A wearable sensor for unobtrusive, long-term assessment of electrodermal activity

Exploring physiological responses through electrodermal activity (EDA) for evaluating the impact of universal design features in a hotel environment

Purpose: The aim of this study was to explore the feasibility of using electrodermal activity (EDA) to detect changes in physiological arousal linked to perceptions of accommodations, focusing on universal design (UD) features. In environments like hotels, designers must consider wellness, social integration, and cultural appropriateness to effectively implement UD. Challenges exist with implementing and evaluating UD to accommodate diverse user needs due to conflicting definitions and application issues. To meet the need for post-design evaluation discerning accommodations by features and user groups, EDA measures offer a way to capture individual reactions to external stimuli.

Materials and Methods: In this study, 22 adults (14 young, 8 older) completed an independent hotel walkthrough while expressing their perceptions. EDA was measured using a wristband, and participants' perceived stress and usability were assessed through questionnaires. Phasic EDA was extracted to represent discrete event-evoked changes in arousal.

Results: Findings demonstrated the potential of EDA to identify physiological response variations based on age and location within the hotel. Older adults displayed significantly higher levels of arousal and more favorable usability ratings (4.61 out of 5) compared to young adults, with peak arousal in the corridor and public restroom. Younger adults showed the highest arousal in the bathroom, often with negative associations. The groups differed in their reactions to the bathroom and reception areas.

Conclusions: Divergences between physiological responses and subjective outcomes highlighted the complexity of translating arousal measures into meaningful insights. EDA, combined with commentary, enhanced our understanding of user reactions to design elements to fill gaps left by subjective methods.

Effective engagement in computerized cognitive training for older adults

Computerized cognitive training (CCT) is a frontline therapy to prevent or slow age-related cognitive decline. A prerequisite for CCT research to provide clinically relevant improvements in cognition is to understand effective engagement, i.e., the pattern of energy investment that ensures CCT effectiveness. Even though previous studies have assessed whether particular variables (e.g., gamification) predict engagement and/or CCT effectiveness, the field lacks a systematic approach to understanding effective engagement. Here, by comprehensively reviewing and evaluating engagement and adjacent literature, we propose a standardized measurement and operational framework to promote effective engagement with CCT targeting cognitive decline in older adults. We suggest that promoting effective engagement with CCT has two key steps: 1) comprehensively measuring engagement with CCT and 2) identifying which aspects of engagement are essential to achieve the pre-specified outcome of clinically relevant improvements in cognition. The proposed measurement and operational framework of effective engagement will allow future research to maximize older adults' engagement with CCT to slow/prevent age-related cognitive decline.

Investigation of Possible Sources of Electrodermal Activity in Surgical Personnel to Assess Workplace Stress Levels

OBJECTIVE

The aim of this study was to evaluate alternative measurement points to assess stress levels during surgery, given that taking electrodermal activity (EDA) measurements from the palms, a commonly used standard, is impractical in surgical contexts.

METHODS

A combination of mentally challenging tasks on a laparoscopy trainer (LapSim) and physically challenging sports exercises were performed by a group of participants. During these tasks, EDA was measured at three different locations: the fingers, toes, and shoulder/neck area. Additionally, an electrocardiogram was used as an objective measure of stress levels.

RESULTS

The findings indicated that EDA measurements taken at the toes produced similar high skin conductance levels to measurements taken at the palms. However, significantly lower skin conductance levels were observed at the shoulder. Despite this, the cross-correlation of EDA data revealed high correlation coefficients (above 0.96) between both the toe and shoulder measurements when compared to the palm data.

CONCLUSIONS

The study concludes that both the toes and the shoulder/neck area serve as viable alternative locations for assessing occupational stress levels in surgical personnel. This assertion is supported by the similarity of the data produced by these methods to that of the standard finger-based technique, with the latter still being identified as the most sensitive method for capturing EDA. Subsequently, these findings attest to the potential for practical adaptation of this technique in surgical contexts.

Automatic motion artifact detection in electrodermal activity signals using 1D U-net architecture

We developed a method for automated detection of motion and noise artifacts (MNA) in electrodermal activity (EDA) signals, based on a one-dimensional U-Net architecture. EDA has been widely employed in diverse applications to assess sympathetic functions. However, EDA signals can be easily corrupted by MNA, which frequently occur in wearable systems, particularly those used for ambulatory recording. MNA can lead to false decisions, resulting in inaccurate assessment and diagnosis. Several approaches have been proposed for MNA detection; however, questions remain regarding the generalizability and the feasibility of implementation of the algorithms in real-time especially those involving deep learning approaches. In this work, we propose a deep learning approach based on a one-dimensional U-Net architecture using spectrograms of EDA for MNA detection. We developed our method using four distinct datasets, including two independent testing datasets, with a total of 9602 128-s EDA segments from 104 subjects. Our proposed scheme, including data augmentation, spectrogram computation, and 1D U-Net, yielded balanced accuracies of 80.0 ± 13.7 % and 75.0 ± 14.0 % for the two independent test datasets; these results are better than or comparable to those of other five state-of-the-art methods. Additionally, the computation time of our feature computation and machine learning classification was significantly lower than that of other methods (p < .001). The model requires only 0.28 MB of memory, which is far smaller than the two deep learning approaches (4.93 and 54.59 MB) which were used as comparisons to our study. Our model can be implemented in real-time in embedded systems, even with limited memory and an inefficient microprocessor, without compromising the accuracy of MNA detection.

Wearable Sensors, Data Processing, and Artificial Intelligence in Pregnancy Monitoring: A Review

Pregnancy monitoring is always essential for pregnant women and fetuses. According to the report of WHO (World Health Organization), there were an estimated 287,000 maternal deaths worldwide in 2020. Regular hospital check-ups, although well established, are a burden for pregnant women because of frequent travelling or hospitalization. Therefore, home-based, long-term, non-invasive health monitoring is one of the hot research areas. In recent years, with the development of wearable sensors and related data-processing technologies, pregnancy monitoring has become increasingly convenient. This article presents a review on recent research in wearable sensors, physiological data processing, and artificial intelligence (AI) for pregnancy monitoring. The wearable sensors mainly focus on physiological signals such as electrocardiogram (ECG), uterine contraction (UC), fetal movement (FM), and multimodal pregnancy-monitoring systems. The data processing involves data transmission, pre-processing, and application of threshold-based and AI-based algorithms. AI proves to be a powerful tool in early detection, smart diagnosis, and lifelong well-being in pregnancy monitoring. In this review, some improvements are proposed for future health monitoring of pregnant women. The rollout of smart wearables and the introduction of AI have shown remarkable potential in pregnancy monitoring despite some challenges in accuracy, data privacy, and user compliance.

Sympathetic arousal among depressed college students: Examining the interplay between psychopathology and social activity

Depressed individuals exhibit altered sensitivity to both positive and negative social contact, and may not reap the same psychological and emotional benefits to socializing as non-depressed individuals. Although depressive symptoms and loneliness predict social withdrawal and decreased pleasure, little is currently understood about immediate affective arousal dynamics during real-time socializing. Using a novel ambulatory protocol that tracked both objective features of affective arousal (electrodermal activity) and subjective valence (self-reported) during college students' social interactions, we evaluated the moderating role of depression and loneliness symptoms on the associations between socializing with others (specifically, with a romantic partner, a close friend, or a group of friends) and the arousal and valence dimensions of affect. Among a racially and ethnically diverse sample of 118 college students (64% African American/Black/Continental African, 20% Latinx, 8% Asian, and 8% White) recruited from a large, predominantly White Midwestern university, those lower in depression and loneliness symptomatology evinced decreased average arousal (Β = -0.10, SE = 0.04, p < .01) when in relaxed and intimate socializing contexts (e.g., with a romantic partner and a close friend), consistent with the idea that these contexts facilitate important opportunities for psychological rest and recovery. Those lower in depression and loneliness symptoms also showed higher average arousal when socializing in the energizing context of being with a group of friends. Overall, the results suggest psychopathology is reflected in patterns of sympathetic arousal when socializing, with more depressed and lonely individuals generally feeling worse while receiving fewer psychophysiological rewards in multiple socializing contexts.

