Skin Conductance Research Articles

Effects of labyrinth-like path designs on mitigating stress response to traffic noise

Traffic noise poses significant mental health risks primarily through stress responses. Although labyrinths have been proven to reduce stress and promote relaxation, their efficacy in mitigating stress induced by traffic noise remains unclear. To address this issue, this study conducted repeated-measures experiments to investigate the effects of different labyrinth-like path designs on stress responses to traffic noise. Heart rate variability and electrodermal activity were measured using the E4 wristband to evaluate participants’ physiological stress levels. The results suggest that: (1) Walking on a continuous curved path layout (CS) effectively mitigated stress responses to traffic noise, as indicated by multiple physiological stress measures; (2) Regarding stress-mitigating path continuity attributes, 180-degree turns were positive attributes (p < 0.05), while 90-degree turns were negative attributes (p < 0.01); (3) Regarding stress-mitigating path layout features, curved segments (p < 0.01) and switchbacks (p < 0.05) were positive features, while U shapes were negative features (p < 0.01). This study indicates that labyrinth-like path designs can effectively mitigate stress responses to traffic noise; however, complex path layouts with frequent directional changes should be avoided in noisy environments, particularly in areas lacking resources for sound barriers and noise-absorbing vegetation. Our findings contribute to the development of evidence-based guidelines for designing stress-mitigating streets in high-traffic environments. When mainstreaming these guidelines through public codes, further comprehensive analyses including feasibility assessments, economic evaluations, and community engagement are warranted to ensure practical and sustainable implementation.

Exploration of stress reactivity and fear conditioning on intrusive memory frequency in a conditioned-intrusion paradigm

Background and objectivesThe conditioned-intrusion paradigm was designed to provide insight into the relationship between fear conditioning and intrusive memory formation, which is relevant to understanding posttraumatic stress disorder symptoms and treatment. However, boundary conditions of this new paradigm have not been explored and it is currently not known whether findings from this work are valid in a clinical context. MethodsIn the current study, we explored the relationship between stress reactivity to trauma film clips, usual exposure to violent media, renewal of fear conditioning using skin conductance as well as subjective ratings, and the effect of shock versus film clip during conditioning on the frequency of intrusive memories. An adapted fear conditioning paradigm using trauma clips as unconditional stimuli was used, and participants subsequently reported intrusive memories of the trauma clips. ResultsSkin conductance responses to conditioned stimuli paired with shocks and film clips were significantly higher than conditioned stimuli paired with film clips alone. Subjective stress reactivity, previous exposure to violent media, and film valence rating were associated with the frequency of intrusive memories. No aspects of fear conditioning were associated with intrusive memories, and factor analysis suggested the fear conditioning and stress related to film clip viewing were mostly separate constructs. Similarly, content and triggers of intrusive memories were usually film-clip related rather than conditional stimulus related. LimitationsWe did not observe strong conditioning effects of the unconditional stimuli to conditional stimuli, which were shapes rather than high frequency stimuli such as faces. ConclusionsThese findings provide potential boundary conditions for this paradigm and suggest multiple ways in which the validity of the paradigm can be tested in the future.

Advancing the integration of biosignal-based automated pain assessment methods into a comprehensive model for addressing cancer pain

BackgroundTailoring effective strategies for cancer pain management requires a careful analysis of multiple factors that influence pain phenomena and, ultimately, guide the therapy. While there is a wealth of research on automatic pain assessment (APA), its integration with clinical data remains inadequately explored. This study aimed to address the potential correlations between subjective and APA-derived objectives variables in a cohort of cancer patients.MethodsA multidimensional statistical approach was employed. Demographic, clinical, and pain-related variables were examined. Objective measures included electrodermal activity (EDA) and electrocardiogram (ECG) signals. Sensitivity analysis, multiple factorial analysis (MFA), hierarchical clustering on principal components (HCPC), and multivariable regression were used for data analysis.ResultsThe study analyzed data from 64 cancer patients. MFA revealed correlations between pain intensity, type, Eastern Cooperative Oncology Group Performance status (ECOG), opioids, and metastases. Clustering identified three distinct patient groups based on pain characteristics, treatments, and ECOG. Multivariable regression analysis showed associations between pain intensity, ECOG, type of breakthrough cancer pain, and opioid dosages. The analyses failed to find a correlation between subjective and objective pain variables.ConclusionsThe reported pain perception is unrelated to the objective variables of APA. An in-depth investigation of APA is required to understand the variables to be studied, the operational modalities, and above all, strategies for appropriate integration with data obtained from self-reporting.Trial registrationThis study is registered with ClinicalTrials.gov, number (NCT04726228), registered 27 January 2021, https://classic.clinicaltrials.gov/ct2/show/NCT04726228?term=nct04726228&draw=2&rank=1

EDA-Graph: Graph Signal Processing of Electrodermal Activity for Emotional States Detection.

