Violence Exposure and Trauma-Informed Care.

Addressing violence in pediatrics requires a working knowledge of trauma-informed care (TIC). TIC weaves together our current understanding of evolution, child development, and human physiology and how these explain common childhood responses to traumatic events. In this article, we describe our current approach to treating childhood trauma in the context of violence. Ultimately, TIC relies on the pediatrician's ability to keep trauma high on their differential diagnosis. TIC leverages a child's natural strengths and biologic processes by (1) scaffolding the patient's relationships to safe, stable, and nurturing adults and (2) strengthening core resilience skills while addressing trauma symptoms when necessary.

MicroSweat: A Wearable Microfluidic Patch for Noninvasive and Reliable Sweat Collection Enables Human Stress Monitoring.

Stress affects cognition, behavior, and physiology, leading to lasting physical and mental illness. The ability to detect and measure stress, however, is poor. Increased circulating cortisol during stress is mirrored by cortisol release from sweat glands, providing an opportunity to use it as an external biomarker for monitoring internal emotional state. Despite the attempts at using wearable sensors for monitoring sweat cortisol, there is a lack of reliable wearable sweat collection devices that preserve the concentration and integrity of sweat biomolecules corresponding to stress levels. Here, a flexible, self-powered, evaporation-free, bubble-free, surfactant-free, and scalable capillary microfluidic device, MicroSweat, is fabricated to reliably collect human sweat from different body locations. Cortisol levels are detected corresponding to severe stress ranging from 25 to 125 ng mL-1 averaged across multiple body regions and 100-1000 ng mL-1 from the axilla. A positive nonlinear correlation exists between cortisol concentration and stress levels quantified using the perceived stress scale (PSS). Moreover, owing to the sweat variation in response to environmental effects and physiological differences, the longitudinal and personalized profile of sweat cortisol is acquired, for the first time, for various body locations. The obtained sweat cortisol data is crucial for analyzing human stress in personalized and clinical healthcare sectors.

Effects of the Visual Character of Transitional Spaces on Human Stress Recovery in a Virtual Reality Environment.

As people's levels of stress increase with the complexity of contemporary urban life, the stress healing agenda in built environments has become more critical than ever. Previous research has demonstrated that linear and nonlinear shapes in the environment have an impact on human stress recovery. However, to date, most studies have focused on indoor and outdoor spaces, while research on transitional spaces is still limited. Transitional spaces connect the interior with the exterior and are ubiquitous in the city, such as plazas, open cafes, and urban corridors. We hypothesize that curved and linear environments affect human stress recovery differently in transitional spaces. To test this hypothesis, virtual reality (VR) technology and experiments were conducted with 40 participants. At the end of the Trier Social Stress Test (TSST), participants were randomly assigned to four VR environments to test which environment is more effective in stress recovery for humans. Participants' physiological data, including heart rate and blood pressure, were measured by bio-monitoring sensors. The psychological data were tested by the State-Trait Anxiety Inventory (STAI). In general, the resulting data indicate that the curved environment is more effective than the linear environment for the recovery of human stress in transitional spaces.

The Concept of Advanced Multi-Sensor Monitoring of Human Stress.

Many people live under stressful conditions which has an adverse effect on their health. Human stress, especially long-term one, can lead to a serious illness. Therefore, monitoring of human stress influence can be very useful. We can monitor stress in strictly controlled laboratory conditions, but it is time-consuming and does not capture reactions, on everyday stressors or in natural environment using wearable sensors, but with limited accuracy. Therefore, we began to analyze the current state of promising wearable stress-meters and the latest advances in the record of related physiological variables. Based on these results, we present the concept of an accurate, reliable and easier to use telemedicine device for long-term monitoring of people in a real life. In our concept, we ratify with two synchronized devices, one on the finger and the second on the chest. The results will be obtained from several physiological variables including electrodermal activity, heart rate and respiration, body temperature, blood pressure and others. All these variables will be measured using a coherent multi-sensors device. Our goal is to show possibilities and trends towards the production of new telemedicine equipment and thus, opening the door to a widespread application of human stress-meters.