Recent Progress in Biosensors for Depression Monitoring-Advancing Personalized Treatment

Depression is currently a major contributor to unnatural deaths and the healthcare burden globally, and a patient's battle with depression is often a long one. Because the causes, symptoms, and effects of medications are complex and highly individualized, early identification and personalized treatment of depression are key to improving treatment outcomes. The development of wearable electronics, machine learning, and other technologies in recent years has provided more possibilities for the realization of this goal. Conducting regular monitoring through biosensing technology allows for a more comprehensive and objective analysis than previous self-evaluations. This includes identifying depressive episodes, distinguishing somatization symptoms, analyzing etiology, and evaluating the effectiveness of treatment programs. This review summarizes recent research on biosensing technologies for depression. Special attention is given to technologies that can be portable or wearable, with the potential to enable patient use outside of the hospital, for long periods.

Feasibility of using electrodermal activity responses to assess level of crowdedness of pedestrian paths

As urban populations grow, it's imperative to evaluate and enhance the quality of pedestrian paths from the user's perspective. Crowdedness, associated with discomfort and safety, is crucial in determining the overall walking quality and user experience. Previously utilized methods for measuring crowdedness, such as travel diaries and floating population surveys, were limited to collecting perceptual data from sporadic surveys with restricted spatial coverage. Similarly, methods based on CCTV or mobile service data have been used but present issues with blind spots and fail to consider pedestrian perspectives. Against this background, this study explores the feasibility of assessing crowdedness levels by measuring subjects' physiological responses in a laboratory setting based on visual images of real and virtual environments. This study hypothesizes that the amount of people or vehicles passing by affects the electrodermal activity (EDA) of pedestrians, indicating the comfort level of using the environment. Experimental EDA data were measured using a wearable device while the subjects were watching videos showing different pedestrian traffic flows. Representative EDA signal features (e.g., skin conductance responses) were extracted after data pre-processing. Noticeable changes in EDA responses are observed when subjects countered specific environmental variations, such as differing volumes of passing people, on pedestrian paths. The findings suggest that EDA data can be instrumental in differentiating crowdedness levels on pedestrian paths. This study contributes to the body of knowledge by demonstrating the potential of EDA data to characterize the crowdedness experienced by pedestrians. This aids in the development of a novel, quantitative method to gauge pedestrian path crowdedness and to discern contributing factors, such as path width.

Stress Classification and Vital Signs Forecasting for IoT-Health Monitoring

Health monitoring embedded with intelligence is the demand of the day. In this era of a large population with the emergence of a variety of diseases, the demand for healthcare facilities is high. Yet there is scarcity of medical experts, technicians for providing healthcare to the people affected with some medical problem. This article presents an Internet of Things (IoT) system architecture for health monitoring and how data analytics can be applied in the health sector. IoT is employed to integrate the sensor information, data analytics, machine intelligence and user interface to continuously track and monitor the health condition of the patient. Considering data analytics as the major part, we focused on the implementation of stress classification and forecasted the future values from the recorded data using sensors. Physiological vitals like Pulse, oxygen level percentage (SpO2), temperature, arterial blood pressure along with the patients age, height, weight and movement are considered. Various traditional and ensemble machine learning methods are applied to stress classification data. The experimental results have shown that a hypertuned random forest algorithm has given a better performance with an accuracy of 94.3%. In a view that knowing the future values in prior helps in quick decision making, critical vitals like pulse, oxygen level percentage and blood pressure have been forecasted. The data is trained with ML and neural network models. GRU model has given better performance with lower error rates of 1.76, 0.27, 5.62 RMSE values and 0.845, 0.13, 2.01 MAE values for pulse, SpO2 and blood pressure respectively.

Non-invasive detection of mental fatigue in construction equipment operators through geometric measurements of facial features

INTRODUCTION

Prolonged operation of construction equipment could lead to mental fatigue, which can increase the chances of human error-related accidents as well as operators' ill-health. The objective detection of operators' mental fatigue is crucial for reducing accident risk and ensuring operator health. Electroencephalography, photoplethysmography, electrodermal activity, and eye-tracking technology have been used to mitigate this issue. These technologies are invasive and wearable sensors that can cause irritation and discomfort. Geometric measurements of facial features can serve as a noninvasive alternative approach. Its application in detecting mental fatigue of construction equipment operators has not been reported in the literature. Although the application of facial features has been widespread in other domains, such as drivers and other occupation scenarios, their ecological validity for construction excavator operators remains a knowledge gap.

METHOD

This study proposed employing geometric measurements of facial features to detect mental fatigue in construction equipment operators' facial features. In this study, seventeen operators performed excavation operations. Mental fatigue was labeled subjectively and objectively using NASA-TLX scores and EDA values. Based on geometric measurements, facial features (eyebrow, mouth outer, mouth corners, head motion, eye area, and face area) were extracted.

RESULTS

The results showed that there was significant difference in the measured metrics for high fatigue compared to low fatigue. Specifically, the most noteworthy variation was for the eye and face area metrics, with mean differences of 45.88% and 26.9%, respectively.

CONCLUSIONS

The findings showed that geometrical measurements of facial features are a useful, noninvasive approach for detecting the mental fatigue of construction equipment operators.

Urban environment influences on stress, autonomic reactivity and circadian rhythm: protocol for an ambulatory study of mental health and sleep

Introduction

Converging evidence suggests that urban living is associated with an increased likelihood of developing mental health and sleep problems. Although these aspects have been investigated in separate streams of research, stress, autonomic reactivity and circadian misalignment can be hypothesized to play a prominent role in the causal pathways underlining the complex relationship between the urban environment and these two health dimensions. This study aims at quantifying the momentary impact of environmental stressors on increased autonomic reactivity and circadian rhythm, and thereby on mood and anxiety symptoms and sleep quality in the context of everyday urban living.

Method

The present article reports the protocol for a feasibility study that aims at assessing the daily environmental and mobility exposures of 40 participants from the urban area of Jerusalem over 7 days. Every participant will carry a set of wearable sensors while being tracked through space and time with GPS receivers. Skin conductance and heart rate variability will be tracked to monitor participants' stress responses and autonomic reactivity, whereas electroencephalographic signal will be used for sleep quality tracking. Light exposure, actigraphy and skin temperature will be used for ambulatory circadian monitoring. Geographically explicit ecological momentary assessment (GEMA) will be used to assess participants' perception of the environment, mood and anxiety symptoms, sleep quality and vitality. For each outcome variable (sleep quality and mental health), hierarchical mixed models including random effects at the individual level will be used. In a separate analysis, to control for potential unobserved individual-level confounders, a fixed effect at the individual level will be specified for case-crossover analyses (comparing each participant to oneself).

Conclusion

Recent developments in wearable sensing methods, as employed in our study or with even more advanced methods reviewed in the Discussion, make it possible to gather information on the functioning of neuro-endocrine and circadian systems in a real-world context as a way to investigate the complex interactions between environmental exposures, behavior and health. Our work aims to provide evidence on the health effects of urban stressors and circadian disruptors to inspire potential interventions, municipal policies and urban planning schemes aimed at addressing those factors.

Detection of dynamic changes of electrodermal activity to predict the classroom performance of college students

It is emphasized in the Self-regulated learning (SRL) framework that self-monitoring of learning state is vital for students to keep effective in studying. However, it's still challenging to get an accurate and timely understanding of their learning states during classes. In this study, we propose to use electrodermal activity (EDA) signals which are deemed to be associated with physiological arousal state to predict the college student's classroom performance. Twenty college students were recruited to attend eight lectures in the classroom, during which their EDA signals were recorded simultaneously. For each lecture, the students should complete pre- and after-class tests, and a self-reported scale (SRS) on their learning experience. EDA indices were extracted from both time and frequency domains, and they were furtherly mapped to the student's learning efficiency. As a result, the indices relevant to the dynamic changes of EDA had significant positive correlations with the learning efficiency. Furthermore, compared with only using SRS, a combination with EDA indices had significantly higher accuracy in predicting the learning efficiency. In conclusion, our findings demonstrate that the EDA dynamics are sensitive to the changes in learning efficiency, suggesting a promising approach to predicting the classroom performance of college students.

