Skin potential response for stress recognition in simulated urban driving

In this paper, we address the problem of possible stress conditions arising in car drivers, thus affecting their driving performance. We apply various Machine Learning (ML) algorithms to analyse the stress of subjects while driving in an urban area in two different situations: one with cars, pedestrians and traffic along the course, and the other characterized by the complete absence of any of these possible stress-inducing factors. To evaluate the presence of a stress condition we use two Skin Potential Response (SPR) signals, recorded from each hand of the test subjects, and process them through a Motion Artifact (MA) removal algorithm which reduces the artifacts that might be introduced by the hand movements. We then compute some statistical features starting from the cleaned SPR signal. A binary classification ML algorithm is then fed with these features, giving as an output a label that indicates if a time interval belongs to a stress condition or not. Tests are carried out in a laboratory at the University of Udine, where a car driving simulator with a motorized motion platform has been prearranged. We show that the use of one single SPR signal, along with the application of ML algorithms, enables the detection of possible stress conditions while the subjects are driving, in the traffic and no traffic situations. As expected, we observe that the test individuals are less stressed in the situation without traffic, confirming the effectiveness of the proposed slightly invasive system for detection of stress in drivers.

Effects of The Long-Time Wearing of Gloves by Health Care Workers Managing Covid-19 Assessed by Electrodermal Activity: A Pilot Study

Continues wearing gloves is a crucial problem among health care workers treating patients with coronavirus (COVID-19) infection that may cause skin complications. In the present study, the skin conductance (SC), skin potential (SP) and skin susceptance (SS) were used to identify the effects of long-term wearing gloves among health care workers managing COVID-19. The experimental procedure was repeated 30 times on three subjects recruited as volunteers to wear gloves for 4 hours. The baseline of parameters SC, SP, and SS were recorded at normal conditions without wearing the glove at zero time (To). The parameters were again recorded in the cases of glove-wearing with and without zinc oxide ointment (ZnO) for the same period. Noticeable differences were found in each of SC, SP and SS for subjects who wore gloves when compared to the baseline for 4 h. Results also showed significant differences in each of SP and SS during the same period with applying ZnO comparing to wearing gloves without applying ZnO. The results show that skin hydration is significantly improved by using ZnO. However, changing gloves at least every four hours of continuous wearing is recommended.

ON THE ISSUE OF VERIFICATION OF THE RESULTS OF ELECTRO-PUNCTURE DIAGNOSTICS (EPD) IN THE COMPREHENSIVE PATIENT REHABILITATION WITH MYOCARDIAL INFARCTION (MI)

The methods of studying the objective condition of the patient in the treatment and rehabilitation in traditional oriental medicine from pulse diagnostics to electro-puncture diagnostics (EPD) have been analyzed. It is noted that the development of medicine seeks to level the subjectivity of the diagnostician through the use of objective parameters of sympathetic and parasympathicotonia of representative of biologically active points (BAP). Methods that are not able to perform such a task (Folls EPD) cannot be considered scientific. The authors technique of the Nakatani EPD variant, protected by a patent, on the example of examination of patients with MI when prescribing cardioselective BAB is presented. The physiological indicators of maxima and minima of electro-skin potential of BAP as daily and monthly cycles of an organism are described by the east medicine considered. It is reasonable to believe that the physiological rhythms of the body recorded in the EPD are a valuable method of verification and interpretation of research results.

Identification of The Key miRNAs And Target Genes In Basal Cell Carcinoma By Bioinformatics Analysis

Background The highly tissue-destructive and localized accumulation of basal cell carcinoma(BCC) makes it one of the most important cancers affecting people's lives. Existing therapeutic approaches, including surgical treatment, chemotherapy, and Hedgehog pathway inhibitors, have failed to achieve broad therapeutic effects for various reasons. This study aims to explore additional potential therapeutic targets and possible diagnostic and prognostic biomarkers using bioinformatics analysis.Material/Methods The Gene Expression Omnibus (GEO) database identified the microarray dataset GSE34535. The GEO2R tool was used to screen out differentially expressed genes (DEGs) between BCC and non-lesional skin. Potential target genes of DE-miRNA were screened using the miRWalk, mirDIP and miRTarBase databases. Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis for target genes were established using the DAVID database. Protein–protein interaction network and miRNA-hub gene network were analyzed based on the STRING database and visualized by Cytoscape software. Results 51 up-regulated DE-miRNAs and 38 down-regulated DE-miRNAs were identified from the BCC samples. miR-455-5p was mainly up-regulated and miR-139-5p was mainly down-regulated. Two key bub genes MAPK1 and EGFR were identified in the PPI network. Four out of the ten hub genes were regulated by up-regulated miR-18a and four by down-regulated miR-133b. Viral infections were also identified in the study.Conclusions Bioinformatics identified four miRNAs and two important hub genes that may be associated with BCC, and it was suggested that viruses may play a role in BCC.

