Aura detection using thermal camera with convolutional neural network method for mental health diagnosis

Abstract

Mental health is an important aspect in realizing overall health. For this reason, non-invasive medical equipment is needed for people with mental disorders. This study aimed to create a psychological health diagnostic tool by detecting auras using a thermal camera from facial objects. The contribution of this study is that the tool can detect the patient's aura without physical contact so that the patient is more comfortable and does not feel invaded. This research designed a system for detecting electromagnetic wave radiation energy emitted by the body using a thermal camera. Face detection in the input image was performed using a convolutional neural network (CNN) model single shot multibox detector (SSD), which is one of the CNN models that implements a bounding box to estimate the localization of detected objects. In this case, system testing was used to evaluate the performance of the CNN system algorithm for aura detection in terms of color (main color or average color). The results obtained were detectibility by 80%, selectivity by 88.88%, precision by 70%, sensitivity by 87.5%, and accuracy by 63.63%. The design of the aura detection system in this study will make it easier for psychiatrists and psychologists to help make a noninvasive diagnosis.