Student arousal, engagement, and emotion relative to Physical Education periods in school

BACKGROUND

Exercise has transient effects on cognition and mood, however the impact of Physical Education (PE) on cognitive and affective processes across the school day has not been examined.

METHOD

This study used wearables and questionnaires to track student arousal, engagement, and emotion across school days/periods following PE. Skin conductance, heart rate, heart rate variability, and self-reported engagement, arousal, and valence were analyzed for 23 students (age 15-17 years) on days with and without PE.

RESULTS

Sympathetic arousal was significantly higher for two hours following PE and there were stronger decreases in arousal across other classes relative to days without PE. On days with PE, engagement decreased, whereas valence increased from morning to afternoon.

CONCLUSION

These findings highlight the importance of considering acute effects of PE on learning across the entire school day, and demonstrates the feasibility of wearables to clarify how the timing of PE could positively or negatively affect self-regulation and learning.

Artificial intelligence-enhanced epileptic seizure detection by wearables

OBJECTIVE

Wrist- or ankle-worn devices are less intrusive than the widely used electroencephalographic (EEG) systems for monitoring epileptic seizures. Using custom-developed deep-learning seizure detection models, we demonstrate the detection of a broad range of seizure types by wearable signals.

METHODS

Patients admitted to the epilepsy monitoring unit were enrolled and asked to wear wearable sensors on either wrists or ankles. We collected patients' electrodermal activity, accelerometry (ACC), and photoplethysmography, from which blood volume pulse (BVP) is derived. Board-certified epileptologists determined seizure onset, offset, and types using video and EEG recordings per the International League Against Epilepsy 2017 classification. We applied three neural network models-a convolutional neural network (CNN) and a CNN-long short-term memory (LSTM)-based generalized detection model and an autoencoder-based personalized detection model-to the raw time-series sensor data to detect seizures and utilized performance measures, including sensitivity, false positive rate (the number of false alarms divided by the total number of nonseizure segments), number of false alarms per day, and detection delay. We applied a 10-fold patientwise cross-validation scheme to the multisignal biosensor data and evaluated model performance on 28 seizure types.

RESULTS

We analyzed 166 patients (47.6% female, median age = 10.0 years) and 900 seizures (13 254 h of sensor data) for 28 seizure types. With a CNN-LSTM-based seizure detection model, ACC, BVP, and their fusion performed better than chance; ACC and BVP data fusion reached the best detection performance of 83.9% sensitivity and 35.3% false positive rate. Nineteen of 28 seizure types could be detected by at least one data modality with area under receiver operating characteristic curve > .8 performance.

SIGNIFICANCE

Results from this in-hospital study contribute to a paradigm shift in epilepsy care that entails noninvasive seizure detection, provides time-sensitive and accurate data on additional clinical seizure types, and proposes a novel combination of an out-of-the-box monitoring algorithm with an individualized person-oriented seizure detection approach.

Video-based sympathetic arousal assessment via peripheral blood flow estimation

Electrodermal activity (EDA) is considered a standard marker of sympathetic activity. However, traditional EDA measurement requires electrodes in steady contact with the skin. Can sympathetic arousal be measured using only an optical sensor, such as an RGB camera? This paper presents a novel approach to infer sympathetic arousal by measuring the peripheral blood flow on the face or hand optically. We contribute a self-recorded dataset of 21 participants, comprising synchronized videos of participants' faces and palms and gold-standard EDA and photoplethysmography (PPG) signals. Our results show that we can measure peripheral sympathetic responses that closely correlate with the ground truth EDA. We obtain median correlations of 0.57 to 0.63 between our inferred signals and the ground truth EDA using only videos of the participants' palms or foreheads or PPG signals from the foreheads or fingers. We also show that sympathetic arousal is best inferred from the forehead, finger, or palm.

Subjective risk and associated electrodermal activity of a self-driving car passenger in an urban shared space

Shared spaces are urban areas without physical separation between motorised and non-motorised users. Previous research has suggested that it is difficult for users to appropriate these spaces and that the advent of self-driving cars could further complicate interactions. It is therefore important to study the perception of these spaces from the users' perspectives to determine which conditions may promote their acceptance of the vehicles. This study investigates the perceived collision risk of a self-driving car's passenger when pedestrians cross the vehicle's path. The experiment was conducted with a driving simulator. Seven factors were manipulated to vary the dynamics of the crossing situations in order to analyse their influence on the passenger's perception of collision risk. Two measures of perceived risk were obtained. A continuous subjective assessment, reflecting an explicit risk evaluation, was reported in real time by participants. On the other hand, their skin conductance responses, which reflects implicit information processing, were recorded. The relationship between the factors and the risk perception indicators was studied using Bayesian networks. The best Bayesian networks demonstrate that subjective collision risk assessments are primarily influenced by the factors that determine the relative positions of the vehicle and the pedestrian as well as the distance between them when they are in close proximity. The analysis further reveals that variations in skin conductance response indicators are more likely to be explained by variations in subjective assessments than by variations in the manipulated factors. These findings could benefit the development of self-driving navigation among pedestrians by improving understanding of the factors that influence passengers' feelings.

Student engagement in health professions education: AMEE Guide No. 152

This guide aims to support our colleagues to have comprehensive understanding of student engagement in health professions education. Despite the universal agreement about the significance of student engagement, there is lack of uniformity in conceptualizing and operationalizing this emerging construct. We review the theoretical basis explaining student engagement from three main perspectives: behavioral, psychological, and socio-cultural. In addition, we propose a contemporary and comprehensive framework for the student engagement in higher education, which is applicable to health professions education contexts. Drawing from this framework, we explain the conceptualization of the construct and its preceding factors, mediators, dimensions, spheres, and outcomes of student engagement. The proposed framework introduces student 'engagement through partnerships' as a novel component compared with the existing models of student engagement in higher education. This way, we are proposing a mixed model that not only considers the student as a 'customer' but also as a 'partner' in education. Engagement of students through partnerships include four areas: (1) provision of the education program, (2) scholarly research, (3) governance and quality assurance, and (4) community activities. This guide will provide practical applications on how to improve student engagement in health professions education. Finally, we highlight the current gaps in areas of research in the student engagement literature and suggested plans for future directions.[Box: see text].

Feasibility of wearable sensors in the NICU: Psychophysiological measures of parental stress

BACKGROUND

Parents report elevated stress during their infant's NICU hospitalization. Real-time measures may improve our understanding of parental stress in the NICU.

AIM

Examine the feasibility of wearable sensors to describe parental stress in the NICU.

STUDY DESIGN

In this prospective feasibility study of 12 parent-infant dyads, parents wore an Empatica E4 wristband to measure psychophysiological stress via electrodermal activity (EDA) during sensory interventions (holding, massage, reading, touch, etc) with their babies. Baseline and intervention periods were delineated during which concurrent monitoring and clinical observations of infant behavior and environmental factors were recorded. Feasibility was assessed by investigating recruitment/enrollment, retention/adherence, acceptability, sensor usability, and changes in EDA waveforms based on potential stressors. For the latter, independent samples t-tests and ANOVA were used to examine differences in EDA from baseline to intervention, and the impact of environmental and infant factors on parent stress were visually analyzed against EDA waveforms.

RESULTS

Wearable sensor use in the NICU appeared feasible as assessed by all methods. Preliminary data analysis indicated that overall parent EDA levels during parent-infant interactions were low, and engagement in sensory intervention(s) led to a non-significant increase in parental EDA, measured by both skin conductance levels and non-specific skin conductance responses. Three main patterns of EDA emerged: a temporary increase in EDA at the beginning of the intervention followed by a decrease and plateau, a plateau in EDA from baseline to intervention, and a gradual rise in EDA throughout intervention. Specific environmental and infant factors, such as infant stress and health care providers entering the room, appeared to impact parent stress levels.

