Predictive model for the identification of injuries with joint hypermobility in the arm ligament

Abstract

Joint hypermobility is a common condition affecting many people and carries an increased risk of injury and associated complications such as chronic pain and fatigue. The need for effective injury prevention strategies in this population is crucial to improve their quality of life. This study aimed to develop a smart glove incorporating sensors and artificial neural networks (ANN) to monitor and predict risky movements in people with joint hypermobility. A comprehensive approach was employed, starting with an extensive literature review on joint hypermobility and its implications. In the design and development of the predictive model, ANNs were implemented for data analysis and prediction. It was tested with a population of patients diagnosed with joint hypermobility. Key findings revealed a 92% accuracy rate in detecting risky movements, indicating its potential for practical application in daily activities that could affect joint use. The developed technology shows great promise in preventing injuries and improving the quality of life of people with joint hypermobility. This innovative approach can transform rehabilitation practices and promote personalized care in the treatment of this condition.