Stress detection using wearable physiological and sociometric sensors

Stress remains a significant social problem for individuals in modern societies. This paper presents a machine learning approach for the automatic detection of stress of people in a social situation by combining two sensor systems that capture physiological and social responses. We compare the performance using different classifiers including support vector machine, AdaBoost, and k-nearest neighbour. Our experimental results show that by combining the measurements from both sensor systems, we could accurately discriminate between stressful and neutral situations during a controlled Trier social stress test (TSST). Moreover, this paper assesses the discriminative ability of each sensor modality individually and considers their suitability for real time stress detection. Finally, we present an study of the most discriminative features for stress detection

Emotion Detection Research: A Systematic Review Focuses on Data Type, Classifier Algorithm, and Experimental Methods

There is a lot of research being done on detecting human emotions. Emotion detection models are developed based on physiological data. With the development of low-cost wearable devices that measure human physiological data such as brain activity, heart rate, and skin conductivity, this research can be conducted in developing countries like Southeast Asia. However, as far as the author's research is concerned, a literature review has yet to be found on how this research on emotion detection was carried out in Southeast Asia. Therefore, this study aimed to conduct a systematic review of emotion detection research in Southeast Asia, focusing on the selection of physiological data, classification methods, and how the experiment was conducted according to the number of participants and duration. Using PRISMA guidelines, 22 SCOPUS-indexed journal articles and proceedings were reviewed. The review found that physiological data were dominated by brain activity data with the Muse Headband, followed by heart rate and skin conductivity collected with various wristbands, from around 5-31 participants, for 8 minutes to 7 weeks. Classification analysis applies machine learning, deep learning, and traditional statistics. The experiments were conducted primarily in sitting and standing positions, conditioned environments (for developing research), and unconditioned environments (applied research). This review concluded that future research opportunities exist regarding other data types, data labeling methods, and broader applications. These reviews will contribute to the enrichment of ideas and the development of emotion recognition research in Southeast Asian countries in the future

Enhancing Proprioception and Regulating Cognitive Load in Neurodiverse Populations through Biometric Monitoring with Wearable Technologies

This paper considers the realm of wearable technologies and their prospective applications for individuals with neurodivergent conditions, specifically Autism Spectrum Disorders (ASDs). The study undertakes a multifaceted analysis that encompasses biomarker sensing technologies, AI-driven biofeedback mechanisms, and haptic devices, focusing on their implications for enhancing proprioception and social interaction among neurodivergent populations. While wearables offer a range of opportunities for societal advancement, a discernable gap remains: a scarcity of consumer-oriented applications tailored to the unique physiological and psychological needs of these individuals. Key takeaways underscore the emergent promise of tailored auditory stimuli in workplace dynamics and the efficacy of haptic feedback in sensory substitution. The investigation concludes with an urgent call for multidisciplinary research aimed at the development of specific consumer applications, rigorous empirical validation, and an ethical framework encompassing data privacy and user consent. As the pervasiveness of technology in daily life continues to expand, the article posits that there is an imperative for future research to shift from generalized solutions to individualized applications, thereby ensuring that the spectrum of wearable technology truly accommodates the full scope of human neurodiversity

Linking physical and social environments with mental health in old age: A multisensor approach for continuous real-life ecological and emotional assessment

Background: Urban stress is mentioned as a plausible mechanism leading to chronic stress, which is a risk factor of depression. Yet, an accurate assessment of urban stressors in environmental epidemiology requires new methods. This article discusses methods for the sensor-based continuous assesment of geographic environments, stress and depressive symptoms in older age. We report protocols of the promoting mental well-being and healthy ageing in cities (MINDMAP) and Healthy Aging and Networks in Cities (HANC) studies nested in the RECORD Cohort as a background for a broad discussion about the theoretical foundation and monitoring tools of mobile sensing research in older age. Specifically, these studies allow one to compare how older people with and without depression perceive, navigate and use their environment; and how the built environments, networks of social contacts, and spatial mobility patterns influence the mental health of older people. Methods: Our research protocol combines (1) Global Positioning System (GPS) and accelerometer tracking and a GPS-based mobility survey to assess participants' mobility patterns, activity patterns and environmental exposures; (2) proximity detection to assess whether household members are close to each other; (3) ecological momentary assessment to track momentary mood and stress and environmental perceptions; and (4) electrodermal activity for the tentative prediction of stress. Data will be compared within individuals (at different times) and between persons with and without depressive symptoms. Conclusion: The development of mobile sensing and survey technologies opens an avenue to improve understanding of the role of momentary stressors and resourcing features of residential and non-residential environments for older populations' mental health. However, validation, privacy and ethical aspects are important issues to consider