The continuous detection of emotional states has many applications in mental health, marketing, human-computer interaction, and assistive robotics. Electrodermal activity (EDA), a signal modulated by sympathetic nervous system activity, provides continuous insight into emotional states. However, EDA possesses intricate nonstationary and nonlinear characteristics, making the extraction of emotion-relevant information challenging. We propose a novel graph signal processing (GSP) approach to model EDA signals as graphical networks, termed EDA-graph. The GSP leverages graph theory concepts to capture complex relationships in time-series data. To test the usefulness of EDA-graphs to detect emotions, we processed EDA recordings from the CASE emotion dataset using GSP by quantizing and linking values based on the Euclidean distance between the nearest neighbors. From these EDA-graphs, we computed the features of graph analysis, including total load centrality (TLC), total harmonic centrality (THC), number of cliques (GNC), diameter, and graph radius, and compared those features with features obtained using traditional EDA processing techniques. EDA-graph features encompassing TLC, THC, GNC, diameter, and radius demonstrated significant differences (p < 0.05) between five emotional states (Neutral, Amused, Bored, Relaxed, and Scared). Using machine learning models for classifying emotional states evaluated using leave-one-subject-out cross-validation, we achieved a five-class F1 score of up to 0.68.

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.

Identifying digital biomarkers of illness activity and treatment response in bipolar disorder with a novel wearable device (TIMEBASE): protocol for a pragmatic observational clinical study.

Bipolar disorder is highly prevalent and consists of biphasic recurrent mood episodes of mania and depression, which translate into altered mood, sleep and activity alongside their physiological expressions. The IdenTifying dIgital bioMarkers of illnEss activity and treatment response in BipolAr diSordEr with a novel wearable device (TIMEBASE) project aims to identify digital biomarkers of illness activity and treatment response in bipolar disorder. We designed a longitudinal observational study including 84 individuals. Group A comprises people with acute episode of mania (n = 12), depression (n = 12 with bipolar disorder and n = 12 with major depressive disorder (MDD)) and bipolar disorder with mixed features (n = 12). Physiological data will be recorded during 48 h with a research-grade wearable (Empatica E4) across four consecutive time points (acute, response, remission and episode recovery). Group B comprises 12 people with euthymic bipolar disorder and 12 with MDD, and group C comprises 12 healthy controls who will be recorded cross-sectionally. Psychopathological symptoms, disease severity, functioning and physical activity will be assessed with standardised psychometric scales. Physiological data will include acceleration, temperature, blood volume pulse, heart rate and electrodermal activity. Machine learning models will be developed to link physiological data to illness activity and treatment response. Generalisation performance will be tested in data from unseen patients. Recruitment is ongoing. This project should contribute to understanding the pathophysiology of affective disorders. The potential digital biomarkers of illness activity and treatment response in bipolar disorder could be implemented in a real-world clinical setting for clinical monitoring and identification of prodromal symptoms. This would allow early intervention and prevention of affective relapses, as well as personalisation of treatment.

A Research on Emotion Recognition of the Elderly Based on Transformer and Physiological Signals

Aiming at problems such as the difficulty of recognizing emotions in the elderly and the inability of traditional machine-learning models to effectively capture the nonlinear relationship between physiological signal data, a Recursive Map (RM) combined with a Vision Transformer (ViT) is proposed to recognize the emotions of the elderly based on Electroencephalogram (EEG), Electrodermal Activity (EDA), and Heart Rate Variability (HRV) signals. The Dung Beetle Optimizer (DBO) is used to optimize the variational modal decomposition of EEG, EDA, and HRV signals. The optimized decomposed time series signals are converted into two-dimensional images using RM, and then the converted image signals are applied to the ViT for the study of emotion recognition of the elderly. The pre-trained weights of ViT on the ImageNet-22k dataset are loaded into the model and retrained with the two-dimensional image data. The model is validated and compared using the test set. The research results show that the recognition accuracy of the proposed method on EEG, EDA, and HRV signals is 99.35%, 86.96%, and 97.20%, respectively. This indicates that EEG signals can better reflect the emotional problems of the elderly, followed by HRV signals, while EDA signals have poorer effects. Compared with Support Vector Machine (SVM), Naive Bayes (NB), and K-Nearest Neighbors (KNN), the recognition accuracy of the proposed method is increased by at least 9.4%, 11.13%, and 12.61%, respectively. Compared with ResNet34, EfficientNet-B0, and VGG16, it is increased by at least 1.14%, 0.54%, and 3.34%, respectively. This proves the superiority of the proposed method in emotion recognition for the elderly.

