Stress Reduction Using Bilateral Stimulation in Virtual Reality

The goal of this research is to integrate Virtual Reality (VR) with the bilateral stimulation used in EMDR as a tool to relieve stress. We created a 15 minutes relaxation training program for adults in a virtual, relaxing environment in form of a walk in the woods. The target platform for the tool is HTC Vive, however it can be easily ported to other VR platforms. An integral part of this tool is a set of sensors, which serves as physiological measures to evaluate the effectiveness of such system. What is more, the system integrate visual (passing sphere), auditory (surround sound) and tactile signals (vibration of controllers). A pilot treatment programme, incorporating the above mentioned VR system, was carried out. Experimental group consisting of 28 healthy adult volunteers (office workers), participated in three different sessions of relaxation training. Before starting, baseline features such as subjectively perceived stress, mood, heart rate, galvanic skin response and muscle response were registered. The monitoring of physiological indicators is continued during the training session and one minute after its completion. Before and after the session, volunteers were asked to re-fill questionnaires regarding the current stress level and mood. The obtained results were analyzed in terms of variability over time: before, during and after the session

Human Health Engineering Volume II

In this Special Issue on “Human Health Engineering Volume II”, we invited submissions exploring recent contributions to the field of human health engineering, i.e., technology for monitoring the physical or mental health status of individuals in a variety of applications. Contributions could focus on sensors, wearable hardware, algorithms, or integrated monitoring systems. We organized the different papers according to their contributions to the main parts of the monitoring and control engineering scheme applied to human health applications, namely papers focusing on measuring/sensing physiological variables, papers highlighting health-monitoring applications, and examples of control and process management applications for human health. In comparison to biomedical engineering, we envision that the field of human health engineering will also cover applications for healthy humans (e.g., sports, sleep, and stress), and thus not only contribute to the development of technology for curing patients or supporting chronically ill people, but also to more general disease prevention and optimization of human well-being

Effectiveness analysis of traditional and mixed reality simulations in medical training: a methodological approach for the assessment of stress, cognitive load and performance

