Experimentally induced stress validated by EMG activity

Challenging Behaviors in Children with Nonverbal Autism: A Questionnaire to Guide the Design of a Wearable Device for Biomarker Recording

Children with non- or minimally verbal autism (nmvASD) commonly display sensory and emotional dysregulations leading to extremely stressful situations that trigger challenging behaviors which are often difficult to treat. Nonetheless, this population remains rarely studied in clinical research. Recent methods use electrophysiological biomarkers as diagnostic tools to detect stress signals, which may be useful in anticipating situations or conditions leading to challenging behaviors in nmvASD. A specific questionnaire was created in order to identify the characteristics of nmvASD children and gather the opinions of future users (parents and caregivers) on the design of a wearable device able to collect stress-related electrophysiological data. The results indicate that approximately 67.5% of respondents (n = 40) would be interested in such a device, both in outpatient and inpatient settings. In 70% of cases, prolonged contact with an object on the trunk is always well accepted by the child. This location was also preferentially chosen by 57.5% of respondents for such a wearable device. The presence of wires could be problematic in 82.5% of cases. About 65% of respondents find it far better to integrate these wearable devices directly into the clothing. These results will help in the development of devices specifically developed for the nmvASD population to enhance their care for behavioral disorders and based on user-center design.

Novel multimodal sensing and machine learning strategies to classify cognitive workload in laparoscopic surgery

BACKGROUND

Surgeons can experience elevated cognitive workload (CWL) during surgery due to various factors including operative technicalities and the environmental demands of the operating theatre. This can result in poorer outcomes and have a detrimental effect on surgeon well-being. The objective measurement of CWL provides a potential solution to facilitate classification of workload levels, however results are variable when physiological measures are used in isolation. The aim of this study is to develop and propose a multimodal machine learning (ML) approach to classify CWL levels using a bespoke sensor platform and to develop a ML approach to impute missing pupil diameter measures due to the effect of blinking or noise.

MATERIALS AND METHODS

Ten surgical trainees performed a simulated laparoscopic cholecystectomy under cognitive conditions of increasing difficulty, namely a modified auditory N-back task with increasing difficulty and a verbal clinical scenario. Physiological measures were recorded using a novel platform (MAESTRO). Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) were used as direct measures of CWL. Indirect measures included electromyography (EMG), electrocardiography (ECG) and pupil diameter (PD). A reference point for validation was provided by subjective assessment of perceived CWL using the SURG-TLX. A multimodal machine learning approach that systematically implements a CNN-BiLSTM, a binary version of the metaheuristic Manta Ray Foraging Optimisation (BMRFO) and a version of Fuzzy C-Means (FCM) called Optimal Completion Strategy (OCS) was used to classify the associated perceived CWL state.

RESULTS

Compared to other state of the art classification techniques, cross-validation results for the classification of CWL levels suggest that the CNN-BLSTM and BMRFO approach provides an average accuracy of 97 % based on the confusion matrix. Additionally, OCS demonstrated a superior average performance of 9.15 % in terms of Root-Mean-Square-Error (RMSE) when compared to other PD imputation methods.

CONCLUSION

Perceived CWL levels were correctly classified using a multimodal ML approach. This approach provides a potential route to accurately classify CWL levels, which may have application in future surgical training and assessment programs as well as the development of cognitive support systems in the operating room.

Heart rate monitoring to detect acute pain in non-verbal patients: a study protocol for a randomized controlled clinical trial

BACKGROUND

Autism entails reduced communicative abilities. Approximately 30% of individuals with autism have intellectual disability (ID). Some people with autism and ID are virtually non-communicative and unable to notify their caregivers when they are in pain. In a pilot study, we showed that heart rate (HR) monitoring may identify painful situations in this patient group, as HR increases in acutely painful situations.

OBJECTIVES

This study aims to generate knowledge to reduce the number of painful episodes in non-communicative patients' everyday lives. We will 1) assess the effectiveness of HR as a tool for identifying potentially painful care procedures, 2) test the effect of HR-informed changes in potentially painful care procedures on biomarkers of pain, and 3) assess how six weeks of communication through HR affects the quality of communication between patient and caregiver.

METHODS

We will recruit 38 non-communicative patients with autism and ID residing in care homes.

ASSESSMENTS

HR is measured continuously to identify acutely painful situations. HR variability and pain-related cytokines (MCP-1, IL-1RA, IL-8, TGFβ1, and IL-17) are collected as measures of long-term pain. Caregivers will be asked to what degree they observe pain in their patients and how well they believe they understand their patient's expressions of emotion and pain. Pre-intervention: HR is measured 8 h/day over 2 weeks to identify potentially painful situations across four settings: physiotherapy, cast use, lifting, and personal hygiene.