CONCLUSION

Although these preliminary findings provide support for use of EDA in the NICU, future studies are needed.

The temporal dynamics of dissociation: protocol for an ecological momentary assessment and laboratory study in a transdiagnostic sample

BACKGROUND

Dissociation is a ubiquitous clinical phenomenon. Dissociative disorders (DD) are primarily characterized by dissociation, and dissociative states are also a criterion for borderline personality disorder (BPD) and the dissociative subtype of post-traumatic stress disorder (PTSD). Dissociative reactions (e.g., depersonalization/derealization or gaps in awareness/memory) across diagnostic categories are believed to be affect contingent and theorized to serve affect regulation functions. What is not clear, however, is how self-reported affect and physiological reactivity unfold within dissociative episodes. To address this issue, the present project aims to investigate the hypothesis (1) whether self-reported distress (as indicated by arousal, e.g., feeling tense/agitated, and/or valence, e.g., feeling discontent/unwell) and physiological reactivity increase before dissociative episodes and (2) whether self-reported distress and physiological reactivity decrease during and after dissociative episodes in a transdiagnostic sample of patients with DD, BPD, and/or PTSD.

METHODS

We will use a smartphone application to assess affect and dissociation 12 times per day over the course of one week in everyday life. During this time, heart and respiratory rates will be remotely monitored. Afterwards, participants will report affect and dissociative states eight times in the laboratory before, during, and after the Trier Social Stress Test. During the laboratory task, we will continuously record heart rate, electrodermal activity, and respiratory rate, as well as measure blood pressure and take salivary samples to determine cortisol levels. Our hypotheses will be tested using multilevel structural equation models. Power analyses determined a sample size of 85.

DISCUSSION

The project will test key predictions of a transdiagnostic model of dissociation based on the idea that dissociative reactions are affect contingent and serve affect regulation functions. This project will not include non-clinical control participants. In addition, the assessment of dissociation is limited to pathological phenomena.

Affective Impressions Recognition under Different Colored Lights Based on Physiological Signals and Subjective Evaluation Method

The design of the light environment plays a critical role in the interaction between people and visual objects in space. Adjusting the space's light environment to regulate emotional experience is more practical for the observers under lighting conditions. Although lighting plays a vital role in spatial design, the effects of colored lights on individuals' emotional experiences are still unclear. This study combined physiological signal (galvanic skin response (GSR) and electrocardiography (ECG)) measurements and subjective assessments to detect the changes in the mood states of observers under four sets of lighting conditions (green, blue, red, and yellow). At the same time, two sets of abstract and realistic images were designed to discuss the relationship between light and visual objects and their influence on individuals' impressions. The results showed that different light colors significantly affected mood, with red light having the most substantial emotional arousal, then blue and green. In addition, GSR and ECG measurements were significantly correlated with impressions evaluation results of interest, comprehension, imagination, and feelings in subjective evaluation. Therefore, this study explores the feasibility of combining the measurement of GSR and ECG signals with subjective evaluations as an experimental method of light, mood, and impressions, which provided empirical evidence for regulating individuals' emotional experiences.

Modeling the Design Characteristics of Woven Textile Electrodes for long-Term ECG Monitoring

An electrocardiograph records the periodic voltage generated by the heart over time. There is growing demand to continuously monitor the ECG for proactive health care and human performance optimization. To meet this demand, new conductive textile electrodes are being developed which offer an attractive alternative to adhesive gel electrodes but they come with their own challenges. The key challenge with textile electrodes is that the relationship between the manufacturing parameters and the ECG measurement is not well understood, making design an iterative process without the ability to prospectively develop woven electrodes with optimized performance. Here we address this challenge by applying the traditional skin-electrode interface circuit model to woven electrodes by constructing a parameterized model of the ECG system. Then the unknown parameters of the system are solved for with an iterative MATLAB optimizer using measured data captured with the woven electrodes. The results of this novel analysis confirm that yarn conductivity and total conductive area reduce skin electrode impedance. The results also indicate that electrode skin pressure and moisture require further investigation. By closing this gap in development, textile electrodes can be better designed and manufactured to meet the demands of long-term ECG capture.

Ergonomic human-robot collaboration in industry: A review

In the current industrial context, the importance of assessing and improving workers' health conditions is widely recognised. Both physical and psycho-social factors contribute to jeopardising the underlying comfort and well-being, boosting the occurrence of diseases and injuries, and affecting their quality of life. Human-robot interaction and collaboration frameworks stand out among the possible solutions to prevent and mitigate workplace risk factors. The increasingly advanced control strategies and planning schemes featured by collaborative robots have the potential to foster fruitful and efficient coordination during the execution of hybrid tasks, by meeting their human counterparts' needs and limits. To this end, a thorough and comprehensive evaluation of an individual's ergonomics, i.e. direct effect of workload on the human psycho-physical state, must be taken into account. In this review article, we provide an overview of the existing ergonomics assessment tools as well as the available monitoring technologies to drive and adapt a collaborative robot's behaviour. Preliminary attempts of ergonomic human-robot collaboration frameworks are presented next, discussing state-of-the-art limitations and challenges. Future trends and promising themes are finally highlighted, aiming to promote safety, health, and equality in worldwide workplaces.

Parents' differential susceptibility to a "micro" parenting intervention: Rationale and study protocol for a randomized controlled microtrial

BACKGROUND

Given evidence that parenting can influence children's development, parenting interventions are often the strategy of choice when it comes to treating children's disruptive behavior problems-or preventing problems from developing in the first place. What remains under appreciated, however, is that some parents appear to be more responsive to interventions to foster skilled parenting than others. Notable in this regard is the ever-increasing observational and, perhaps more importantly, experimental evidence indicating that some children prove more susceptible to parenting interventions than others. So, while the experimental evidence clearly indicates that "susceptibility factors" which children carry seem to affect their likelihood of benefiting from a parenting intervention (and other environmental influences), what remains unclear is why the parenting interventions in question prove more effective in changing the behavior of some parents more than others. Could it be as a result of their own parental characteristics?

OBJECTIVE

The Parfective Microtrial in a randomized controlled microtrial, in which we focus not just on parental (and child) responsiveness but also on an underlying physiological mechanism hypothesized to contribute to heightened susceptibility to parenting interventions.

METHODS

Participants are 120 families, with children aged 4-5 years, recruited from the community. Of these, 60 are randomly assigned to the "micro" intervention condition (i.e., immediate positive parenting feedback) and 60 families to the care-as-usual control condition. Assessments in both conditions will be conducted at baseline (pretest), after 2 weeks (posttest), and after 4 weeks (follow-up). Primary outcomes are the hypothesized moderating effects of physiology on the anticipated "micro" intervention effect (i.e., decrease in negative parenting behavior and/or increase in positive parenting behavior). Secondary outcomes are the observed (changes in) child behavior in response to the parenting intervention, such that those parents and children-in the same family-who manifest these physiological attributes will prove most susceptible to the beneficial effects of the intervention.

TRIAL REGISTRATION

This study protocol is registered at ClinicalTrials.gov (NCT05539170).

Examining Stress and Residual Symptoms in Remitted and Partially Remitted Depression Using a Wearable Electrodermal Activity Device: A Pilot Study

Consistent evidence suggests residual symptoms and stress are the most reliable predictors of relapse in remitted depression. Prevailing methodologies often do not enable continuous real-time sampling of stress. Thus, little is known about day-to-day interactions between residual symptoms and stress in remitted depression. In preparation for a full-scale trial, this study aimed to pilot a wrist-worn wearable electrodermal activity monitor: ADI (Analog Devices, Inc.) Study Watch for assessing interactions between physiological stress and residual depressive symptoms following depression remission. 13 individuals remitted from major depression completed baseline, daily diary, and post-daily diary assessments. Self-reported stress and residual symptoms were measured at baseline and post-daily diary. Diary assessments required participants to wear ADI's Study Watch during waking hours and complete self-report questionnaires every evening over one week. Sleep problems, fatigue, energy loss, and agitation were the most frequently reported residual symptoms. Average skin conductance responses (SCRs) were 16.09 per-hour, with an average of 11.30 hours of wear time per-day. Increased residual symptoms were associated with enhanced self-reported stress on the same day. Increased SCRs on one day predicted increased residual symptoms on the next day. This study showed a wearable electrodermal activity device can be recommended for examining stress as a predictor of remitted depression. This study also provides preliminary work on relationships between residual symptoms and stress in remitted depression. Importantly, significant findings from the small sample of this pilot are preliminary with an aim to follow up with a 3-week full-scale study to draw conclusions about psychological processes explored. Clinical and Translational Impact Statemen-ADI's wearable electrodermal activity device enables a continuous measure of physiological stress for identifying its interactions with residual depressive symptoms following remission. This novel procedure is promising for future studies.

