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This review investigates the effects of psychological stress on the human body measured through biosignals. When a potentially threatening stimulus is perceived, a cascade of physiological processes occurs mobilizing the body and nervous system to confront the imminent threat and ensure effective adaptation. Biosignals that can be measured reliably...
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... Employing a methodology based on clinical research conditions (refer to Portney, 2020;Lewith et al., 2010), we tested the alleged protective effect by exposing volunteer participants-referred to hereafter as testeesto the product's influence while in proximity to the Areca Plus Card™. Several physiological parameters (see Chapter 2.4) were monitored by the appropriate measurement protocol (Giannakakis et al., 2022). In the testing, we did not tackle the question of the possible technical (physical) protecting capacity of the tested product. ...
The company 2023. LTD requested scientific validation of its product Areca Plus Card™, which allegedly protects the user from microwave mobile phone radiation. Validation was done via testing on human organisms following clinical research conditions. In the testing, volunteers were exposed to mobile phone radiation while being exposed to the product that was attached to the back of the mobile phone. The testing shows high overall statistical differences (meaning differences in p-value and Cohen's D) between the two testing situations, where in both cases, the testees were exposed to the same procedure and the same dose of mobile phone microwave radiation during the whole time of testing. Since the test reveals significant differences between the allegedly protected and unprotected situations, we conclude that the named product does impact the human organism. It appears to offset, at least partially, the biological effects of microwave radiation from 4G technology mobile phones.
... To counteract this issue, the field of HRI can borrow inspiration from affective computing research [18][19][20], in which various physiological signals such as heart rate, skin conductance, respiration rate, and brain activity are used as objective measures of stress levels and emotional responses [20][21][22]. Unlike self-reports that can only be collected once the interaction with the robot is over, physiological signals are time series data that can be recorded with high temporal resolution during the interaction [19]. Additionally, they are less prone to individual biases as they capture unconscious or automatic responses that participants might not express or even be aware of. ...
... GSRs, also known as electrodermal activity or skin conductance, are obtained by continuously measuring the electrical changes in the skin of the palm or fingers caused by sweat gland activity in response to emotional arousal [26]. Although the usage of GSRs for arousal detection and stress monitoring has been well documented in the affective computing literature [21,26,27] and biofeedback research for stress management [28], very few studies have employed it in HRI settings. For instance, Jerčić et al. [29] applied GSRs, together with a heart rate sensor, to a human-robot collaboration scenario to measure the impact of physiological arousal on people's performance in a decision-making game task when they collaborated with a robot vs. another human. ...
The current study investigated the effectiveness of social robots in facilitating stress management interventions for university students by evaluating their physiological responses. We collected electroencephalogram (EEG) brain activity and Galvanic Skin Responses (GSRs) together with self-reported questionnaires from two groups of students who practiced a deep breathing exercise either with a social robot or a laptop. From GSR signals, we obtained the change in participants’ arousal level throughout the intervention, and from the EEG signals, we extracted the change in their emotional valence using the neurometric of Frontal Alpha Asymmetry (FAA). While subjective perceptions of stress and user experience did not differ significantly between the two groups, the physiological signals revealed differences in their emotional responses as evaluated by the arousal–valence model. The Laptop group tended to show a decrease in arousal level which, in some cases, was accompanied by negative valence indicative of boredom or lack of interest. On the other hand, the Robot group displayed two patterns; some demonstrated a decrease in arousal with positive valence indicative of calmness and relaxation, and others showed an increase in arousal together with positive valence interpreted as excitement. These findings provide interesting insights into the impact of social robots as mental well-being coaches on students’ emotions particularly in the presence of the novelty effect. Additionally, they provide evidence for the efficacy of physiological signals as an objective and reliable measure of user experience in HRI settings.
... -Stress: Detecting stress becomes crucial, especially with the introduction of gamification elements in the training session, where the patient may experience stress, particularly if the exercise surpasses their current skill level. Extensive research has explored diverse modalities for stress detection, encompassing physiological signals, speech, gestures, and contextual behavioral patterns [Koceska et al., 2021, Larradet et al., 2020, Giannakakis et al., 2019, Heimerl et al., 2023. Physiological signals, including ECG, BVP, EDA, and respiration, have demonstrated high efficacy in stress detection [Gedam and Paul, 2021, Prajod et al., 2024, Smets et al., 2018. ...
