A Multisensor System Embedded in a Computer Mouse for Occupational Stress Detection.

Occupational stress is a major challenge in modern societies, related with many health and economic implications. Its automatic detection in an office environment can be a key factor toward effective management, especially in the post-COVID era of changing working norms. The aim of this study is the design, development and validation of a multisensor system embedded in a computer mouse for the detection of office work stress. An experiment is described where photoplethysmography (PPG) and galvanic skin response (GSR) signals of 32 subjects were obtained during the execution of stress-inducing tasks that sought to simulate the stressors present in a computer-based office environment. Kalman and moving average filters were used to process the signals and appropriately formulated algorithms were applied to extract the features of pulse rate and skin conductance. The results found that the stressful periods of the experiment significantly increased the participants' reported stress levels while negatively affecting their cognitive performance. Statistical analysis showed that, in most cases, there was a highly significant statistical difference in the physiological parameters measured during the different periods of the experiment, without and with the presence of stressors. These results indicate that the proposed device can be part of an unobtrusive system for monitoring and detecting the stress levels of office workers.

A Personalized Healthcare Platform for Monitoring Mental Health of a Person During COVID

Stress is the human body’s response to various factors such as mental, physical, or emotional pressure. Coronavirus Disease 2019 pandemic has disrupted the mental health of most people worldwide. Stress plays a crucial role in corona virus disease patients during their medication period. Therefore, a remote mental health monitoring system has become a necessity. The physiological data captured using body sensors can provide rich information about the stress experienced by a person. Paper proposes a personalized stress indicator for monitoring a person’s mental health through a personalized healthcare platform. The physiological data from body sensors such as the galvanic skin response sensor, electrocardiogram module, and accelerometer module are sent in real-time to an Internet of things platform, ‘ThingSpeak.’ In the ThingSpeak platform, MATLAB analysis is performed to calculate the baseline threshold value of each user. Then, the stress percentage is evaluated based on the data rate above the threshold. The stress percentage is displayed on an output channel of the ThingSpeak platform. It enables remote monitoring of patients’ mental health by sending the health updates to the doctor or caretaker through email. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Relationship between Sensitivity Tendency and Psychological Stress Reactivity in Healthy Students

This study examined the relationship between sensory processing sensitivity and psychological stress reactivity in 69 healthy Japanese university students. The Japanese version of the Highly Sensitive Person Scale and the Japanese version of the Adolescent/Adult Sensory Profile were used for subjective assessment. The Galvanic skin response was measured as an objective measure of stress responses while the participants were completing the Stroop task. The Wilcoxon signed-rank test, the Spearman rank correlation coefficient, and the Mann–Whitney U test were conducted for data analysis. The results demonstrated that there was no significant correlation between the Japanese version of the Highly Sensitive Person Scale and Galvanic skin response. However, there was a marginal trend toward significance between low registration in the Japanese version of the Adolescent/Adult Sensory Profile and Galvanic skin response (rs = 0.231, p < 0.10;rs = 0.219, p < 0.10), suggesting that self-rated sensitivity was not necessarily associated with objective measures. These results indicate that sensory processing sensitivity analyses require the consideration of the traits and characteristics of the participants and multifaceted evaluations using a sensitivity assessment scale other than the Japanese version of the Highly Sensitive Person Scale.

IoT garment for remote elderly care network.

The elderly is a continuous growth sector thanks to the life expectancy increase in Western society. This sector is especially at risk from the appearance of respiratory diseases and, therefore, is the most affected sector in the COVID-19 epidemic. Many of these elderly require continuous care in residences or by specialized caregivers, but these personal contacts put this sector at risk. In this work, an IoT system for elderly remote monitoring is studied, designed, developed and tested. This system is composed by a smart garment that records information from various physiological sensors in order to detect falls, sudden changes in body temperature, heart problems and heat stroke; This information is sent to a cloud server through a gateway located in the patient's residence, allowing to real-time monitor remotely patient's activity using a customized App, as well as receiving alerts in dangerous situations. This system has been tested with professional caregivers, obtaining usability and functionality surveys; and, in addition, a detailed power-consumption study has been carried out. The results, compared with other similar systems, demonstrate that the proposed one is useful, usable, works in real time and has a decent power consumption that allows the patient to carry it during all day without charging the battery.

Academic stress detection on university students during COVID-19 outbreak by using an electronic nose and the galvanic skin response.

Academic stress is an emotion that students experience during their time at the university, sometimes causing physical and mental health effects. Because of the COVID-19 pandemic, universities worldwide have left the classroom to provide the method of teaching virtually, generating challenges, adaptations, and more stress in students. In this pilot study, a methodology for academic stress detection in engineering students at the University of Pamplona (Colombia) is proposed by developing and implementing an artificial electronic nose system and the galvanic skin response. For the study, the student's stress state and characteristics were taken into account to make the data analysis where a set of measurements were acquired when the students were presenting a virtual exam. Likewise, for the non-stress state, a set of measurements were obtained in a relaxation state after the exam date. To carry out the pre-processing and data processing from the measurements obtained previously by both systems, a set of algorithms developed in Python software were used to perform the data analysis. Linear Discriminant Analysis (LDA), K-Nearest Neighbors (K-NN), and Support Vector Machine (SVM) classification methods were applied for the data classification, where a 96 % success rate of classification was obtained with the E-nose, and 100 % classification was achieved by using the Galvanic Skin Response.

Wearable Sensors for Assessing the Role of Olfactory Training on the Autonomic Response to Olfactory Stimulation.

Wearable sensors are nowadays largely employed to assess physiological signals derived from the human body without representing a burden in terms of obtrusiveness. One of the most intriguing fields of application for such systems include the assessment of physiological responses to sensory stimuli. In this specific regard, it is not yet known which are the main psychophysiological drivers of olfactory-related pleasantness, as the current literature has demonstrated the relationship between odor familiarity and odor valence, but has not clarified the consequentiality between the two domains. Here, we enrolled a group of university students to whom olfactory training lasting 3 months was administered. Thanks to the analysis of electrocardiogram (ECG) and galvanic skin response (GSR) signals at the beginning and at the end of the training period, we observed different autonomic responses, with higher parasympathetically-mediated response at the end of the period with respect to the first evaluation. This possibly suggests that an increased familiarity to the proposed stimuli would lead to a higher tendency towards relaxation. Such results could suggest potential applications to other domains, including personalized treatments based on odors and foods in neuropsychiatric and eating disorders.