Data quality evaluation in wearable monitoring

Wearable recordings of neurophysiological signals captured from the wrist offer enormous potential for seizure monitoring. Yet, data quality remains one of the most challenging factors that impact data reliability. We suggest a combined data quality assessment tool for the evaluation of multimodal wearable data. We analyzed data from patients with epilepsy from four epilepsy centers. Patients wore wristbands recording accelerometry, electrodermal activity, blood volume pulse, and skin temperature. We calculated data completeness and assessed the time the device was worn (on-body), and modality-specific signal quality scores. We included 37,166 h from 632 patients in the inpatient and 90,776 h from 39 patients in the outpatient setting. All modalities were affected by artifacts. Data loss was higher when using data streaming (up to 49% among inpatient cohorts, averaged across respective recordings) as compared to onboard device recording and storage (up to 9%). On-body scores, estimating the percentage of time a device was worn on the body, were consistently high across cohorts (more than 80%). Signal quality of some modalities, based on established indices, was higher at night than during the day. A uniformly reported data quality and multimodal signal quality index is feasible, makes study results more comparable, and contributes to the development of devices and evaluation routines necessary for seizure monitoring.

Compensation for Electrode Detachment in Electrical Impedance Tomography with Wearable Textile Electrodes

Electrical impedance tomography (EIT) is a radiation-free and noninvasive medical image reconstruction technique in which a current is injected and the reflected voltage is received through electrodes. EIT electrodes require good connection with the skin for data acquisition and image reconstruction. However, detached electrodes are a common occurrence and cause measurement errors in EIT clinical applications. To address these issues, in this study, we proposed a method for detecting faulty electrodes using the differential voltage value of the detached electrode in an EIT system. Additionally, we proposed the voltage-replace and voltage-shift methods to compensate for invalid data from the faulty electrodes. In this study, we present the simulation, experimental, and in vivo chest results of our proposed methods to verify and evaluate the feasibility of this approach.

Graphene e-tattoos for unobstructive ambulatory electrodermal activity sensing on the palm enabled by heterogeneous serpentine ribbons

Electrodermal activity (EDA) is a popular index of mental stress. State-of-the-art EDA sensors suffer from obstructiveness on the palm or low signal fidelity off the palm. Our previous invention of sub-micron-thin imperceptible graphene e-tattoos (GET) is ideal for unobstructive EDA sensing on the palm. However, robust electrical connection between ultrathin devices and rigid circuit boards is a long missing component for ambulatory use. To minimize the well-known strain concentration at their interfaces, we propose heterogeneous serpentine ribbons (HSPR), which refer to a GET serpentine partially overlapping with a gold serpentine without added adhesive. A fifty-fold strain reduction in HSPR vs. heterogeneous straight ribbons (HSTR) has been discovered and understood. The combination of HSPR and a soft interlayer between the GET and an EDA wristband enabled ambulatory EDA monitoring on the palm in free-living conditions. A newly developed EDA event selection policy leveraging unbiased selection of phasic events validated our GET EDA sensor against gold standards.

ComEDA: A new tool for stress assessment based on electrodermal activity

Non-specific sympathetic arousal responses to different stressful elicitations can be easily recognized from the analysis of physiological signals. However, neural patterns of sympathetic arousal during physical and mental fatigue are clearly not unitary. In the context of physiological monitoring through wearable and non-invasive devices, electrodermal activity (EDA) is the most effective and widely used marker of sympathetic activation. This study presents ComEDA, a novel approach for the characterization of complex dynamics of EDA. ComEDA overcomes the methodological limitations related to the application of nonlinear analysis to EDA dynamics, is not parameter-sensitive and is suitable for the analysis of ultra-short time series. We validated the proposed algorithm using synthetic series of white noise and 1/f noise, varying the number of samples from 50 to 5000. By applying our approach, we were able to discriminate a statistically significant increase of complexity in the 1/f noise with respect to white noise, obtaining p-values in the range [4.35 × 10-6, 0.03] after the Mann-Whitney test. Then, we tested ComEDA on both EDA signal and its tonic and phasic components, acquired from healthy subjects during four experimental protocols: two inducing a sympathetic activation through physical efforts and two based on mentally stressful tasks. Results are encouraging and promising, outperforming state of the art metrics such as the Sample Entropy. ComEDA shows good performance not only in discriminating between stressful tasks and resting state (p-value < 0.01 after the Wilcoxon non-parametric statistical test applied to EDA signals of all the four datasets), but also in differentiating different trends of complexity of EDA dynamics when induced by physical and mental stressors. These findings suggest future applications to automatically detect and selectively identify threats due to overwhelming stress impacting both physical and mental health or in the field of telemedicine to monitor autonomic diseases correlated to atypical sympathetic activation. The Matlab code implementing the ComEDA algorithm is available online.

Seizure-related differences in biosignal 24-h modulation patterns

A seizure likelihood biomarker could improve seizure monitoring and facilitate adjustment of treatments based on seizure risk. Here, we tested differences in patient-specific 24-h-modulation patterns of electrodermal activity (EDA), peripheral body temperature (TEMP), and heart rate (HR) between patients with and without seizures. We enrolled patients who underwent continuous video-EEG monitoring at Boston Children's Hospital to wear a biosensor. We divided patients into two groups: those with no seizures and those with at least one seizure during the recording period. We assessed the 24-h modulation level and amplitude of EDA, TEMP, and HR. We performed machine learning including physiological and clinical variables. Subsequently, we determined classifier performance by cross-validated machine learning. Patients with seizures (n = 49) had lower EDA levels (p = 0.031), EDA amplitudes (p = 0.045), and trended toward lower HR levels (p = 0.060) compared to patients without seizures (n = 68). Averaged cross-validated classification accuracy was 69% (AUC-ROC: 0.75). Our results show the potential to monitor and forecast risk for epileptic seizures based on changes in 24-h patterns in wearable recordings in combination with clinical variables. Such biomarkers might be applicable to inform care, such as treatment or seizure injury risk during specific periods, scheduling diagnostic tests, such as admission to the epilepsy monitoring unit, and potentially other neurological and chronic conditions.

Are Psychophysiological Wearables Suitable for Comparing Pedagogical Teaching Approaches?

This study describes how wearable devices can be used in elementary schools to compare some aspects of different teaching approaches. Upper arm wearables were used as an objective tool to compare three approaches when teaching science: (i) classical frontal teaching, (ii) embodied (kinesthetic) teaching, and (iii) a distance teaching approach. Using the wearables, the approaches were compared in terms of their impact on students' psychological arousal and perceived well-being. In addition, short-term and long-term knowledge gain and physiological synchronization between teacher and students during the lecture were assessed. A synchronization index was defined to estimate the degree of physiological synchronization. During distance teaching, by means of measurements with wearables, students were significantly less physically active and significantly less psychologically aroused. Embodied teaching allowed significantly higher physical activation than during the other two approaches. The synchronization index for all three teaching approaches was positive with the highest values for distance and frontal teaching. Moreover, knowledge gain immediately after the embodied lessons was higher than after frontal lessons. No significant differences in the long-term knowledge retention between the three different teaching methods were found. This pilot study proved that wearables are a useful tool in research in the field of education and have the potential to contribute to a deeper understanding of the mechanisms involved in learning, even in complex environments such as an elementary school classroom.