Individuals with diverse motor abilities often benefit from intensive and specialized rehabilitation therapies aimed at enhancing their functional recovery. Nevertheless, the challenge lies in the restricted availability of neurorehabilitation professionals, hindering the effective delivery of the necessary level of care. Robotic devices hold great potential in reducing the dependence on medical personnel during therapy but, at the same time, they generally lack the crucial human interaction and motivation that traditional in-person sessions provide. To bridge this gap, we introduce an AI-based system aimed at delivering personalized, out-of-hospital assistance during neurorehabilitation training. This system includes a rehabilitation training device, affective signal classification models, training exercises, and a socially interactive agent as the user interface. With the assistance of a professional, the envisioned system is designed to be tailored to accommodate the unique rehabilitation requirements of an individual patient. Conceptually, after a preliminary setup and instruction phase, the patient is equipped to continue their rehabilitation regimen autonomously in the comfort of their home, facilitated by a socially interactive agent functioning as a virtual coaching assistant. Our approach involves the integration of an interactive socially-aware virtual agent into a neurorehabilitation robotic framework, with the primary objective of recreating the social aspects inherent to in-person rehabilitation sessions. We also conducted a feasibility study to test the framework with healthy patients. The results of our preliminary investigation indicate that participants demonstrated a propensity to adapt to the system. Notably, the presence of the interactive agent during the proposed exercises did not act as a source of distraction; instead, it positively impacted users' engagement.
... Our study successfully induced stress among participants during automated driving using the NDRT. Arousal and negative feelings were increased in response to stress, as expected (Giannakakis et al., 2019;Hidalgo-Muñoz et al., 2019). Additionally, we found UNOBTRUSIVE DRIVING RESPIRATION GUIDANCE that the presence of stress resulted in increased perseveration towards the NDRT following the takeover alarm. ...
Stress impacts driving-related cognitive functions like attention and decision-making, and may arise in automated vehicles due to non-driving tasks. Unobtrusive relaxation techniques are needed to regulate stress without distracting from driving. Tactile wearables have shown efficacy in stress regulation through respiratory guidance, but individual variations may affect their efficacy. This study assessed slow-breathing tactile guidance under different stress levels on 85 participants. Physiological, behavioral and subjective data were collected. The influence of individual variations (e.g., driving habits and behavior, personality) using logistic regression analysis was explored. Participants could follow the guidance and adjust breathing while driving, but subjective efficacy depended on individual variations linked to different efficiency in using the technique, in relation with its attentional cost. An influence of factors linked to the evaluation of context criticality was also found. The results suggest that considering individual and contextual variations is crucial in designing and using such techniques in demanding driving contexts. In this line some design recommendations and insights for further studies are provided.
... Besides, photoplethysmography (PPG) offers insights into blood flow status and peripheral vascular characteristics from a hemodynamic standpoint. Thus, fusing these CVS data from different views can facilitate a deeper understanding of CVS characteristics, unlocking substantial potential for diverse healthcare applications by extracting potential patterns and interactions within the data [1] [2]. Single-lead ECG and PPG offer a comprehensive understanding of CVS from complementary perspectives and are widely integrated into various wearable devices due to their portability and low cost. ...
The progression of deep learning and the widespread adoption of sensors have facilitated automatic multi-view fusion (MVF) about the cardiovascular system (CVS) signals. However, prevalent MVF model architecture often amalgamates CVS signals from the same temporal step but different views into a unified representation, disregarding the asynchronous nature of cardiovascular events and the inherent heterogeneity across views, leading to catastrophic view confusion. Efficient training strategies specifically tailored for MVF models to attain comprehensive representations need simultaneous consideration. Crucially, real-world data frequently arrives with incomplete views, an aspect rarely noticed by researchers. Thus, the View-Centric Transformer (VCT) and Multitask Masked Autoencoder (M2AE) are specifically designed to emphasize the centrality of each view and harness unlabeled data to achieve superior fused representations. Additionally, we systematically define the missing-view problem for the first time and introduce prompt techniques to aid pretrained MVF models in flexibly adapting to various missing-view scenarios. Rigorous experiments involving atrial fibrillation detection, blood pressure estimation, and sleep staging-typical health monitoring tasks-demonstrate the remarkable advantage of our method in MVF compared to prevailing methodologies. Notably, the prompt technique requires finetuning less than 3% of the entire model's data, substantially fortifying the model's resilience to view missing while circumventing the need for complete retraining. The results demonstrate the effectiveness of our approaches, highlighting their potential for practical applications in cardiovascular health monitoring. Codes and models are released at URL.