La simulazione nell'educazione in medicina è considerata un metodo di formazione in grado di migliorare le competenze cliniche e il comportamento degli operatori sanitari e, di conseguenza, la qualità dell'assistenza per il paziente. Inoltre, l'utilizzo di nuove tecnologie come la Realtà Aumentata, offre ai discenti l'opportunità di esercitarsi in un ambiente immersivo. L'opportunità di sperimentare questo innovativo metodo didattico è efficace non solo nel ridurre il rischio di errori e approcci sbagliati ma anche nel provare ansia e stress simili a quelli avvertiti nella pratica reale. La sfida sta nel trovare il giusto equilibrio. I discenti devono infatti provare lo stesso stress che avvertirebbero lavorando ad un vero caso clinico ma, allo stesso tempo, devono essere controllati ed evitati possibili disturbi da stress post-traumatico, verificabili soprattutto nel campo della gestione delle emergenze (pronto soccorso). Inoltre, è fondamentale anche ottenere alte prestazioni e un apprendimento adeguato, evitando sovraccarichi cognitivi che influenzerebbero negativamente l’apprendimento. Tuttavia, ad oggi mancano ancora studi approfonditi sull'impatto che le simulazioni mediche hanno su stress, frustrazione, carico cognitivo e apprendimento dei discenti. Per questo motivo, l'obiettivo principale di questo studio è valutare l'efficacia del training tramite simulazione, analizzando prestazioni, ansia, stress e carico cognitivo durante simulazioni cliniche tradizionali (con manichino) ed avanzate (in realtà mista). A questo scopo, è stato sviluppato un approccio metodologico strutturato e completo per valutare le prestazioni, le condizioni emotive e cognitive degli studenti. Questo comprende l'acquisizione e l'analisi di parametri psicologici (valutazione soggettiva), segnali biometrici (valutazione oggettiva) e prestazioni. Questa indagine consente di evidenziare i punti deboli delle simulazioni e offre l'opportunità di definire utili linee guida per la riprogettazione e l'ottimizzazione delle stesse. La metodologia è stata applicata su tre casi studio: il primo si riferisce a simulazioni ad alta fedeltà per la gestione del paziente in pronto soccorso, il secondo si riferisce a simulazioni a bassa fedeltà per la pratica della rachicentesi. Per il terzo caso studio, è stato progettato e sviluppato un prototipo di simulatore in realtà mista per la rachicentesi, con l'obiettivo di migliorare il senso di realismo e immersione della simulazione a bassa fedeltà. 148 studenti sono stati coinvolti nei primi due casi studio osservazionali, mentre soltanto 36 studenti hanno preso parte allo studio pilota sulla simulazione in realtà mista. In tutti i casi di studio sono state effettuate analisi descrittive delle prestazioni, degli stati cognitivi ed emotivi. Per le simulazioni ad alta e bassa fedeltà, le analisi di regressione statistica hanno evidenziato quali variabili influenzano le prestazioni, lo stress e il carico cognitivo degli studenti. Per lo studio pilota sulla realtà mista, l'analisi della user experience ha sottolineato i limiti tecnici della nuova tecnologia.Simulation in medical education is considered a training method capable of improving clinical competence and practitioners’ behaviour, and, consequently quality of care and patient’s outcome. Moreover, the use of new technologies, such as augmented reality, offers to the learners the opportunity to engage themselves in an immersive environment. The opportunity to experiment with this innovative instructional method is effective not only in reducing the risk of errors and wrong approaches but also in experiencing anxiety and stress as in real practice. The challenge is to find the right stress balance: learners have to feel as if they were practicing in the real stressful clinical case, and, at the same time, post-traumatic stress disorders, verifiable especially in the emergency field, must be controlled and avoided. Moreover, it is fundamental also to obtain high performance and learning, thus avoiding cognitive overloads. However, extensive researches about the impact of medical simulations on students’ stress, frustration, cognitive load, and learning are still lacking. For this reason, the main objective of this study is to assess simulation training effectiveness by analysing performance, anxiety, stress, and cognitive load during traditional (with manikin) and advanced (with augmented reality) clinical simulations. A structured and comprehensive methodological approach to assess performance, emotional and cognitive conditions of students has been developed. It includes the acquisition and analysis of psychological parameters (subjective assessment), biometric signals (objective assessment), and task performance. This investigation allows to point out simulations’ weaknesses and offers the opportunity to define useful optimisation guidelines. The methodology has been applied to three case studies: the first one refers to high-fidelity simulations, for the patient management in the emergency room, the second one refers to low-fidelity simulation for rachicentesis. For the third case study, a prototype of a mixed reality simulator for the rachicentesis practice has been designed and developed aiming at improving the sense of realism and immersion of the low-fidelity simulation. While 148 students have been enrolled in the first two case studies, only 36 students have taken part in the pilot study about mixed reality simulation. Descriptive analysis about performance, cognitive and emotional states have been done in all the case studies. For the high-fidelity and low-fidelity simulations, the statistical regression analysis has pointed out which variables affect students’ performance, stress, and cognitive load. For the pilot study about mixed reality, the user experience analysis highlighted the technical limitations of the new technology

Wearables measuring electrodermal activity to assess perceived stress in care:A scoping review

Background:Chronic stress responses can lead to physical and behavioural health problems, often experienced and observed in the care of people with intellectual disabilities or people with dementia. Electrodermal activity (EDA) is a bio-signal for stress, which can be measured by wearables and thereby support stress management. However, the how, when and to what extent patients and healthcare providers can benefit is unclear. This study aims to create an overview of available wearables enabling the detection of perceived stress by using EDA.Methods:Following the PRISMA-SCR protocol for scoping reviews, four databases were included in the search of peer-reviewed studies published between 2012 and 2022, reporting detection of EDA in relation to self-reported stress or stress-related behaviours. Type of wearable, bodily location, research population, context, stressor type and the reported relationship between EDA and perceived stress were extracted.Results:Of the 74 included studies, the majority included healthy subjects in laboratory situations. Field studies and studies using machine learning (ML) to predict stress have increased in the last years. EDA is most often measured on the wrist, with offline data processing. Studies predicting perceived stress or stress-related behaviour using EDA features, reported accuracies between 42% and 100% with an average of 82.6%. Of these studies, the majority used ML.Conclusion:Wearable EDA sensors are promising in detecting perceived stress. Field studies with relevant populations in a health or care context are lacking. Future studies should focus on the application of EDA-measuring wearables in real-life situations to support stress management