A Deep Convolutional Autoencoder for Automatic Motion Artifact Removal in Electrodermal Activity Signals: A Preliminary Study

Automatic motion artifact (MA) removal in electrodermal activity (EDA) signals is a major challenge because of the aperiodic and irregular characteristics of EDA. Given the lack of a suitable MA removal algorithm, a substantial amount of EDA data is typically discarded, especially during ambulatory monitoring. Current methods for MA removal in EDA are feasible when data are corrupted with low magnitude artifacts. In this study, we propose a more data-driven deep convolutional autoencoder (DCAE) for automated motion artifact removal in EDA signals. The DCAE was trained using several publicly available datasets. We used both Gaussian white noise (GWN) and real-life induced MA data records collected in a laboratory setting to corrupt the clean EDA signals. We compared the performance of our DCAE network with three state-of-the-art methods using the performance metrics the signal-to-noise ratio (SNR) improvement (SNR_imp), and the mean squared error (MSE). The proposed DCAE provided significantly higher SNR_impand lower MSE compared to three other methods for both synthetically and real-life induced MA. While the work is preliminary, this work illustrates a promising approach which can potentially be used to remove many different types of MA.

Wearables: An R Package With Accompanying Shiny Application for Signal Analysis of a Wearable Device Targeted at Clinicians and Researchers

Physiological signals (e.g., heart rate, skin conductance) that were traditionally studied in neuroscientific laboratory research are currently being used in numerous real-life studies using wearable technology. Physiological signals obtained with wearables seem to offer great potential for continuous monitoring and providing biofeedback in clinical practice and healthcare research. The physiological data obtained from these signals has utility for both clinicians and researchers. Clinicians are typically interested in the day-to-day and moment-to-moment physiological reactivity of patients to real-life stressors, events, and situations or interested in the physiological reactivity to stimuli in therapy. Researchers typically apply signal analysis methods to the data by pre-processing the physiological signals, detecting artifacts, and extracting features, which can be a challenge considering the amount of data that needs to be processed. This paper describes the creation of a “Wearables” R package and a Shiny “E4 dashboard” application for an often-studied wearable, the Empatica E4. The package and Shiny application can be used to visualize the relationship between physiological signals and real-life stressors or stimuli, but can also be used to pre-process physiological data, detect artifacts, and extract relevant features for further analysis. In addition, the application has a batch process option to analyze large amounts of physiological data into ready-to-use data files. The software accommodates users with a downloadable report that provides opportunities for a careful investigation of physiological reactions in daily life. The application is freely available, thought to be easy to use, and thought to be easily extendible to other wearable devices. Future research should focus on the usability of the application and the validation of the algorithms.

The Prospects of Non-EEG Seizure Detection Devices in Dogs

The unpredictable nature of seizures is challenging for caregivers of epileptic dogs, which calls the need for other management strategies such as seizure detection devices. Seizure detection devices are systems that rely on non-electroencephalographic (non-EEG) ictal changes, designed to detect seizures. The aim for its use in dogs would be to provide owners with a more complete history of their dog's seizures and to help install prompt (and potentially life-saving) intervention. Although seizure detection via wearable intracranial EEG recordings is associated with a higher sensitivity in humans, there is robust evidence for reliable detection of generalized tonic-clonic seizures (GTCS) using non-EEG devices. Promising non-EEG changes described in epileptic humans, include heart rate variability (HRV), accelerometry (ACM), electrodermal activity (EDA), and electromyography (EMG). Their sensitivity and false detection rate to detect seizures vary, however direct comparison of studies is nearly impossible, as there are many differences in study design and standards for testing. A way to improve sensitivity and decrease false-positive alarms is to combine the different parameters thereby profiting from the strengths of each one. Given the challenges of using EEG in veterinary clinical practice, non-EEG ictal changes could be a promising alternative to monitor seizures more objectively. This review summarizes various seizure detection devices described in the human literature, discusses their potential use and limitations in veterinary medicine and describes what is currently known in the veterinary literature.

Carrying a passenger and relaxation before driving: Classification of young drivers' physiological activation

Drivers are often held responsible for road crashes. Previous research has shown that stressors such as carrying passengers in the vehicle can be a source of accidents for young drivers. To mitigate this problem, this study investigated whether the presence of a passenger behind the wheel can be predicted using machine learning, based on physiological signals. It also addresses the question whether relaxation before driving can positively influence the driver's state and help controlling the potential negative consequences of stressors. Sixty young participants completed a 10‐min driving simulator session, either alone or with a passenger. Before their driving session, participants spent 10 min relaxing or listening to an audiobook. Physiological signals were recorded throughout the experiment. Results show that drivers experience a higher increase in skin conductance when driving with a passenger, which can be predicted with 90%‐accuracy by a k‐nearest neighbors classifier. This might be a possible explanation for increased risk taking in this age group. Besides, the practice of relaxation can be predicted with 80% accuracy using a neural network. According to the statistical analysis, the potential beneficial effect of relaxation did not carry out on the driver's physiological state while driving, although machine learning techniques revealed that participants who exercised relaxation before driving could be recognized with 70% accuracy. Analysis of physiological characteristics after classification revealed several relevant physiological indicators associated with the presence of a passenger and relaxation.

Effect of active learning and online discussions on the academic performances of dental students

BACKGROUND

COVID-19 caused significant confusion around the world, and dental education was no exception. Therefore, in line with the demands of the times, this study sought to determine the applicability of online active learning to dental education.

METHODS

This study was conducted in the second semester of 2020 at a school of dentistry in a selective university in Korea. A total of 114 dental students were recruited. Participants were assigned to four different groups (lecture and discussion [LD], lecture and discussion with instructor's worksheet [LW], self-study and discussion [SSD], and self-study and discussion with instructor's worksheet [SW]) using the random breakout room function in the Zoom video conference application. Their final test scores were then analyzed using analysis of variance and the online active learning results were compared with the offline learning results.

RESULTS

The scores were highest for the transfer type items in the SSD group, followed by the SW group and the two lecture groups, which had no significant differences. These scores and pattern differences between the groups were similar for all items. The results suggested that studying by oneself rather than simply listening to lectures enhanced the effects of the discussions and led to higher learning outcomes. In addition, the effect of the instructor's intervention in the middle of the discussion varied depending on the pre-learning activities of discussion. As with previous offline experiments, self-study followed by group discussion had higher learning outcomes for both the verbatim and transfer type items.

CONCLUSIONS

In agreement with the Interactive, Constructive, Active, and Passive (ICAP) framework and other active learning theories, the findings clearly indicated that online active learning was applicable to dental students, and when self-study precedes discussion, the learning is richer and the learning outcomes are better.

Comparison of Electrodermal Activity from Multiple Body Locations Based on Standard EDA Indices' Quality and Robustness against Motion Artifact

The most traditional sites for electrodermal activity (EDA) data collection, palmar locations such as fingers or palms, are not usually recommended for ambulatory monitoring given that subjects have to use their hands regularly during their daily activities, and therefore, alternative sites are often sought for EDA data collection. In this study, we collected EDA signals (n = 23 subjects, 19 male) from four measurement sites (forehead, back of neck, finger, and inner edge of foot) during cognitive stress and induction of mild motion artifacts by walking and one-handed weightlifting. Furthermore, we computed several EDA indices from the EDA signals obtained from different sites and evaluated their efficiency to classify cognitive stress from the baseline state. We found a high within-subject correlation between the EDA signals obtained from the finger and the feet. Consistently high correlation was also found between the finger and the foot EDA in both the phasic and tonic components. Statistically significant differences were obtained between the baseline and cognitive stress stage only for the EDA indices computed from the finger and the foot EDA. Moreover, the receiver operating characteristic curve for cognitive stress detection showed a higher area-under-the-curve for the EDA indices computed from the finger and foot EDA. We also evaluated the robustness of the different body sites against motion artifacts and found that the foot EDA location was the best alternative to other sites.

