# Prediction of Health Problems Using Deep Learning Images and Bio-Signals
###### Abstract  
We propose a study to predict health abnormalities by analyzing body temperature and the heart rate variability parameters of pulse waves. The research method firstly selects fever by applying a deep learning model to thermal images, and secondly, extracts heart rate variability from pulse waves through a photo-plethysmograph sensor. It analyzes the relevance of body temperature and health status by dividing the presence or absence of fever cases and comparing parameters related to autonomic nerves and stress cases. As a result of the experiment, the control group with normal body temperature had a mean pulse variability of 37.65, SDNN of 64.83, and RMSSD of 42.24. In contrast, in the experimental group, which consisted of individuals with fever, the average pulse variability was 31.91, the SDNN was 42.34, and the RMSSD was 26.80. Our research expects to be applicable to a thermal imaging system that can measure body temperature and bio-signals together and show the predicted results of health status for fever cases. deep learning; abnormal; health condition; stress; thermal image +
Footnote †: journal: Medical  
The typical method of face detection is to find important features of a human face (e.g., eyes, nose, mouth, etc.) from input images and to normalize face images of a certain size in reference to such features. Based on [PERSON] and [PERSON] algorithm released in the early 2000s, the Boosted Haar Feature and Cascade algorithm was suggested in a relevant major study to extract features and detect a human face [5]. Thereafter, a convolutional neural network (CNN) and regions with a convolutional neural network (R-CNN) were proposed, which contributed significantly to the area of object detection and recognition in the form of deep learning [6; 7; 8]. Major examples of deep learning models are faster R-CNNs [9], upgrading the previous version of R-CNNs in terms of speed and performance, which extract a target area and classify objects from that area and you only look once (YOLO) [10], which distinguishes objects from the entire image at once. There has been continued research on face detection by means of such deep learning models for security and authentication [11; 12], and recently, as the number of people wearing a mask to prevent droplet infection increases, some studies suggest methods to detect faces wearing a mask [13]. Measuring the body temperature to diagnose fever is the most basic and fundamental practice in the medical field to check the human body's health condition quickly. Fever is a normal reaction to a human body's immuno-stimulant, and it is closely related to the autonomous nervous system. Heart rate variability (HRV) is a parameter to digitize and analyze such autonomous nerve reactions. It is referred to as an indicator to analyze fine changes in heart beats in a scientific and quantitative manner and to determine conditions of the autonomous nervous system such as antagonism, stress, blood vessel condition, etc. HRV can be measured based on pulse waves or electrocardiogram measurements by means of photo-plethysmograph (PPG), which is a non-invasive sensing method [14; 15; 16]. Various methods are currently employed to determine the health status of various organisms. Examples include research on the causes of adverse health effects on organisms such as humans, plants, animals, bio-signal detection research using biosensors and machine learning, and research to identify abnormal signs using thermal image data [17; 18; 19; 20; 21; 22; 23; 24; 25]. There are, however, insufficient studies on the relationship between HRV and stress analysis for fever symptoms and on the detection of specific objects using deep learning for thermal imaging. This study suggests a method to predict and identify individuals with fever by applying a convolutional neural network model specially designed for image detection to thermal images.