INTERVENTION

Changes in procedures for identified painful situations are in the form of changes in 1) physiotherapy techniques, 2) preparations for putting on casts, 3) lifting techniques or 4) personal hygiene procedures.

DESIGN

Nineteen patients will start intervention in week 3 while 19 patients will continue data collection for another 2 weeks before procedure changes are introduced. This is done to distinguish between specific effects of changes in procedures and non-specific effects, such as caregivers increased attention.

DISCUSSION

This study will advance the field of wearable physiological sensor use in patient care.

TRIAL REGISTRATION

Registered prospectively at ClinicalTrials.gov (NCT05738278).

Can peripheral biomarkers reflect the psychological condition in geriatric populations? The influence of cardiovascular comorbidities

BACKGROUND

Although previous studies indicated the association between peripheral biomarkers and psychological conditions, a higher prevalence of cardiovascular diseases (CVD) among geriatric populations may hinder the applicability of the biomarkers. The objective of this study was to assess the adequacy of the application of biomarkers to evaluate psychological conditions among geriatric populations.

METHOD

We collected information on the demographics and history of CVD in all participants. All participants completed the Brief Symptom Rating Scale (BSRS-5) and the Chinese Happiness Inventory (CHI), which are the measurement of negative and positive psychological conditions, respectively. Four indicators of the peripheral biomarkers, including the standard deviation of normal to normal RR intervals (SDNN), finger temperature, skin conductance, and electromyogram were collected for each participant during a 5-min resting state. Multiple linear regression models were conducted to evaluate the association between the biomarkers and the psychological measurements (BSRS-5, CHI) with and without the inclusion of the participants with CVD.

RESULTS

A total of 233 participants without CVD (non-CVD group) and 283 participants with CVD (CVD group) were included. The CVD group was older and with higher body mass index compared to the non-CVD group. In the multiple linear regression model with all participants, only BSRS-5 scores had a positive association with electromyogram. After the exclusion of the CVD group, the association between the BSRS-5 scores and electromyogram was more relevant, while CHI scores became positively associated with SDNN.

CONCLUSIONS

A single measurement of the peripheral biomarker may be insufficient to depict psychological conditions among geriatric populations.

Emotional Imagery Influences the Adaptive Force in Young Women: Unpleasant Imagery Reduces Instantaneously the Muscular Holding Capacity

The link between emotions and motor function has been known for decades but is still not clarified. The Adaptive Force (AF) describes the neuromuscular capability to adapt to increasing forces and was suggested to be especially vulnerable to interfering inputs. This study investigated the influence of pleasant and unpleasant food imagery on the manually assessed AF of elbow and hip flexors objectified by a handheld device in 12 healthy women. The maximal isometric AF was significantly reduced during unpleasant vs. pleasant imagery and baseline (p < 0.001, dz = 0.98−1.61). During unpleasant imagery, muscle lengthening started at 59.00 ± 22.50% of maximal AF, in contrast to baseline and pleasant imagery, during which the isometric position could be maintained mostly during the entire force increase up to ~97.90 ± 5.00% of maximal AF. Healthy participants showed an immediately impaired holding function triggered by unpleasant imagery, presumably related to negative emotions. Hence, AF seems to be suitable to test instantaneously the effect of emotions on motor function. Since musculoskeletal complaints can result from muscular instability, the findings provide insights into the understanding of the causal chain of linked musculoskeletal pain and mental stress. A case example (current stress vs. positive imagery) suggests that the approach presented in this study might have future implications for psychomotor diagnostics and therapeutics.

Stressors Length and the Habituation Effect-An EEG Study

The research described in this paper aimed to determine whether people respond differently to short and long stimuli and whether stress stimuli repeated over time evoke a habituation effect. To meet this goal, we performed a cognitive experiment with eight subjects. During this experiment, the subjects were presented with two trays of stress-inducing stimuli (different in length) interlaced with the main tasks. The mean beta power calculated from the EEG signal recorded from the two prefrontal electrodes (Fp1 and Fp2) was used as a stress index. The main results are as follows: (i) we confirmed the previous finding that beta power assessed from the EEG signal recorded from prefrontal electrodes is significantly higher for the STRESS condition compared to NON-STRESS condition; (ii) we found a significant difference in beta power between STRESS conditions that differed in length-the beta power was four times higher for short, compared to long, stress-inducing stimuli; (iii) we did not find enough evidence to confirm (or reject) the hypothesis that stress stimuli repeated over time evoke the habituation effect; although the general trends aggregated over subjects and stressors were negative, their slopes were not statistically significant; moreover, there was no agreement among subjects with respect to the slope of individual trends.