Emotion Regulation in Adolescents: Evidence of the Validity and Factor Structure of the Cognitive Emotion Regulation Questionnaire (CERQ)

The Cognitive Emotion Regulation Questionnaire (CERQ) is an assessment tool to evaluate cognitive emotion regulation strategies. The main objective of this study is to provide new empirical evidence about the validity and reliability of the CERQ via a sample of 271 Spanish adolescents (136 female, 135 male) aged from 15 to 18 years (M = 15.7, SD = 0.76). The analytical process was carried out in two phases. A confirmatory factor analysis was performed on the polychoric correlation matrix between items. Four possible alternative models were contrasted: two models with nine factors and two models with two second-order factors and nine first-order factors, with 36 and 27 items, respectively. The model with nine correlated factors and 27 items obtained the best indices of overall fit. Subsequently, the reliability of the measurements was estimated on this model. The results reaffirm the validity of the 27-item version of the CERQ over the original 36-item structure. The findings also confirm that the CERQ is a reliable instrument for the evaluation of emotion regulation strategies in adolescents.

Beat-to-beat Estimation of Peripheral Arterial Stiffness from Local PWV for Quantitative Evaluation of Sympathetic Nervous System Activity

Sympathetic nervous system activity (SNSA) can rapidly modulate arterial stiffness, thus making it an important biomarker for SNSA evaluation. Pulse wave velocity (PWV) is a well-known quantitative indicator of arterial stiffness, but its functional responsivity to SNSA has not been elucidated. This paper reports a method to estimate rapid changes in peripheral arterial stiffness induced by SNSA using local PWV (LPWV) and to further quantify SNSA based on the estimated stiffness. LPWV was measured from the artery near the wrist to the artery near the forefinger using a biodegradable piezoelectric sensor and a photoplethysmography sensor in an electrocutaneous stimulus experiment in which pain indicts the SNSA. The relationship between LPWV, simultaneously measured peripheral arterial stiffness index, and self-reported pain intensity was quantified. The stiffness estimated by LPWV alone and the stiffness estimated by LPWV and arterial pressure both approximate the peripheral arterial stiffness index (R² = 0.9775 and 0.9719). Pain intensity can be quantitatively evaluated in a sigmoidal relationship by either the estimated stiffness based on LPWV alone (r = 0.8594) or the estimated stiffness based on LPWV and arterial pressure (r = 0.9738). Our results demonstrated the validity of LPWV in the quantitative evaluation of SNSA and the optionality of blood pressure correction depending on application scenarios. This study advances the understanding of sympathetic innervation of peripheral arteries through the sympathetic responsivity of LPWV and contributes a quantitative biomarker for SNSA evaluation.

Current trends and opportunities in the methodology of electrodermal activity measurement

Electrodermal activity (EDA) has been measured in the laboratory since the late 1800s. Although the influence of sudomotor nerve activity and the sympathetic nervous system on EDA is well established, the mechanisms underlying EDA signal generation are not completely understood. Owing to simplicity of instrumentation and modern electronics, these measurements have recently seen a transfer from the laboratory to wearable devices, sparking numerous novel applications while bringing along both challenges and new opportunities. In addition to developments in electronics and miniaturization, current trends in material technology and manufacturing have sparked innovations in electrode technologies, and trends in data science such as machine learning and sensor fusion are expanding the ways that measurement data can be processed and utilized. Although challenges remain for the quality of wearable EDA measurement, ongoing research and developments may shorten the quality gap between wearable EDA and standardized recordings in the laboratory. In this topical review, we provide an overview of the basics of EDA measurement, discuss the challenges and opportunities of wearable EDA, and review recent developments in instrumentation, material technology, signal processing, modeling and data science tools that may advance the field of EDA research and applications over the coming years.

Owner's Perception of Seizure Detection Devices in Idiopathic Epileptic Dogs

Accurate knowledge of seizure frequency is key to optimising treatment. New methods for detecting epileptic seizures are currently investigated in humans, which rely on changes in biomarkers, also called seizure detection devices. Critical to device development, is understanding user needs and requirements. No information on this subject has been published in veterinary medicine. Many dog health collars are currently on the market, but none has proved to be a promising seizure detector. An online survey was created and consisted of 27 open, closed, and scaled questions divided over two parts: part one focused on general questions related to signalment and seizure semiology, the second part focused specifically on the use of seizure detection devices. Two hundred and thirty-one participants caring for a dog with idiopathic epilepsy, were included in the study. Open questions were coded using descriptive coding by two of the authors independently. Data was analysed using descriptive statistics and binary logistic regression. Our results showed that the unpredictability of seizures plays a major part in the management of canine epilepsy and dog owners have a strong desire to know when a seizure occurs. Nearly all dog owners made changes in their daily life, mainly focusing on intensifying supervision. Owners believed seizure detection devices would improve their dog's seizure management, including a better accuracy of seizure frequency and the ability to administer emergency drugs more readily. Owners that were already keeping track of their dog's seizures were 4.2 times more likely to show confidence in using seizure detection devices to manage their pet's seizures, highlighting the need for better monitoring systems. Our results show that there is a receptive market for wearable technology as a new management strategy in canine epilepsy and this topic should be further explored.

Medical Students' Opinions of Anatomy Teaching Resources and Their Role in Achieving Learning Outcomes

Several teaching resources are used to enhance the learning of anatomy. The purpose of this study was to examine the preference of medical students on the use of various resources to learn anatomy and their link to 12 learning outcomes. A selected response item questionnaire was administered that asked students to rank six laboratory teaching resources from most to least preferred, and rate how useful these six resources were towards achieving 12 learning outcomes. These learning outcomes covered many of the learning domains such as demonstrating an understanding of anatomy, visualizing structures, appreciating clinical correlations, and understanding anatomical variations. Medical students ranked cadaveric prosections paired with an active learning clinical tutorial as the highest rank and most useful resource for learning anatomy, followed by dissection videos, electronic resources, and printed material, followed by plastinated specimens and plastic models. Overall, cadaveric prosections were also rated as the most helpful teaching resource in achieving various learning outcomes. In conclusion, anatomy teachers should provide prosections coupled with clinical tutorials as well as electronic resources as students prefer these and think they help them learn anatomy. Future studies will investigate the impact of using these resources on students' performance.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s40670-021-01436-2.

Dry Wearable Textile Electrodes for Portable Electrical Impedance Tomography

Electrical impedance tomography (EIT), a noninvasive and radiation-free medical imaging technique, has been used for continuous real-time regional lung aeration. However, adhesive electrodes could cause discomfort and increase the risk of skin injury during prolonged measurement. Additionally, the conductive gel between the electrodes and skin could evaporate in long-term usage and deteriorate the signal quality. To address these issues, in this work, textile electrodes integrated with a clothing belt are proposed to achieve EIT lung imaging along with a custom portable EIT system. The simulation and experimental results have verified the validity of the proposed portable EIT system. Furthermore, the imaging results of using the proposed textile electrodes were compared with commercial electrocardiogram electrodes to evaluate their performance.

Validity of electrodermal activity-based measures of sympathetic nervous system activity from a wrist-worn device

Measuring electrodermal activity (EDA) on the wrist with the use of dry electrodes is a promising method to help identify person-specific stressors during prolonged recordings in daily life. While the feasibility of this method has been demonstrated, detailed testing of validity of such ambulatory EDA is scarce. In a controlled laboratory study, we examine SCL and ns.SCR derived from wrist-based dry electrodes (Philips DTI) and palm-based wet electrodes (VU-AMS) in 112 healthy adults (57% females, mean age = 22.3, SD = 3.4) across 26 different conditions involving mental stressors or physical activities. Changes in these EDA measures were compared to changes in the Pre-ejection period (PEP) and stressor-induced changes in affect. Absolute SCL and ns.SCR frequency were lower at the wrist compared to the palm. Wrist-based ns.SCR and palm-based ns.SCR and SCL responded directionally consistent with our experimental manipulation of sympathetic nervous system (SNS) activity. Average within-subject correlations between palm-based and wrist-based EDA were significant but modest (r SCL = 0.31; r ns.SCR = 0.42). Changes in ns.SCR frequency at the palm (r = -0.44) and the wrist (r = -0.36) were correlated with changes in PEP. Both palm-based and wrist based EDA predicted changes in affect (6.5%-14.5%). Our data suggest that wrist-based ns.SCR frequency is a useful addition to the psychophysiologist's toolkit, at least for epidemiology-sized ambulatory studies of changes in sympathetic activity during daily life.