Predicting the severity of mood and neuropsychiatric symptoms from digital biomarkers using wearable physiological data and deep learning

Neuropsychiatric symptoms (NPS) and mood disorders are common in individuals with mild cognitive impairment (MCI) and increase the risk of progression to dementia. Wearable devices collecting physiological and behavioral data can help in remote, passive, and continuous monitoring of moods and NPS, overcoming limitations and inconveniences of current assessment methods. In this longitudinal study, we examined the predictive ability of digital biomarkers based on sensor data from a wrist-worn wearable to determine the severity of NPS and mood disorders on a daily basis in older adults with predominant MCI. In addition to conventional physiological biomarkers, such as heart rate variability and skin conductance levels, we leveraged deep-learning features derived from physiological data using a self-supervised convolutional autoencoder. Models combining common digital biomarkers and deep features predicted depression severity scores with a correlation of r = 0.73 on average, total severity of mood disorder symptoms with r = 0.67, and mild behavioral impairment scores with r = 0.69 in the study population. Our findings demonstrated the potential of physiological biomarkers collected from wearables and deep learning methods to be used for the continuous and unobtrusive assessments of mental health symptoms in older adults, including those with MCI. Trial registrationThis trial was registered with ClinicalTrials.gov (NCT05059353) on September 28, 2021, titled “Effectiveness and Safety of a Digitally Based Multidomain Intervention for Mild Cognitive Impairment”.

Impact of trauma type on neural mechanisms of threat conditioning and its extinction

Trauma type moderates the impact of trauma exposure on clinical symptomatology; however, the impact of trauma type on the neural correlates of emotion regulation is not as well understood. This study examines how violent and nonviolent trauma differentially influence the neural correlates of conditioned fear and extinction. We aggregated psychophysiological and fMRI data from three studies; we categorized reported trauma as violent or nonviolent, and subdivided violent trauma as sexual or nonsexual. We examined skin conductance responses (SCR) during a fear conditioning and extinction paradigm. For fMRI data analyses, we conducted region-specific and whole-brain analyses. We examined associations between beta weights from specific brain regions and CAPS scores. The group exposed to violent trauma showed significantly higher SCR during extinction recall. Those exposed to nonviolent trauma showed significantly higher functional activation during late extinction learning. The group exposed to violent trauma showed higher functional connectivity within the default mode network (DMN) and between the DMN and frontoparietal control network. For secondary analyses of sexual vs nonsexual trauma, we did not observe any between-group differences in SCR. During late extinction learning, the group exposed to sexual trauma showed significantly higher activation in the prefrontal cortex and precuneus. During extinction recall, the group exposed to nonsexual trauma showed significantly higher activation in the insular cortex. Violent trauma significantly impacts functional brain activations and connectivity in brain areas important for perception and attention with no significant impact on brain areas that modulate emotion regulation. Sexual trauma impacts brain areas important for internal perception.

A GAI-based multi-scale convolution and attention mechanism model for music emotion recognition and recommendation from physiological data

Due to the subjectivity of emotions and the limited number of emotion categories, existing deep learning models require assistance to achieve objective, accurate, and flexible personalized music emotion recommendations. This paper introduces a deep learning approach that combines Generative Artificial Intelligence (GAI) and explicitly leverages physiological indicators to enhance the model's intelligence, versatility, and automation. Physiological indicators such as Heart Rate Variability (HRV) and Galvanic Skin Response (GSR) can be measured using sensors placed on the body's surface, providing more precise information about human emotional changes. This research employs a three-dimensional emotion model, including the tension-arousal axis, energy-arousal axis, and valence axis, to explain the correlation and accuracy between music data and emotions. Based on this, a music emotion classifier is designed, incorporating GAI algorithms to recommend music by matching users' physiological and emotional types with the emotional features of music. The classifier uses Mel-Frequency Cepstral Coefficients (MFCC) to transform audio into Mel-spectrogram as input features. The music emotion selection module adopts a GAI framework of Variational Autoencoder (VAE) and integrates multi-scale parallel convolution and attention mechanism modules. Experimental results demonstrate that this approach is competitive compared to existing deep learning architectures on PMEmo, RAVDESS, and Soundtrack datasets. Furthermore, due to GAI's efficient classification capability, this model is suitable for resource-constrained mobile devices and other smart devices. The results of this study can be applied to emotion-based music recommendation systems, contributing to emotional interventions and improving the performance of exercise and music therapy.