Exploration of Emotion Dynamics Sensing Using Trapezius EMG and Fingertip Temperature

Exploration of the physiological signals associated with subjective emotional dynamics has practical significance. Previous studies have reported that the dynamics of subjective emotional valence and arousal can be assessed using facial electromyography (EMG) and electrodermal activity (EDA), respectively. However, it remains unknown whether other methods can assess emotion dynamics. To investigate this, EMG of the trapezius muscle and fingertip temperature were tested. These measures, as well as facial EMG of the corrugator supercilii and zygomatic major muscles, EDA (skin conductance level) of the palm, and continuous ratings of subjective emotional valence and arousal, were recorded while participants (n = 30) viewed emotional film clips. Intra-individual subjective-physiological associations were assessed using correlation analysis and linear and polynomial regression models. Valence ratings were linearly associated with corrugator and zygomatic EMG; however, trapezius EMG was not related, linearly or curvilinearly. Arousal ratings were linearly associated with EDA and fingertip temperature but were not linearly or curvilinearly related with trapezius EMG. These data suggest that fingertip temperature can be used to assess the dynamics of subjective emotional arousal.

Stress Detection from Surface Electromyography using Convolutional Neural Networks

The study of stress and its implications has been the focus of interest in various fields of science. Many automated/semi-automated stress detection systems based on physiological markers have been gaining enormous popularity and importance in recent years. Such non-voluntary physiological features exhibit unique characteristics in terms of reliability, accuracy. Combined with machine learning techniques, they offer a great field of study of stress identification and modelling. In this study, we explore the use of Convolutional Neural Networks (CNN) for stress detection through surface electromyography signals (sEMG) of the trapezius muscle. One of the main advantages of this model is the use of the sEMG signal without computed features, contrary to classical machine learning algorithms. The proposed model achieved good results, with 73% f1-score for a multi-class classification and 82% in a bi-class classification.

The Stressful Experience of Goal Orientations Under Frustration: Evidence Using Physiological Means

The purpose of the present study was to test the hypothesis that goal orientation is associated with divergent forms of emotional reactivity under frustration. Goal orientations were assessed using bifurcations of performance goals described earlier. Physiological stress levels were measured via a blood volume pulse analysis after individuals were subjected to a computerized Stroop task using a malfunctioning mouse to induce enhanced frustration. The results indicated that performance-avoidance goals were associated with the highest levels of emotional reactivity, with normative outcome goals being significantly more detrimental than ability goals. We concluded that the motivation to avoid failure or to outperform others is the most detrimental determinant of stress and needs to be avoided by all means. Instead, it is suggested that educators emphasize performance using personal best outcomes or by valuing engagement, deep processing and task completion.

Mindfulness practice correlates with reduced exam-induced stress and improved exam performance in preclinical medical students with the "acting with awareness", "non-judging" and "non-reacting" facets of mindfulness particularly associated with improved exam performance

BACKGROUND

Medical students demonstrate higher levels of psychological distress compared with the general population and other student groups, especially at exam times. Mindfulness interventions show promise in stress reduction for this group, and in the reduction of cortisol, an established clinical marker of the body's stress response. This study investigated the relationship of mindfulness to exam-induced stress, salivary cortisol and exam performance in undergraduate medical students.

METHODS

A controlled pre-post analysis design with within-groups comparisons. 67 medical students completed the five facet mindfulness questionnaire (FFMQ) and provided saliva samples, from which cortisol was extracted, during group work (control/baseline) and immediately prior to end of year 2 examinations (experimental). Academic performance data was extracted for comparison with measures.

RESULTS

Exam-induced salivary cortisol concentration showed a significant negative relation with exam performance. Total FFMQ score showed a significant positive relation with exam performance and a significant negative relation with exam-induced salivary cortisol. The specific mindfulness facets of acting with awareness, non-judging and non-reacting also showed a positive correlation with exam performance.

CONCLUSIONS

This study suggests that there exists an important relationship between mindfulness and the physiological biomarker of stress, cortisol, and this manifests into improved assessment outcomes potentially through healthier, more adaptive coping and stress management strategies. In particular, this study identifies the acting with awareness, non-judging and non-reacting facets of mindfulness to be significantly associated with exam performance suggesting that these may be important facets for clinical educators to target when helping students with mindfulness practice.

Multi-Parameter Physiological State Monitoring in Target Detection Under Real-World Settings

Mental state changes induced by stimuli under experimental settings or by daily events in real life affect task performance and are entwined with physical and mental health. In this study, we developed a physiological state indicator with five parameters that reflect the subject's real-time physiological states based on online EEG signal processing. These five parameters are attention, fatigue, stress, and the brain activity shifts of the left and right hemispheres. We designed a target detection experiment modified by a cognitive attention network test for validating the effectiveness of the proposed indicator, as such conditions would better approximate a real chaotic environment. Results demonstrated that attention levels while performing the target detection task were significantly higher than during rest periods, but also exhibited a decay over time. In contrast, the fatigue level increased gradually and plateaued by the third rest period. Similar to attention levels, the stress level decreased as the experiment proceeded. These parameters are therefore shown to be highly correlated to different stages of the experiment, suggesting their usage as primary factors in passive brain-computer interfaces (BCI). In addition, the left and right brain activity indexes reveal the EEG neural modulations of the corresponding hemispheres, which set a feasible reference of activation for an active BCI control system, such as one executing motor imagery tasks. The proposed indicator is applicable to potential passive and active BCI applications for monitoring the subject's physiological state change in real-time, along with providing a means of evaluating the associated signal quality to enhance the BCI performance.