The Exposome in the Era of the Quantified Self

Human health is regulated by complex interactions among the genome, the microbiome, and the environment. While extensive research has been conducted on the human genome and microbiome, little is known about the human exposome. The exposome comprises the totality of chemical, biological, and physical exposures that individuals encounter over their lifetimes. Traditional environmental and biological monitoring only targets specific substances, whereas exposomic approaches identify and quantify thousands of substances simultaneously using nontargeted high-throughput and high-resolution analyses. The quantified self (QS) aims at enhancing our understanding of human health and disease through self-tracking. QS measurements are critical in exposome research, as external exposures impact an individual's health, behavior, and biology. This review discusses both the achievements and the shortcomings of current research and methodologies on the QS and the exposome and proposes future research directions.

Seizure detection using wearable sensors and machine learning: Setting a benchmark

OBJECTIVE

Tracking seizures is crucial for epilepsy monitoring and treatment evaluation. Current epilepsy care relies on caretaker seizure diaries, but clinical seizure monitoring may miss seizures. Wearable devices may be better tolerated and more suitable for long-term ambulatory monitoring. This study evaluates the seizure detection performance of custom-developed machine learning (ML) algorithms across a broad spectrum of epileptic seizures utilizing wrist- and ankle-worn multisignal biosensors.

METHODS

We enrolled patients admitted to the epilepsy monitoring unit and asked them to wear a wearable sensor on either their wrists or ankles. The sensor recorded body temperature, electrodermal activity, accelerometry (ACC), and photoplethysmography, which provides blood volume pulse (BVP). We used electroencephalographic seizure onset and offset as determined by a board-certified epileptologist as a standard comparison. We trained and validated ML for two different algorithms: Algorithm 1, ML methods for developing seizure type-specific detection models for nine individual seizure types; and Algorithm 2, ML methods for building general seizure type-agnostic detection, lumping together all seizure types.

RESULTS

We included 94 patients (57.4% female, median age = 9.9 years) and 548 epileptic seizures (11 066 h of sensor data) for a total of 930 seizures and nine seizure types. Algorithm 1 detected eight of nine seizure types better than chance (area under the receiver operating characteristic curve [AUC-ROC] = .648-.976). Algorithm 2 detected all nine seizure types better than chance (AUC-ROC = .642-.995); a fusion of ACC and BVP modalities achieved the best AUC-ROC (.752) when combining all seizure types together.

SIGNIFICANCE

Automatic seizure detection using ML from multimodal wearable sensor data is feasible across a broad spectrum of epileptic seizures. Preliminary results show better than chance seizure detection. The next steps include validation of our results in larger datasets, evaluation of the detection utility tool for additional clinical seizure types, and integration of additional clinical information.

Automated Pain Assessment in Children using Electrodermal Activity and Video Data Fusion via Machine Learning

Pain assessment in children continues to challenge clinicians and researchers, as subjective experiences of pain require inference through observable behaviors, both involuntary and deliberate. The presented approach supplements the subjective self-report-based method by fusing electrodermal activity (EDA) recordings with video facial expressions to develop an objective pain assessment metric. Such an approach is specifically important for assessing pain in children who are not capable of providing accurate self-pain reports, requiring nonverbal pain assessment. We demonstrate the performance of our approach using data recorded from children in post-operative recovery following laparoscopic appendectomy. We examined separately and combined the usefulness of EDA and video facial expression data as predictors of childrens self-reports of pain following surgery through recovery. Findings indicate that EDA and facial expression data independently provide above chance sensitivities and specificities, but their fusion for classifying clinically significant pain vs. clinically nonsignificant pain achieved substantial improvement, yielding 90.91% accuracy, with 100% sensitivity and 81.82% specificity. The multimodal measures capitalize upon different features of the complex pain response. Thus, this paper presents both evidence for the utility of a weighted maximum likelihood algorithm as a novel feature selection method for EDA and video facial expression data and an accurate and objective automated classification algorithm capable of discriminating clinically significant pain from clinically nonsignificant pain in children.

Correlation Analysis of Different Measurement Places of Galvanic Skin Response in Test Groups Facing Pleasant and Unpleasant Stimuli

The galvanic skin response (GSR; also widely known as electrodermal activity (EDA)) is a signal for stress-related studies. Given the sparsity of studies related to the GSR and the variety of devices, this study was conducted at the Human Health Activity Laboratory (H2AL) with 17 healthy subjects to determine the variability in the detection of changes in the galvanic skin response among a test group with heterogeneous respondents facing pleasant and unpleasant stimuli, correlating the GSR biosignals measured from different body sites. We experimented with the right and left wrist, left fingers, the inner side of the right foot using Shimmer3GSR and Empatica E4 sensors. The results indicated the most promising homogeneous places for measuring the GSR, namely, the left fingers and right foot. The results also suggested that due to a significantly strong correlation among the inner side of the right foot and the left fingers, as well as the moderate correlations with the right and left wrists, the foot may be a suitable place to homogenously measure a GSR signal in a test group. We also discuss some possible causes of weak and negative correlations from anomalies detected in the raw data possibly related to the sensors or the test group, which may be considered to develop robust emotion detection systems based on GRS biosignals.

Youth engagement during making: using electrodermal activity data and first-person video to generate evidence-based conjectures

Purpose

This paper aims to introduce and explores the use of electrodermal activity (EDA) data as a tool for obtaining data about youth engagement during maker learning activities.

Design/methodology/approach

EDA and survey data were collected from a yearlong afterschool maker program for teens that met weekly and was hosted at a children’s museum. Data from four youth who were simultaneously present for eight weeks were examined to ascertain what experiences and activities were more or less engaging for them, based on psychophysiological measures.

Findings

Most of the focal youth appeared to show higher levels of engagement by survey measures throughout the program. However, when examined by smaller time intervals, certain activities appeared to be more engaging. Planning of maker activities was one space where engagement was higher. Completing sewing projects with minimal social interaction appeared to be less engaging. Specific activities involving common maker technologies yielded mixed levels of engagement.

Originality/value

Some research is emerging that uses EDA data as a basis for generating inferences about various states while participating in maker learning activities. This paper provides a novel analysis building on some techniques established in the still emergent body of prior research in this area.

Framework for selecting and benchmarking mobile devices in psychophysiological research

Commercially available consumer electronics in (smartwatches and wearable biosensors) are increasingly enabling acquisition of peripheral physiological and physical activity data inside and outside of laboratory settings. However, there is scant literature available for selecting and assessing the suitability of these novel devices for scientific use. To overcome this limitation, the current paper offers a framework to aid researchers in choosing and evaluating wearable technologies for use in empirical research. Our seven-step framework includes: (1) identifying signals of interest; (2) characterizing intended use cases; (3) identifying study-specific pragmatic needs; (4) selecting devices for evaluation; (5) establishing an assessment procedure; (6) performing qualitative and quantitative analyses on resulting data; and, if desired, (7) conducting power analyses to determine sample size needed to more rigorously compare performance across devices. We illustrate the application of the framework by comparing electrodermal, cardiovascular, and accelerometry data from a variety of commercial wireless sensors (Affectiva Q, Empatica E3, Empatica E4, Actiwave Cardio, Shimmer) relative to a well-validated, wired MindWare laboratory system. Our evaluations are performed in two studies (N = 10, N = 11) involving psychometrically sound, standardized tasks that include physical activity and affect induction. After applying our framework to this data, we conclude that only some commercially available consumer devices for physiological measurement are capable of wirelessly measuring peripheral physiological and physical activity data of sufficient quality for scientific use cases. Thus, the framework appears to be beneficial at suggesting steps for conducting more systematic, transparent, and rigorous evaluations of mobile physiological devices prior to deployment in studies.