Beneath the surface:How social inhibition affects stress and emotion regulation

Sociale geremdheid is een persoonlijkheidskenmerk dat gekenmerkt wordt door angst voor, en het vermijden van, onbekende situaties. Sociaal geremde mensen zijn gevoeliger voor sociale dreiging en onderdrukken emotionele expressie, gedachten en gedragingen tijdens sociale interacties. Eerder onderzoek laat zien dat sociale geremdheid samen zou kunnen hangen met een verslechterde psychologische en lichamelijke gezondheid, maar hoe en waarom dit zo is bij sociaal geremde volwassenen is nog onduidelijk. Daarom was het doel van dit proefschrift om meer inzicht te krijgen in sociale geremdheid bij volwassenen, en kennis te vergaren over de lichamelijke en psychologische processen die gerelateerd zijn aan dit persoonlijkheidskenmerk. Onderzoeksmethoden De eerste stap was om een meetinstrument te ontwikkelen dat sociale geremdheid bij volwassenen betrouwbaar en valide kan meten. Met dit meetinstrument waren we in staat om te bekijken in hoeverre mensen met en zonder deze persoonlijkheidstrek van elkaar verschillen op bepaalde uitkomsten. Daarna hebben we een aantal stress- en emotieregulatie experimenten uitgevoerd in het Gedrags-fysiologisch Onderzoekslaboratorium (GO-Lab) om te bestuderen hoe sociaal geremde mensen reageren op stress en hoe ze omgaan met bepaalde emoties (verdriet, boosheid). Belangrijkste conclusies De uitkomsten van dit proefschrift laten zien dat sociaal geremde mensen meer psychologische en lichamelijke stress ervaren en minder goed kunnen omgaan met negatieve emoties, wat op den duur kan leiden tot stress-gerelateerde gezondheidsproblemen. Dit komt voornamelijk doordat sociaal geremde mensen sociale situaties als bedreigend ervaren en daardoor meer op hun hoede zijn, wat zorgt voor een herhaalde activatie van het stress-systeem. Daarnaast hebben sociaal geremde mensen de neiging om de (negatieve) gevoelens die ze ervaren te vermijden of onderdrukken, om niet te laten zien hoe ze zich echt voelen, uit angst voor afwijzing van anderen. Het vermijden en onderdrukken van emoties hangt samen met het ervaren van meer angst en stress, en zou een risico factor kunnen zijn voor het ontwikkelen van psychologische en lichamelijke aandoeningen. Belangrijkste aanbevelingen De bevindingen tonen aan dat het belangrijk is om sociaal geremde mensen te ondersteunen bij het managen van hun emotionele en lichamelijke welzijn. Het ontwikkelen en testen van interventies die gericht zijn op het emotionele en lichamelijke risicoprofiel van sociale remming is daarom essentieel

Design and Development of a Real-Time Bio-Sensing System Assessing Student Mental Workload and Engagement