Towards a Resilience to Stress Index Based on Physiological Response: A Machine Learning Approach

This study proposes a new index to measure the resilience of an individual to stress, based on the changes of specific physiological variables. These variables include electromyography, which is the muscle response, blood volume pulse, breathing rate, peripheral temperature, and skin conductance. We measured the data with a biofeedback device from 71 individuals subjected to a 10-min psychophysiological stress test. The data exploration revealed that features' variability among test phases could be observed in a two-dimensional space with Principal Components Analysis (PCA). In this work, we demonstrate that the values of each feature within a phase are well organized in clusters. The new index we propose, Resilience to Stress Index (RSI), is based on this observation. To compute the index, we used non-supervised machine learning methods to calculate the inter-cluster distances, specifically using the following four methods: Euclidean Distance of PCA, Mahalanobis Distance, Cluster Validity Index Distance, and Euclidean Distance of Kernel PCA. While there was no statistically significant difference (p>0.01) among the methods, we recommend using Mahalanobis, since this method provides higher monotonic association with the Resilience in Mexicans (RESI-M) scale. Results are encouraging since we demonstrated that the computation of a reliable RSI is possible. To validate the new index, we undertook two tasks: a comparison of the RSI against the RESI-M, and a Spearman correlation between phases one and five to determine if the behavior is resilient or not. The computation of the RSI of an individual has a broader scope in mind, and it is to understand and to support mental health. The benefits of having a metric that measures resilience to stress are multiple; for instance, to the extent that individuals can track their resilience to stress, they can improve their everyday life.

The Influence of Neuroticism on the Muscle Response in the Trapezius and Frontalis Muscles to Anticipatory Stress

Abstract. Objective quantification of mental stress in the workplace would be beneficial for designing work tasks to avoid the negative consequences of mental stress. Methods such as surface electromyography have proven to be sensitive to mental demands. However, there is little knowledge about the muscle response and moderating factors during anticipatory stress paradigms. This study examined whether the personality dimension neuroticism moderates the muscle response to the expectation of an unpredictable electrical shock. Forty-seven subjects underwent three expectation phases, in which they could expect a pleasant audio signal (NoShock) or an electric shock in two conditions (anticipation of the first: Shock1, and second electric shock: Shock2) at an unpredictable moment. The frontalis muscle activity and the upper and upper/middle parts of the trapezius muscle were recorded using surface electromyography. Neuroticism was surveyed using the Big Five Inventory to assign the subjects to a group with lower or higher neuroticism. Shock1 only induced higher trapezius muscle activity in the higher neuroticism group, which vanished during Shock2, while the frontalis muscle showed no significant effects. The results suggest that neuroticism should be considered a moderating factor in assessing anticipatory stress using surface electromyography at the trapezius muscle.

Removal of physiological artifacts from simultaneous EEG and fMRI recordings

OBJECTIVE

Simultaneous recording of the electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) allows a combination of eletrophysiological and haemodynamic information to be used to form a more complete picture of cerebral dynamics. However, EEG recorded within the MRI scanner is contaminated by both imaging artifacts and physiological artifacts. The majority of the techniques used to pre-process such EEG focus on removal of the imaging and balistocardiogram artifacts, with some success, but don't remove all other physiological artifacts.

METHODS

We propose a new offline EEG artifact removal method based upon a combination of independent component analysis and fMRI-based head movement estimation to aid the removal of physiological artifacts from EEG recorded during EEG-fMRI recordings. Our method makes novel use of head movement trajectories estimated from the fMRI recording in order to assist with identifying physiological artifacts in the EEG and is designed to be used after removal of the fMRI imaging artifact from the EEG.

RESULTS

We evaluate our method on EEG recorded during a joint EEG-fMRI session from healthy adult participants. Our method significantly reduces the influence of all types of physiological artifacts on the EEG. We also compare our method with a state-of-the-art physiological artifact removal method and demonstrate superior performance removing physiological artifacts.

CONCLUSIONS

Our proposed method is able to remove significantly more physiological artifact components from the EEG, recorded during a joint EEG-fMRI session, than other state-of-the-art methods.

SIGNIFICANCE

Our proposed method represents a marked improvement over current processing pipelines for removing physiological noise from EEG recorded during a joint EEG-fMRI session.

