and precise monitoring to provide personalized treatment, depending on the nature of an injury. To enhance treatment outcomes in sports rehabilitation, this paper utilizes an improved Recurrent Neural Network (RNN) model that is optimized for sequential data analysis and incorporates attention mechanisms to prioritize relevant features from profiles of marital art students in colleges, injury details, and rehabilitation protocols. It uses wearable devices of the Internet of Things (IoT) to collect sequential data from different sources in real-time. Next, the gathered data is cleansed and preprocessed, which ensures compatibility with temporal data structures and facilitates seamless integration into clinical settings. This process includes different techniques like normalization, segmentation, and feature extraction. Finally, an RNN model is reconfigured, which consists of the input layer, two hidden LSTM layers, and an output layer that facilitates the processed data of the athletes. The athlete’s progress is continuously monitored, and timely adjustments are made to rehabilitation plans. The model is then trained on diverse datasets, which include athlete profiles, injury characteristics, rehabilitation protocols, and outcome measures. Experimental results demonstrate a 15% increase in prediction accuracy and a 20% improvement in rehabilitation efficiency. Additionally, player performance metrics showed a 25% enhancement in recovery speed and a 30% reduction in the risk of re-injury.