Ο εντοπισμός του επακριβούς επιπέδου προσήλωσης και εμπλοκής των μαθητών με το περιεχόμενο διδασκαλίας στην τάξη είναι ένας από τους πιο μεγαλεπήβολους στόχους των ερευνητών της εκπαιδευτικής και επιστημονικής κοινότητας. (Lang, 1995, Grossberg, 1987). Σχετικές διεπιστημονικές ερευνητικές προσπάθειες προσαύξησης ενδιαφέροντος και εντοπισμού της αποτελεσματικότητας των διδακτικών πρακτικών βασίζονται σε τυπικές μελέτες από τον χώρο της ψυχολογίας, της παιδαγωγικής, της παιδοψυχολογίας και της ψυχοφυσιολογίας. Νέες τεχνολογίες έχουν εισάγει διαγνωστικές συσκευές δανεισμένες από τον χώρο της ιατρικής με σκοπό να εκμεταλλευτούν τις δυνατότητες μετρήσεων βιολογικών σημάτων τα οποία αποτελούν επιβεβαιωμένες εκφράσεις ψυχοφυσιολογικών καταστάσεων οι οποίες μπορούν να μεταφραστούν σε εκδηλώσεις διέγερσης και διάθεσης. Οι ιατρικές συσκευές απαιτούν εργαστηριακό περιβάλλον λόγω των αναγκών χρήσης ηλεκτροδίων, κινητικών περιορισμών, συγχρονισμού και ομοιομορφίας των στοιχείων που προκύπτουν και γι’ αυτό τον λόγο δεν μπόρεσαν ποτέ να αποδόσουν μια προσιτή λύση εφαρμόσιμη ευρύτερα σε εκπαιδευτικό περιβάλλον. Στην παρούσα μελέτη, αναλύονται οι επιδόσεις μιας ειδικά κατασκευασμένης ηλεκτρονικής συσκευής, σχεδιασμένης ώστε να εξεταστούν οι δυνατότητες να εξαχθούν δείκτες ψυχοσωματικών εκφράσεων του χρήστη, με την δυνατότητα να χρησιμοποιείται εύχρηστα στην τάξη χωρίς ηλεκτρόδια και επηρεασμούς από προσαρτήσεις. Το ολοκληρωμένο σύστημα μέτρησης και αποτύπωσης συμπερασμάτων είναι βασισμένο σε μοντελοποίηση συμπεριφορών αλλαγής του καρδιακού παλμού και της ειδικής διηλεκτρικής αγωγιμότητας του δέρματος σε πραγματικό χρόνο. Η συσκευή χρησιμοποιεί οπτικούς και διηλεκτρικούς αισθητήρες επαφής και έχει μελετηθεί σε αντιπαραβολή με διαβαθμισμένα περιβάλλοντα προκλητών καταστάσεων νοητικής φόρτισης. Σειρές πειραματικών διαδικασιών εφαρμοσμένες σε διαβαθμισμένα σενάρια πρόκλησης ψυχοσωματικών διεγέρσεων έχουν ολοκληρωθεί για επικύρωση, μελέτη επιδόσεων και λειτουργία του συστήματος ακόμη και σε σύγκριση με εμπορικό προϊόν. Πειραματικά αποτελέσματα δείχνουν αξιόλογους συσχετισμούς του μοντέλου και των επιδόσεων του συστήματος με τις αναμενόμενες αποκρίσεις με ενθαρρυντικά ποσοστά ακρίβειας.Facing the challenge of improving adaptive interaction in educational technologies scientists and educators have turned their focal point in diverse areas ranging from educational, teaching and behavioural psychology to cognitive, affective and perceptual neuroscience. The introduction of digital technologies and interactive media tools in education has shown improved learning efficiency, much higher memory activation and assimilation than verbal teaching, notably due to enhancing motivation achieved by employing approaches attracting student’s attention. Excelling aspects of audio visual presentation proved highly valuable particularly in classes with multi ethnic groups of students, as for example consistency between definitions and objects which were verbally and visually defined, eliminating possible misconceptions caused by mishearing or misinterpretation by the learner. Taking it all one step further as to how an educational system could be even more efficient, a new element would be needed revealing a credible judgment of learning scores and effectiveness of the learning process instantaneously as for example inner levels of activation and satisfaction. In fact, this could be made possible using existing technologies if subconscious neurophysiological responses of a learner could be ascertained and inferred to psycho-somatic conditions as they occur. A system including bio-sensing, data analysis and processing in real time able to provide quantified markers of psychosomatic states of a learner would help enormously in next generations of educational practice. Incorporating data of student engagement and active involvement could help to deduce the interest of a learner, which is known to improve sensitisation in implicit, incidental and also in classical learning. Experimental settings used in previous studies attempting to incorporate physiological responses and interpretations into responsive educational settings have faced major obstacles. Operational issues caused by the requirements of the devices used for the acquisition of physiological signals such as electrodes and movement restrictions have reduced the progress of such settings to laboratory environments. In such settings as described above, the effects of wiring harnesses and sensory components produced an additional psychological burden on the participants. Consequently, the need to approach the physiological data acquisition from a new angle with seamless and unnoticeable operation is apparent. The challenge to design, develop and validate a system that being minimally obstructive and literally unnoticed by the user would uncover combined subconscious expressions of a learner was the primary objective of this research. Physiological data of Heart Rate and Skin Trans-Conductance (Electro-dermal Response) elected as vitally important and highly appropriate to produce the input of data required to evaluate a behavioural concept model. The behavioural assessment model entailed vector classifiers producing directional interpretations of measurements. Directional information (Gradient response) has been derived by comparison of measurements to previously measured values in real time. Assessing the effectiveness and accuracy of the adopted model to deduce attention and engagement of a learner in real time formed the second major objective. For this purpose, a series of relevant experimental methodologies have been employed. Data produced using formal personality assessments have also been investigated in conjunction with those derived from physiological responses in order to identify personality related particularities. The final part of this work has been supplemented by propositions and suggestions with regards to various applications of the system in accomplishment of the initial aims

Artificial intelligence within the interplay between natural and artificial computation:Advances in data science, trends and applications

Artificial intelligence and all its supporting tools, e.g. machine and deep learning in computational intelligence-based systems, are rebuilding our society (economy, education, life-style, etc.) and promising a new era for the social welfare state. In this paper we summarize recent advances in data science and artificial intelligence within the interplay between natural and artificial computation. A review of recent works published in the latter field and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in artificial intelligence. Moreover, this paper aims to provide a complete analysis and some relevant discussions of the current trends and insights within several theoretical and application fields covered in the essay, from theoretical models in artificial intelligence and machine learning to the most prospective applications in robotics, neuroscience, brain computer interfaces, medicine and society, in general.BMS - Pfizer(U01 AG024904). Spanish Ministry of Science, projects: TIN2017-85827-P, RTI2018-098913-B-I00, PSI2015-65848-R, PGC2018-098813-B-C31, PGC2018-098813-B-C32, RTI2018-101114-B-I, TIN2017-90135-R, RTI2018-098743-B-I00 and RTI2018-094645-B-I00; the FPU program (FPU15/06512, FPU17/04154) and Juan de la Cierva (FJCI-2017–33022). Autonomous Government of Andalusia (Spain) projects: UMA18-FEDERJA-084. Consellería de Cultura, Educación e Ordenación Universitaria of Galicia: ED431C2017/12, accreditation 2016–2019, ED431G/08, ED431C2018/29, Comunidad de Madrid, Y2018/EMT-5062 and grant ED431F2018/02. PPMI – a public – private partnership – is funded by The Michael J. Fox Foundation for Parkinson’s Research and funding partners, including Abbott, Biogen Idec, F. Hoffman-La Roche Ltd., GE Healthcare, Genentech and Pfizer Inc