Classification of Mental Stress Using CNN-LSTM Algorithms with Electrocardiogram Signals

The mental stress faced by many people in modern society is a factor that causes various chronic diseases, such as depression, cancer, and cardiovascular disease, according to stress accumulation. Therefore, it is very important to regularly manage and monitor a person's stress. In this study, we propose an ensemble algorithm that can accurately determine mental stress states using a modified convolutional neural network (CNN)- long short-term memory (LSTM) architecture. When a person is exposed to stress, a displacement occurs in the electrocardiogram (ECG) signal. It is possible to classify stress signals by analyzing ECG signals and extracting specific parameters. To maximize the performance of the proposed stress classification algorithm, fast Fourier transform (FFT) and spectrograms were applied to preprocess ECG signals and produce signals in both the time and frequency domains to aid the training process. As the performance evaluation benchmarks of the stress classification model, confusion matrices, receiver operating characteristic (ROC) curves, and precision-recall (PR) curves were used, and the accuracy achieved by the proposed model was 98.3%, which is an improvement of 14.7% compared to previous research results. Therefore, our model can help manage the mental health of people exposed to stress. In addition, if combined with various biosignals such as electromyogram (EMG) and photoplethysmography (PPG), it may have the potential for development in various healthcare systems, such as home training, sleep state analysis, and cardiovascular monitoring.

Electronic Devices for Stress Detection in Academic Contexts during Confinement Because of the COVID-19 Pandemic

This article studies the development and implementation of different electronic devices for measuring signals during stress situations, specifically in academic contexts in a student group of the Engineering Department at the University of Pamplona (Colombia). For the research’s development, devices for measuring physiological signals were used through a Galvanic Skin Response (GSR), the electrical response of the heart by using an electrocardiogram (ECG), the electrical activity produced by the upper trapezius muscle (EMG), and the development of an electronic nose system (E-nose) as a pilot study for the detection and identification of the Volatile Organic Compounds profiles emitted by the skin. The data gathering was taken during an online test (during the COVID-19 Pandemic), in which the aim was to measure the student’s stress state and then during the relaxation state after the exam period. Two algorithms were used for the data process, such as Linear Discriminant Analysis and Support Vector Machine through the Python software for the classification and differentiation of the assessment, achieving 100% of classification through GSR, 90% with the E-nose system proposed, 90% with the EMG system, and 88% success by using ECG, respectively.

Cardiac and Proprioceptive Accuracy Are Not Related to Body Awareness, Perceived Body Competence, and Affect

Interoception in the broader sense refers to the perception of internal states, including the perception of the actual state of the internal organs (visceroception) and the motor system (proprioception). Dimensions of interoception include (1) interoceptive accuracy, i.e., the ability to sense internal changes assessed with behavioral tests, (2) confidence rating with respect to perceived performance in an actual behavioral test, and (3) interoceptive sensibility, i.e., the self-reported generalized ability to perceive body changes. The relationship between dimension of cardioceptive and proprioceptive modalities and their association with affect are scarcely studied. In the present study, undergraduate students (N = 105, 53 males, age: 21.0 ± 1.87 years) filled out questionnaires assessing positive and negative affect (Positive and Negative Affect Schedule), interoceptive sensibility (Body Awareness Questionnaire), and body competence (Body Competence Scale of the Body Consciousness Questionnaire). Following this, they completed a behavioral task assessing cardioceptive accuracy (the mental heartbeat tracking task by Schandry) and two tasks assessing proprioceptive accuracy with respect to the tension of arm flexor muscles (weight discrimination task) and the angular position of the elbow joint (joint position reproduction task). Confidence ratings were measured with visual analog scales after the tasks. With the exception of a weak association between cardioceptive accuracy and the respective confidence rating, no associations between and within modalities were found with respect to various dimensions of interoception. Further, the interoceptive dimensions were not associated with state and trait positive and negative affect and perceived body competence. In summary, interoceptive accuracy scores do not substantially contribute to conscious representations of cardioceptive and proprioceptive ability. Within our data, non-pathological affective states (PANAS) are not associated with the major dimensions of interoception for the cardiac and proprioceptive modalities.