... Focusing more on the beta band in specific brain areas, studies have found increased frontal activity correlated with vigilance [62,63], while in the parietal brain region, beta activity is correlated with stress states [64,65] and tends to increase due to psychosocial stress in the reactive and recovery phase [66,67]. Moreover, enhanced beta activity in posterior brain areas has been associated with increased anxiety in adults [68]. ...
... The THI provides additional data to the traditional psychoacoustic assessment (e.g., pitch and loudness matching, minimum masking levels, residual inhibition). Tinnitus severity is categorized as follows: no handicap (0-16 points), 'mild' (18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36), 'moderate' (38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50)(51)(52)(53)(54)(55)(56), 'severe' (58)(59)(60)(61)(62)(63)(64)(65)(66)(67)(68)(69)(70)(71)(72)(73)(74)(75)(76), or 'catastrophic' tinnitus handicap (78-100 points). The THI has been used in neurophysiological studies and is widely recommended as a research tool for rating the severity of tinnitus [84]. ...
Background: Despite substantial progress in investigating its psychophysical complexity, tinnitus remains a scientific and clinical enigma. The present study, through an ecological and multidisciplinary approach, aims to identify associations between electroencephalographic (EEG) and psycho-audiological variables. Methods: EEG beta activity, often related to stress and anxiety, was acquired from 12 tinnitus patients (TIN group) and 7 controls (CONT group) during an audio cognitive task and at rest. We also investigated psychological (SCL-90-R; STAI-Y; BFI-10) and audiological (THI; TQ12-I; Hyperacusis) variables using non-parametric statistics to assess differences and relationships between and within groups. Results: In the TIN group, frontal beta activity positively correlated with hyperacusis, parietal activity, and trait anxiety; the latter is also associated with depression in CONT. Significant differences in paranoid ideation and openness were found between groups. Conclusions: The connection between anxiety trait, beta activity in the fronto-parietal cortices and hyperacusis provides insights into brain functioning in tinnitus patients, offering quantitative descriptions for clinicians and new multidisciplinary treatment hypotheses.
... Stress affects more than 70% of the population. Long-term stress causes reduced immunity, cancer, cardiovascular disease, depression, diabetes, and drug addiction [1]. Stress damages mental and physical health. ...
Nowadays, one of the most time-consuming and complex study subjects is predicting working professionals' stress levels. It is thus crucial to estimate active professionals' stress levels to aid their professional development. Several machine learning (ML) and deep learning (DL) methods have been created in earlier articles for this goal. But they also have drawbacks, such as increased design complexity, a high rate of misclassification, a high incidence of mistakes, and reduced efficiency. Considering these issues, the objective of this study is to make a prognosis about the stress levels experienced by working professionals by using a cutting-edge deep learning model known as the convolutional neural networks (CNN). In this paper, we offer a model that combines CNN-based classification with dataset preprocessing, feature extraction, and optimum feature selection using principal component analysis (PCA). When the raw data is preprocessed, duplicate characteristics are eliminated, and missing values are filled.
... On the other hand, recent studies [6] [7] indicate the importance of using physiological indicators for measuring stress. Physiological measures such as electrocardiogram (ECG) [8], electrodermal activity (EDA) [9], electroencephalographic (EEG) signals [10], systolic and diastolic blood pressure (SBP and DBP) [11], respiration rate (RSP) [12] and voice pitch (f0) [13] have shown consistent patterns of change during stress condition [14]. EEG signals, in particular, offer a direct reflection of neural activity, facilitating a more direct understanding of how the brain responds to stressors. ...