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective

IoT DEVELOPMENT FOR HEALTHY INDEPENDENT LIVING

The rise of internet connected devices has enabled the home with a vast amount of enhancements to make life more convenient. These internet connected devices can be used to form a community of devices known as the internet of things (IoT). There is great value in IoT devices to promote healthy independent living for older adults. Fall-related injuries has been one of the leading causes of death in older adults. For example, every year more than a third of people over 65 in the U.S. experience a fall, of which up to 30 percent result in moderate to severe injury. Therefore, this thesis proposes an IoT-based fall detection system for smart home environments that not only to send out alerts, but also launches interaction models, such as voice assistance and camera monitoring. Such connectivity could allow older adults to interact with the system without concern of a learning curve. The proposed IoT-based fall detection system will enable family and caregivers to be immediately notified of the event and remotely monitor the individual. Integrated within a smart home environment, the proposed IoT-based fall detection system can improve the quality of life among older adults. Along with the physical concerns of health, psychological stress is also a great concern among older adults. Stress has been linked to emotional and physical conditions such as depression, anxiety, heart attacks, stroke, etc. Increased susceptibility to stress may accelerate cognitive decline resulting in conversion of cognitively normal older adults to MCI (Mild Cognitive Impairment), and MCI to dementia. Thus, if stress can be measured, there can be countermeasures put in place to reduce stress and its negative effects on the psychological and physical health of older adults. This thesis presents a framework that can be used to collect and pre-process physiological data for the purpose of validating galvanic skin response (GSR), heart rate (HR), and emotional valence (EV) measurements against the cortisol and self-reporting benchmarks for stress detection. The results of this framework can be used for feature extraction to feed into a regression model for validating each combination of physiological measurement. Also, the potential of this framework to automate stress protocols like the Trier Social Stress Test (TSST) could pave the way for an IoT-based platform for automated stress detection and management

An Analysis of Physiological and Psychological Responses in Virtual Reality and Flat Screen Gaming

Recent research has focused on the effectiveness of Virtual Reality (VR) in games as a more immersive method of interaction. However, there is a lack of robust analysis of the physiological effects between VR and flatscreen (FS) gaming. This paper introduces the first systematic comparison and analysis of emotional and physiological responses to commercially available games in VR and FS environments. To elicit these responses, we first selected four games through a pilot study of 6 participants to cover all four quadrants of the valence-arousal space. Using these games, we recorded the physiological activity, including Blood Volume Pulse and Electrodermal Activity, and self-reported emotions of 33 participants in a user study. Our data analysis revealed that VR gaming elicited more pronounced emotions, higher arousal, increased cognitive load and stress, and lower dominance than FS gaming. The Virtual Reality and Flat Screen (VRFS) dataset, containing over 15 hours of multimodal data comparing FS and VR gaming across different games, is also made publicly available for research purposes. Our analysis provides valuable insights for further investigations into the physiological and emotional effects of VR and FS gaming.Comment: This work has been submitted to the IEEE Transactions on Affective Computing for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Interactions Between Humans, Virtual Agent Characters and Virtual Avatars

Simulations allow people to experience events as if they were happening in the real world in a way that is safer and less expensive than live training. Despite improvements in realism in simulated environments, one area that still presents a challenge is interpersonal interactions. The subtleties of what makes an interaction rich are difficult to define. We may never fully understand the complexity of human interchanges, however there is value in building on existing research into how individuals react to virtual characters to inform future investments. Virtual characters can either be automated through computational processes, referred to as agents, or controlled by a human, referred to as an avatar. Knowledge of interactions with virtual characters will facilitate the building of simulated characters that support training tasks in a manner that will appropriately engage learners. Ultimately, the goal is to understand what might cause people to engage or disengage with virtual characters. To answer that question, it is important to establish metrics that would indicate when people believe their interaction partner is real, or has agency. This study makes use of three types of measures: objective, behavioral and self-report. The objective measures were neural, galvanic skin response, and heart rate measures. The behavioral measure was gestures and facial expressions. Surveys provided an opportunity to gain self-report data. The objective of this research study was to determine what metrics could be used during social interactions to achieve the sense of agency in an interactive partner. The results provide valuable feedback on how users need to see and be seen by their interaction partner to ensure non-verbal cues provide context and additional meaning to the dialog. This study provides insight into areas of future research, offering a foundation of knowledge for further exploration and lessons learned. This can lead to more realistic experiences that open the door to human dimension training

A serious VR game for acrophobia therapy in an urban environment

Much of the costs and dangers of exposure therapy in phobia treatment can be removed through virtual reality (VR). Exposing people to heights, for instance, might sound easy, but it still involves time and money investments to reach a tall building, mountain or bridge. People suffering from milder forms of acrophobia might not even be treated at all, the cost not being worth it. This paper presents a prototype that allows exposure therapy to be done in a controlled environment, in a more comfortable, quick and cheaper way. By applying acrophobia questionnaires, collecting biophysical data and developing a virtual reality game, we can expose volunteers to heights and analyze if there is any change in their fear and anxiety levels. This way, regardless of the initial anxiety level and phobia severity, we can check if there is any post-therapy improvement and verify if virtual reality is a viable alternative to real-world exposure