Effect of Virtual Reality on Stress Reduction and Change of Physiological Parameters Including Heart Rate Variability in People With High Stress: An Open Randomized Crossover Trial

Introduction: Although, attempts to apply virtual reality (VR) in mental healthcare are rapidly increasing, it is still unclear whether VR relaxation can reduce stress more than conventional biofeedback. Methods: Participants consisted of 83 healthy adult volunteers with high stress, which was defined as a score of 20 or more on the Perceived Stress Scale-10 (PSS-10). This study used an open, randomized, crossover design with baseline, stress, and relaxation phases. During the stress phase, participants experienced an intentionally generated shaking VR and serial-7 subtraction. For the relaxation phase, participants underwent a randomly assigned relaxation session on day 1 among VR relaxation and biofeedack, and the other type of relaxation session was applied on day 2. We compared the State-Trait Anxiety Inventory-X1 (STAI-X1), STAI-X2, the Numeric Rating Scale (NRS), and physiological parameters including heart rate variability (HRV) indexes in the stress and relaxation phases. Results: A total of 74 participants were included in the analyses. The median age of participants was 39 years, STAI-X1 was 47.27 (SD = 9.92), and NRS was 55.51 (SD = 24.48) at baseline. VR and biofeedback significantly decreased STAI-X1 and NRS from the stress phase to the relaxation phase, while the difference of effect between VR and biofeedback was not significant. However, there was a significant difference in electromyography, LF/HF ratio, LF total, and NN50 between VR relaxation and biofeedback. Conclusion: VR relaxation was effective in reducing subjectively reported stress in individuals with high stress.

Neurophysiological relationship of neuromuscular fatigue and stress disorder in PTSD patients

INTRODUCTION

Apart from mental disorders, other complications that have been reported in some patients with Post-Traumatic Stress Disorder (PTSD), include physical pain and being quick to fatigue, which can severely affect the patients' daily life. Therefore, this study aims to evaluate the relationship between PTSD and physical fatigue in people with PTSD.

METHOD

18 military men with (n = 9) and without PTSD (n = 9), with an age range of 45-60 years, volunteered to participate. They were randomly assigned into two groups: PTSD and non-PTSD groups. Recording of the surface electromyography (EMG) in a specific muscle was conducted twice in both groups, once at baseline and then again after a single session of fatiguing exercise. Data were analyzed by ANOVA with repeated measure (2✕2) at the significance level of 0.05.

RESULTS

Results showed that there was a significant main effect of intervention on electrical activity and neural conduction variables in the PTSD group (p = 0.04, p = 0.02). There was also an effect of time for the both variables (P < 0.001).

CONCLUSION

Stress disorders may affect the time to fatigue in PTSD patients and subsequently cause some difficulties in their daily life.

Design of a Wearable System to Capture Physiological Data to Monitor Surgeons' Stress During Surgery

The mental and physiological stress experienced by surgeons during operations has been identified as an important human factor that impacts surgical performance and patient safety. It is crucial to objectively measure and quantify surgeons' stress via physiological signals in order to enhance the understanding of how stress contributes to surgical outcomes. Current clinical and consumer devices for monitoring bio signals are not well adapted for use in the operating room; therefore, we designed an unobtrusive system, that measures select signals that correlate with stress and stores the data for integration into a data processing pipeline. Herein, we present a proof-of-concept device that captures data from ECG, EMG, EDA, and IMU sensors and initial testing results.

Proposing a convolutional neural network for stress assessment by means of derived heart rate from functional near infrared spectroscopy

BACKGROUND

Stress is known as one of the major factors threatening human health. A large number of studies have been performed in order to either assess or relieve stress by analyzing the brain and heart-related signals.

METHOD

In this study, a method based on the Convolutional Neural Network (CNN) approach is proposed to assess stress induced by the Montreal Imaging Stress Task. The proposed model is trained on the heart rate signal derived from functional Near-Infrared Spectroscopy (fNIRS), which is referred to as HRF. In this regard, fNIRS signals of 20 healthy volunteers were recorded using a configuration of 23 channels located on the prefrontal cortex. The proposed deep learning system consists of two main parts where in the first part, the one-dimensional convolutional neural network is employed to build informative activation maps, and then in the second part, a stack of deep fully connected layers is used to predict the stress existence probability. Thereafter, the employed CNN method is compared with the Dense Neural Network, Support Vector Machine, and Random Forest regarding various classification metrics.

RESULTS

Results clearly showed the superiority of CNN over all other methods. Additionally, the trained HRF model significantly outperforms the model trained on the filtered fNIRS signals, where the HRF model could achieve 98.69 ± 0.45% accuracy, which is 10.09% greater than the accuracy obtained by the fNIRS model.

CONCLUSIONS

Employment of the proposed deep learning system trained on the HRF measurements leads to higher stress classification accuracy than the accuracy reported in the existing studies where the same experimental procedure has been done. Besides, the proposed method suggests better stability with lower variation in prediction. Furthermore, its low computational cost opens up the possibility to be applied in real-time monitoring of stress assessment.