... Samarpita et al. [24] trained a diverse set of machine learning algorithms, such as RF, DT, K-Nearest Neighbors (KNN), MLP, SVM, Adaboost, and Extreme Gradient Boosting (XGBoost). Baliga et al. [25] extracted only the alpha (8-13 Hz) and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) Hz) bands from EEG records and then applied two binary classification algorithms ("under stress" or "not under stress"), SVM and LSTM. Marthinsen et al. [26] proposed a cost-effective minimally intrusive framework with only eight EEG channels selected with a Genetic Algorithm (GA). ...
... Researchers have successfully detected the affective states of a person by considering An Application of Computer Vision Techniques to Study the Relationship between Mental Stress and Pupil Diameter among University Students pupil dilation and thermal facial features [19]. Researchers have considered many physiological parameters like EEG, ECG, EDA, EMG, heart rate, pupil diameter, speech, skin temperature etc. for successful mental stress detection [20]. Like other substantial features, eye blinks carry a strong association with our mental status. ...
Stress is a state of mental tension, which helps us to cope with challenges in our life. It makes us progressive when it is positive, but excessive negative stress that perseveres for a long time leads to a state of depressiveness. Longer stressed stage of a human being changes the size, functionality and frequency of response of many internal and external body parameters. By applying computer vision techniques, these changes of body parameters can be tracked to get useful information about the mental stress for a stress affected person. Many studies show the pupil diameter varies significantly with the effect of stress. Our work is based on the study of variation of pupil diameters of stress affected and not affected university students. With the application of different supervised machine learning algorithms, we have observed that the pupil dilates more in case of stress affected students than non-stressed students. We have also found that the pupils of the students dilates more when they were in positive emotional states than their negative emotional states. This work will be helpful for researchers who are working in the field of emotion detection and recognition and affective disorder analysis.
... [1]. These demands are called stressors and can be further categorized into physical/ environmental or mental/ task-related stressors [2]. In our daily lives, we face various challenges and experience different levels of mental stress. ...
... The Automatic Nervous System (ANS) controls the involuntary movements of the body, such as skin conductivity, heart rate, and pupil dilation [27]- [29]. Since stress causes dynamic changes in the ANS, there are several techniques for detecting stress level changes [2], [28]. The most commonly used signals in stress detection are Heart Rate (HR), Heart Rate Variability (HRV), Galvanic Skin Response (GSR), blood pressure, and respiration rate. ...
... [30]. Previous studies have shown that, under stress, HR, GSR, blood pressure, and respiration rate increase, and the R-peakto-R-peak interval in HRV shorten [2], [28]. Even though HR has been used extensively in previous studies, HR might not accurately reflect mental stress in situations involving body movement, as HR increases during exercise. ...
Objective: To investigate the effects of different approaches to EEG preprocessing, channel montage selection, and model architecture on the performance of an online-capable stress detection algorithm in a classroom scenario. Methods: This analysis used EEG data from a longitudinal stress and fatigue study conducted among university students. Their self-reported stress ratings during each class session were the basis for classifying EEG recordings into either normal or elevated stress states. We used a data-processing pipeline that combined Artifact Subspace Reconstruction (ASR)and an Independent Component Analysis (ICA)-based method to achieve online artifact removal. We compared the performance of a Linear Discriminant Analysis (LDA) and a 4-layer neural network as classifiers. We opted for accuracy, balanced accuracy, and F1 score as the metrics for assessing performance. We examined the impact of varying numbers of input channels using different channel montages. Additionally, we explored different window lengths and step sizes during online evaluation. Results: Our online artifact removal method achieved performance comparable to the offline ICA method in both offline and online evaluations. A balanced accuracy of 77% and 78% in an imbalanced binary classification were observed when using the 11-frontal-channel LDA model with the proposed artifact removal method. Moreover, the model performance remained intact when changing the channel montage from 30 full-scalp channels to just 11 frontal channels. During the online evaluation, we achieved the highest balanced accuracy (78%) with a window length of 20 seconds and a step size of 1 second. Significance: This study comprehensively investigates the deployment of stress detection in real-world scenarios. The findings of this study provide insight into the development of daily mental stress monitoring.
