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).

Physiological stressor impact on peripheral facial temperature, Il-6 and mean arterial pressure, in young people.

Nowadays, stress is part of everyday life, whose long-term effects can trigger health risks. Among the main alterations that occur in the human body we can find the variation of inflammatory activity, blood pressure, and facial peripheral temperature. The objective of this work is to show the facial thermal behavior for men and women, as well as the differences in vascular and inflammatory responses induced by the effect of acute social stress. The Trier Social Stress Test was applied to 15 women and 15 men, free of disease, with an average age of 23.8 years and a standard deviation of 5.52. After capturing the baseline state, and at the end of the test, the inflammatory activity was measured through salivary interleukin-6; the mean blood pressure, and the capture of facial thermographic images. For the thermal images, six regions of interest (biothermomarkers) were analyzed: forehead, right cheek, left cheek, chin, nose, and corrugator muscle. The results obtained after analyzing the information were: an increase in inflammatory activity, an increase in mean blood pressure, and significant temperature changes in different areas of interest of the face, depending on gender. For men, it only appeared in the region of the nose and women's forehead, cheeks, and nose. Furthermore, the correlation between the three variables (il-6, blood pressure, and temperature) was performed and no significant values were found. Regarding the relationship between genders, only significant values were found for il-6.

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.

Manipulating the Alpha Level Cannot Cure Significance Testing.

We argue that making accept/reject decisions on scientific hypotheses, including a recent call for changing the canonical alpha level from p = 0.05 to p = 0.005, is deleterious for the finding of new discoveries and the progress of science. Given that blanket and variable alpha levels both are problematic, it is sensible to dispense with significance testing altogether. There are alternatives that address study design and sample size much more directly than significance testing does; but none of the statistical tools should be taken as the new magic method giving clear-cut mechanical answers. Inference should not be based on single studies at all, but on cumulative evidence from multiple independent studies. When evaluating the strength of the evidence, we should consider, for example, auxiliary assumptions, the strength of the experimental design, and implications for applications. To boil all this down to a binary decision based on a p-value threshold of 0.05, 0.01, 0.005, or anything else, is not acceptable.