The impact of adverse childhood experiences on EMG reactivity: A proof of concept study

Adverse childhood experiences (ACE), such as emotional or physical abuse, can produce a lasting effect on the individual. The aim of this study was to investigate how ACE may impact electromyography (EMG) activity of the trapezius muscle in a novel experimental stress paradigm, in a sample of 120 healthy participants. The stress paradigm consisted of a memory task, in which participants were asked to memorize and recall as many words as possible, displayed on a screen. The study protocol included 2 identical experimental sessions (T0 = 0 and T1 = 6 months). EMG activity was analyzed using multilevel regression analysis. EMG activity was higher during the memory task compared to baseline, supporting the validity of the experimental EMG-stress paradigm. In addition, the EMG increase was attenuated during the second session. Analyses were indicative for a moderating effect of ACE on stress-induced EMG activity: higher ACE scores resulted in greater EMG reactivity. These associations were apparent for early ACE exposure (0-11 years) as well as for later exposure (12-17 years). The association between ACE and EMG reactivity remained significant but was much weaker at T1 in comparison to T0, likely because of reduced unpredictability and uncertainty related to the experiment. In conclusion, this study showed that enduring liabilities occasioned by ACE in a non-clinical population can be studied using an experimental paradigm of EMG stress reactivity, contingent on the level of predictability of the stressor.

Data Engineering for Affective Understanding Systems

Affective understanding is an area of affective computing which is concerned with advancing the ability of a computer to understand the affective state of its user. This area continues to receive attention in order to improve the human-computer interactions of automated systems and services. Systems within this area typically deal with big data from different sources, which require the attention of data engineers to collect, process, integrate and store. Although many studies are reported in this area, few look at the issues that should be considered when designing the data pipeline for a new system or study. By reviewing the literature of affective understanding systems one can deduct important issues to consider during this design process. This paper presents a design model that works as a guideline to assist data engineers when designing data pipelines for affective understanding systems, in order to avoid implementation faults that may increase cost and time. We illustrate the feasibility of this model by presenting its utilization to develop a stress detection application for drivers as a case study. This case study shows that failure to consider issues in the model causes major errors during implementation leading to highly expensive solutions and the wasting of resources. Some of these issues are emergent such as performance, thus implementing prototypes is recommended before finalizing the data pipeline design.

A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving

As driving functions become increasingly automated, motorists run the risk of becoming cognitively removed from the driving process. Psychophysiological measures may provide added value not captured through behavioral or self-report measures alone. This paper provides a selective review of the psychophysiological measures that can be utilized to assess cognitive states in real-world driving environments. First, the importance of psychophysiological measures within the context of traffic safety is discussed. Next, the most commonly used physiology-based indices of cognitive states are considered as potential candidates relevant for driving research. These include: electroencephalography and event-related potentials, optical imaging, heart rate and heart rate variability, blood pressure, skin conductance, electromyography, thermal imaging, and pupillometry. For each of these measures, an overview is provided, followed by a discussion of the methods for measuring it in a driving context. Drawing from recent empirical driving and psychophysiology research, the relative strengths and limitations of each measure are discussed to highlight each measures' unique value. Challenges and recommendations for valid and reliable quantification from lab to (less predictable) real-world driving settings are considered. Finally, we discuss measures that may be better candidates for a near real-time assessment of motorists' cognitive states that can be utilized in applied settings outside the lab. This review synthesizes the literature on in-vehicle psychophysiological measures to advance the development of effective human-machine driving interfaces and driver support systems.

A Review of Wearable Solutions for Physiological and Emotional Monitoring for Use by People with Autism Spectrum Disorder and Their Caregivers

The goal of real-time feedback on physiological changes, stress monitoring and even emotion detection is becoming a technological reality. People in their daily life experience varying emotional states, some of which are negative and which can lead to decreased attention, decreased productivity and ultimately, reduced quality of life. Therefore, having a solution that continuously monitors the physiological signals of the person and assesses his or her emotional well-being could be a very valuable tool. This paper aims to review existing physiological and motional monitoring devices, highlight their features and compare their sensing capabilities. Such technology would be particularly useful for certain populations who experience rapidly changing emotional states such as people with autism spectrum disorder and people with intellectual disabilities. Wearable sensing devices present a potential solution that can support and complement existing behavioral interventions. This paper presents a review of existing and emerging products in the market. It reviews the literature on state-of-the-art prototypes and analyzes their usefulness, clinical validity, and discusses clinical perspectives. A small number of products offer reliable physiological internal state monitoring and may be suitable for people with Autism Spectrum Disorder (ASD). It is likely that more promising solutions will be available in the near future. Therefore, caregivers should be careful in their selection of devices that meet the care-receiver's personal needs and have strong research support for reliability and validity.

