Deep Learning-Based Thermal Imaging Analysis to Diagnose Abnormalities in Sports Buildings: Smart Cyber-Physical Monitoring Sensors at the Edge

Abstract

A joint green-edge computing idea is now realized in practice with the help of intelligent infrastructure for modern sport venues, based on Internet of Things (IoT) platforms and Cyber-Physical Systems (CPS). To monitor their sports actions, athletes need smart environments. Using edge-enabled low-cost and low-power sensors, such as infrared monitoring systems that analyze thermal information, this environment should alert to possible physical damages. Early recognition of sports injuries and joint injuries can usually prevent athletes from pain and missing exercise. One of the most efficient methods for identifying pain and movement problems is to monitor the energy emitted by lower limb injuries. By analyzing thermal images of the lower body parts, this research attempts to automatically identify sports injuries. The thermal image is first isolated from the region of interest. Convolutional structures are applied to identify lesions using a newly developed and optimized method. The performance of the classifier is performed with the possibility of deep learning by pruning the features, to reduce the computational complexity and improve the accuracy, and a model has been developed based on the classification of sports injuries in binary mode (i.e., whether the lesions are present or not) and multiclass mode (i.e., the severity of sports injuries) resulted in optimal results. Thermal images show the different states of joints, including lesions caused by various sports in the lower limbs. This model could provide the ability of solving uncertainty of answers, repeatability, and convergence towards minimum error. As compared to conventional feature extraction and classification approaches, the outputs are more acceptable. By taking advantage of the K-fold cross-validation method, the average error of the proposed method to detect the severity of damage is less than 2.22%.