Prediction of biological information from thermographic images and proposal of health management system

Abstract

Recent years, the number of people with low core body temperature tends to increase, and one of the causes is said to be muscle weakness due to lack of exercise. In addition, it has been pointed out that as the core body temperature decreases, the body's immunity also decreases, making it easier to get sick. On the other hand, with the recent development of machine learning, it is expected to improve the efficiency of work by deep learning in the medical field. Therefore, using machine learning, we proposed a method to visualize physical condition, aiming to predict various biological information only by thermography. In this study, we grasped the relationship between thermographic images and biometric information in step exercise, made the LSTM model and VGG16 model learn the collected data, and verified the prediction accuracy of biometric information. In the prediction experiment, thermographic images and biological information were acquired at three tempos of 100, 120, and 140. Furthermore, we evaluated the health index using the prediction data obtained in this experiment, and verified the error with the actual value.