Automatic Segmentation of Facial Regions of Interest and Stress Detection Using Machine Learning.

Stress is a factor that affects many people today and is responsible for many of the causes of poor quality of life. For this reason, it is necessary to be able to determine whether a person is stressed or not. Therefore, it is necessary to develop tools that are non-invasive, innocuous, and easy to use. This paper describes a methodology for classifying stress in humans by automatically detecting facial regions of interest in thermal images using machine learning during a short Trier Social Stress Test. Five regions of interest, namely the nose, right cheek, left cheek, forehead, and chin, are automatically detected. The temperature of each of these regions is then extracted and used as input to a classifier, specifically a Support Vector Machine, which outputs three states: baseline, stressed, and relaxed. The proposal was developed and tested on thermal images of 25 participants who were subjected to a stress-inducing protocol followed by relaxation techniques. After testing the developed methodology, an accuracy of 95.4% and an error rate of 4.5% were obtained. The methodology proposed in this study allows the automatic classification of a person's stress state based on a thermal image of the face. This represents an innovative tool applicable to specialists. Furthermore, due to its robustness, it is also suitable for online applications.

Face Thermal Map of the Mexican Population in the Basal State.

There has been a wide use of thermal images of the human body in recent years, specifically images with thermal information of regions of interest (ROI) in the face; this information can be used for epidemiological, clinical, and/or psychological purposes. Due to this, it is important to have plenty of information on temperature in these ROIs in the basal state that allows their use as a reference in terms of their thermal analysis. In this work, a face thermal map of the Mexican population in the basal state (n = 196) is created, adding the comparison between different population groups, such as gender, age, and clinical status, obtaining results of great interest for future research. The t-test for independent samples was applied to the ROIs with normal distribution and Mann−Whitney u-test to the ones that did not present normal distribution. Statistically significant differences were found in some of the ROI comparisons like the corrugator, the supraorbitals, and the chin between the control and clinical groups, as well as in the differentiation by age (p < 0.05).

Effects of the Trier Social Stress Test on the distributions of IL-6 and MAP levels.

Trier Social Stress Test (TSST) is an experimental psychological test that induces changes in autonomic, endocrinological and immunological activity. Two measures used to evaluate the inflammatory activity induced by this test are the interleukin 6 (IL-6), a cytokine sensitive to changes in sympathetic nervous activity, and the mean arterial pressure (MAP), a measure sensitive to changes in autonomic activity. This study had two goals: first, the study examined whether TSST increases IL-6 and MAP levels; second, pre- and post-TSST IL-6 levels were compared for participants whose IL-6 levels increased or decreased due to the TSST. Saliva samples of IL-6 and MAP were taken from 42 participants clinically healthy, without psychiatric history, and data were analysed via quantile comparisons. The results showed that TSST did not lead to an increase in sympathetic activity as indexed by IL-6. Instead, TSST led to increases in MAP. Also, there were significant differences between the IL-6 distributions of people whose IL-6 levels changed from low to high (63%) and from high to low (37%) before and after the TSST. These findings suggest that the TSST will not have the same effect on all participants; that is, individual differences can be assessed using a biomarker to identify people with specialized psychological care needs.