ACL Injury Prevention in Athletes with IoT system and Active Sensors

Abstract

The number of Anterior Cruciate Ligament (ACL) injuries among young people and athletic professionals is growing rapidly. ACL reconstruction is being performed at an increasing extent nowadays. Although surgically treated, about 79% of these people develop osteoarthritis of the knee and 20% develop injuries again within two years. The risk of recurrent injuries and arthritis has become a financial burden and a public health concern. One in four young adults with an injury to the ACL has a second ACL injury from them in their career. Knee injuries (especially of the ACL) have a major impact on the future athletic performance. To reduce this damage, a suitable performance evaluation and intervention tool is needed to identify factors that make athletes prone to injury. Therefore, this research designs a novel IoT model using small devices with the possibility of measurement, processing, and communication, employing sensors and internal tools for ACL damage analysis. This paper presents a framework based on the IoT model to keep track of human biological signals during activities that may possibly cause ACL damage. The most important benefit of the suggested system is the flexibility in calculating the clinical data with the resources of the user's body network gadgets.