Human stress classification during public speaking using physiological signals.

Public speaking is a common type of social evaluative situation and a significant amount of the population feel uneasy with it. It is of utmost importance to detect public speaking stress so that appropriate action can be taken to minimize its impacts on human health. In this study, a multimodal human stress classification scheme in response to real-life public speaking activity is proposed. Electroencephalography (EEG), galvanic skin response (GSR), and photoplethysmography (PPG) signals of forty participants are acquired in rest-state and during public speaking activity to divide data into a stressed and non-stressed group. Frequency domain features from EEG and time-domain features from GSR and PPG signals are extracted. The selected set of features from all modalities are fused to classify the stress into two classes. Classification is performed via a leave-one-out cross-validation scheme by using five different classifiers. The highest accuracy of 96.25% is achieved using a support vector machine classifier with radial basis function. Statistical analysis is performed to examine the significance of EEG, GSR, and PPG signals between the two phases of the experiment. Statistical significance is found in certain EEG frequency bands as well as GSR and PPG data recorded before and after public speaking supporting the fact that brain activity, skin conductance, and blood volumetric flow are credible measures of human stress during public speaking activity.

Human Stress and StO2: Database, Features, and Classification of Emotional and Physical Stress.

Emotional and physical stress can cause various health problems. In this paper, we used tissue blood oxygen saturation (StO2), a newly proposed physiological signal, to classify the human stress. We firstly constructed a public StO2 database including 42 volunteers subjected to two types of stress. During the physical stress experiment, we observed that the facial StO2 right after the stress can be either increased or decreased comparing to the baseline. We investigated the StO2 feature combinations for the classification and found that the average StO2 values from left cheek, chin, and the middle of the eyebrow can provide the highest classification rate of 95.56%. Comparison with other stress classification method shows that StO2 based method can provide best classification performance with lowest feature dimension. These results suggest that facial StO2 can be used as a promising features to identify stress states, including emotional and physical stress.

Human stress classification using EEG signals in response to music tracks.

Stress is inevitably experienced by almost every person at some stage of their life. A reliable and accurate measurement of stress can give an estimate of an individual's stress burden. It is necessary to take essential steps to relieve the burden and regain control for better health. Listening to music is a way that can help in breaking the hold of stress. This study examines the effect of music tracks in English and Urdu language on human stress level using brain signals. Twenty-seven subjects including 14 males and 13 females having Urdu as their first language, with ages ranging from 20 to 35 years, voluntarily participated in the study. The electroencephalograph (EEG) signals of the participants are recorded, while listening to different music tracks by using a four-channel MUSE headband. Participants are asked to subjectively report their stress level using the state and trait anxiety questionnaire. The English music tracks used in this study are categorized into four genres i.e., rock, metal, electronic, and rap. The Urdu music tracks consist of five genres i.e., famous, patriotic, melodious, qawali, and ghazal. Five groups of features including absolute power, relative power, coherence, phase lag, and amplitude asymmetry are extracted from the preprocessed EEG signals of four channels and five bands, which are used by the classifier for stress classification. Four classifier algorithms namely sequential minimal optimization, stochastic decent gradient, logistic regression (LR), and multilayer perceptron are used to classify the subject's stress level into two and three classes. It is observed that LR performs well in identifying stress with the highest reported accuracy of 98.76% and 95.06% for two- and three-level classification respectively. For understanding gender, language, and genre related discriminations in stress, a t-test and one-way analysis of variance is used. It is evident from results that English music tracks have more influence on stress level reduction as compared to Urdu music tracks. Among the genres of both languages, a noticeable difference is not found. Moreover, significant difference is found in the scores reported by females as compared to males. This indicates that the stress behavior of females is more sensitive to music as compared to males.