Can Tai Chi and Qigong Postures Shape Our Mood? Toward an Embodied Cognition Framework for Mind-Body Research

Dynamic and static body postures are a defining characteristic of mind-body practices such as Tai Chi and Qigong (TCQ). A growing body of evidence supports the hypothesis that TCQ may be beneficial for psychological health, including management and prevention of depression and anxiety. Although a variety of causal factors have been identified as potential mediators of such health benefits, physical posture, despite its visible prominence, has been largely overlooked. We hypothesize that body posture while standing and/or moving may be a key therapeutic element mediating the influence of TCQ on psychological health. In the present paper, we summarize existing experimental and observational evidence that suggests a bi-directional relationship between body posture and mental states. Drawing from embodied cognitive science, we provide a theoretical framework for further investigation into this interrelationship. We discuss the challenges involved in such an investigation and propose suggestions for future studies. Despite theoretical and practical challenges, we propose that the role of posture in mind-body exercises such as TCQ should be considered in future research.

Novel dry electrodes for recording electrodermal activity

Novel carbon/salt adhesive (CSA) electrodes have been found suitable for collecting electrodermal activity (EDA) signals. Ag/AgCl electrodes are considered the standard for collecting EDA signals, because it highly avoids electrodes' polarization. Ag is an expensive commodity. Furthermore, a hydrogel layer is needed for the Ag/AgCl electrodes to collect EDA signals. Adding hydrogel to the electrodes is a cumbersome process. Aforementioned circumstances highlight the need for a more accessible media to collect these signals, allowing EDA use to spread. Dry electrodes made with a mixture of carbon, salt and adhesive has shown to be suitable for collecting bioelectric signals. We have implemented a constant DC-source EDA circuit, with the intention of testing how these electrodes perform for collecting EDA signals. Recruited subjects (N=4) underwent a test including electric shocks, watching a disturbing video and performing the Stroop task. Time and frequency domain correlation were computed. For the obtained skin conductance responses (SCRs), amplitude, onset-to-peak time, and onset difference between the CSA and Ag/AgCl electrodes' acquired SCRs were computed. We found no significant differences on SCRs amplitude and onset-to-peak time between CSA and Ag/AgCl. Furthermore, the difference in onset time for simultaneous SCRs obtained using both media was not different to zero. We conclude that CSA electrodes are a suitable surrogate of Ag/AgCl electrodes for collecting EDA signals on healthy subjects using the implemented DC circuit.

Impact of early life adversity on EMG stress reactivity of the trapezius muscle

Human and animal research indicates that exposure to early life adversity increases stress sensitivity later in life. While behavioral markers of adversity-induced stress sensitivity have been suggested, physiological markers remain to be elucidated. It is known that trapezius muscle activity increases during stressful situations. The present study examined to what degree early life adverse events experienced during early childhood (0-11 years) and adolescence (12-17 years) moderate experimentally induced electromyographic (EMG) stress activity of the trapezius muscles, in an experimental setting. In a general population sample (n = 115), an anticipatory stress effect was generated by presenting a single unpredictable and uncontrollable electrical painful stimulus at t = 3 minutes. Subjects were unaware of the precise moment of stimulus delivery and its intensity level. Linear and nonlinear time courses in EMG activity were modeled using multilevel analysis. The study protocol included 2 experimental sessions (t = 0 and t = 6 months) allowing for examination of reliability.Results show that EMG stress reactivity during the stress paradigm was consistently stronger in people with higher levels of early life adverse events; early childhood adversity had a stronger moderating effect than adolescent adversity. The impact of early life adversity on EMG stress reactivity may represent a reliable facet that can be used in both clinical and nonclinical studies.

The Influence of Perceived Stress on Cortical Reactivity: A Proof-Of-Principle Study

The aim of this study was to investigate how perceived stress may affect electroencephalographical (EEG) activity in a stress paradigm in a sample of 76 healthy participants. EEG activity was analyzed using multilevel modeling, allowing estimation of nested effects (EEG time segments within subjects). The stress paradigm consisted of a 3-minute pre-stimulus stress period and a 2-minute post-stimulus phase. At t=3 minutes, a single electrical stimulus was delivered. Participants were unaware of the precise moment of stimulus delivery and its intensity level. In the EEG time course of alpha activity, a stronger increase was observed during the post-stimulus period as compared to the pre-stimulus period. An opposite time course effect was apparent for gamma activity. Both effects were in line with a priori expectations and support the validity of this experimental EEG-stress paradigm. Secondly, we investigated whether interaction effects of stress and coping, as measured with the Perceived Stress Scale-10 questionnaire (PSS-10), could be demonstrated. A higher perceived stress score was accompanied by a greater increase in delta- and theta-activity during the post-stimulus phase, compared to low scores. In contrast, low coping capacity was associated with a stronger decrease in slow beta, fast beta and gamma activity during the post-stimulus phase. The results of the present article may be interpreted as proof-of-principle that EEG stress-related activity depends on the level of subjectively reported perceived stress. The inclusion of psychosocial variables measuring coping styles as well as stress-related personality aspects permits further examination of the interconnection between mind and body and may inform on the process of transformation from acute to chronic stress.