Tissue Mechanics in Haired Murine Skin: Potential Implications for Skin Aging

During aging, the skin undergoes changes in architecture and composition. Skin aging phenotypes occur due to accumulated changes in the genome/epigenome, cytokine/cell adhesion, cell distribution/extracellular matrix (ECM), etc. Here we review data suggesting that tissue mechanics also plays a role in skin aging. While mouse and human skin share some similarities, their skin architectures differ in some respects. However, we use recent research in haired murine skin because of the available experimental data. Skin suffers from changes in both its appendages and inter-appendage regions. The elderly exhibit wrinkles and loose dermis and are more likely to suffer from wounds and superficial abrasions with poor healing. They also have a reduction in the number of skin appendages. While telogen is prolonged in aging murine skin, hair follicle stem cells can be rejuvenated to enter anagen if transplanted to a young skin environment. We highlight recent single-cell analyses performed on epidermis and aging human skin which identified new basal cell subpopulations that shift in response to wounding. This may be due to alterations of basement membrane stiffness which would change tissue mechanics in aging skin, leading to altered homeostatic dynamics. We propose that the extracellular matrix (ECM) may play a key role as a chemo-mechanical integrator of the multi-layered senescence-associated signaling pathways, dictating the tissue mechanical landscape of niche microenvironments in aging phenotypes. We show examples where failed chemo-mechanical signaling leads to deteriorating homeostasis during skin aging and suggest potential therapeutic strategies to guide future research to delay the aging processes.

Emotion Recognition Based on Skin Potential Signals with a Portable Wireless Device

Emotion recognition is of great importance for artificial intelligence, robots, and medicine etc. Although many techniques have been developed for emotion recognition, with certain successes, they rely heavily on complicated and expensive equipment. Skin potential (SP) has been recognized to be correlated with human emotions for a long time, but has been largely ignored due to the lack of systematic research. In this paper, we propose a single SP-signal-based method for emotion recognition. Firstly, we developed a portable wireless device to measure the SP signal between the middle finger and left wrist. Then, a video induction experiment was designed to stimulate four kinds of typical emotion (happiness, sadness, anger, fear) in 26 subjects. Based on the device and video induction, we obtained a dataset consisting of 397 emotion samples. We extracted 29 features from each of the emotion samples and used eight well-established algorithms to classify the four emotions based on these features. Experimental results show that the gradient-boosting decision tree (GBDT), logistic regression (LR) and random forest (RF) algorithms achieved the highest accuracy of 75%. The obtained accuracy is similar to, or even better than, that of other methods using multiple physiological signals. Our research demonstrates the feasibility of the SP signal’s integration into existing physiological signals for emotion recognition.

Facial EMG Activity Is Associated with Hedonic Experiences but Not Nutritional Values While Viewing Food Images

The physiological correlates of hedonic/emotional experiences to visual food stimuli are of theoretical and practical interest. Previous psychophysiological studies have shown that facial electromyography (EMG) signals were related to subjective hedonic ratings in response to food images. However, because other data showed positive correlations between hedonic ratings and objective nutritional values of food, whether the facial EMG reactions to food images could reflect the hedonic evaluation or nutritional assessment of food remains unknown. To address this issue, we measured subjective hedonic ratings (liking, wanting, valence, and arousal) and physiological signals (facial EMG of the corrugator supercilii, zygomatic major, masseter, and suprahyoid muscles, skin potential responses, and heart rates) while participants observed food images that had objective nutritional information (caloric, carbohydrate, fat, and protein contents). The results revealed that zygomatic major EMG activity was positively correlated with ratings of liking, wanting, and valence, but not with any objective nutritional value. These data indicate that facial EMG signals in response to food images reflect subjective hedonic experiences, but not objective nutritional values, associated with the food item.

Electrodermal Activity: Simultaneous Recordings

Electrodermal activity (EDA) is a sensitive measure of the sympathetic nervous system activity. It is used to describe changes in the skin electrical properties. This chapter aimed to show advantages of simultaneous recordings of EDA parameters at the same skin site over other recordings. The literature databases, Web of Science and Google Scholar, were searched using terms like “electrodermal activity,” “sequential recording,” “simultaneous recording,” “skin conductance,” “skin potential,” and “skin susceptance.” Articles that include sequential and/or simultaneous recording of EDA parameters were analyzed. The chapter presents a description of the oldest and current methods used for recording EDA parameters and an explanation of the newest techniques used in EDA researches. Although sequential recordings are predominant and widely spreading, much effort has been made to simultaneously record skin conductance (SC) and skin potential (SP), and recently researchers realized the capability of simultaneously recording SC, SP, and skin susceptance (SS) at the same skin site. The advantage of simultaneous over the sequence measurements is that the latter must be manually time realigned when measured by different instruments, which means it is time-consuming. Although the simultaneous measurements are used exclusively for research purposes at this stage, this may open horizons in the modern trends of psychophysiology applications in the near future.