Use of Galvanic Skin Response in a stated preference survey to assess factors influencing college student use of transit

ABSTRACT The COVID-19 pandemic significantly impacted all forms of travel. Transit ridership decreased drastically and has rebounded slowly compared to highway travel in the United States. This research explored a unique method to gain insight into the reasons behind the slow return to transit. Students were surveyed regarding their use of transit before, during, and after the pandemic. The study was conducted in a lab with a Galvanic Skin Response device, a computer with a webcam, and software to measure both recorded and self-reported stress levels in students as they answered questions regarding their transit use, stress levels, and fear of COVID-19. The Fear of COVID-19 and Perceived Stress Scale showed a significant influence on the change in transit use for a small proportion of students. The proposed methodology showed that these psychological scales and collecting GSR data may be valuable in predicting a traveler’s return to transit.

The Impact of Continuous and Partial Reinforcement on the Acquisition and Generalization of Human-Conditioned Fear.

Fear over-generalization as a core symptom of anxiety disorders is manifested by fear responses even to safe stimuli that are very dissimilar to the original dangerous stimulus. The present study investigated the effects of two separate conditioned stimuli-unconditioned stimuli (CS-US) pairing procedures on fear acquisition and generalization using a perceptual discrimination fear-conditioning paradigm, with US expectancy ratings and skin conductance response (SCR) as indicators. One group accepted continuous followed by partial CS-US pairings (C-P group); the other group accepted partial followed by continuous CS-US pairings (P-C group). It was found that compared to the P-C group, the C-P group showed stronger perceptual discrimination of CS+ and CS- in the fear acquisition and showed weaker SCRs and stronger extinction of US expectancy in the generalization. These findings emphasize that CS-US pairings significantly influence fear acquisition and generalization and suggest that continuous-following partial CS-US pairings promote individual discrimination of threat and safety signals and inhibit the generalization of conditioned fear. The results of this study have implications for clinical interventions for patients experiencing negative events.

Evidence That Pervasive Body Gaze Behavior in Heterosexual Men Is a Social Marker for Implicit, Physiological, and Explicit Sexual Assault Propensities.

Deliberate and effortful attempts to gaze at the bodies of women is emerging as a valuable marker of sexual objectification in men. Some preliminary evidence suggests that pervasive body gaze behavior may also accompany insidious attitudes which can facilitate sexual assault. The present study aimed to further explore this potential by examining pervasive body gaze associations with explicit, implicit, and physiological sexual assault propensity measures. We presented 110 heterosexual male participants with images of fully and partially dressed women with and without injuries while measuring their skin conductance responses. We also captured implicit and explicit sexual assault measures in addition to self-reported pervasive body gaze behavior. Pervasive body gaze behavior was significantly correlated with rape myth acceptance attitudes, prior perpetration of sexual assault, a stronger implicit association between erotica and aggression, and lower physiological reactivity during exposure to partially dressed injured women. These findings suggest that body gaze towards women could be a behavioral marker for inclinations to victim blame, preferences for rough sexual conduct, and a physiological desensitization towards female victims. This study further validates a five item self-reported body gaze measure as a valuable tool for detecting deviant sexual objectification attitudes and affective states. As such, measurement and observation of body gaze behavior could be useful for developing risk assessments, estimating intervention efficacy, and enhancing public awareness.

Lunch melodies: Investigating the impact of music on emotions, hunger, liking, and psychophysiology while viewing a lunch meal

Sensory cues like music can influence our behaviour towards food. In the present study, the effect of music on hunger, fullness, desire to eat and liking of foods, while viewing real lunch food items, was investigated. To this end, emotions and physiological measures were obtained to understand the changes in hunger, fullness, desire to eat and liking. The study aimed to examine changes in hunger, fullness, desire to eat, and liking when viewing a lunch meal under silent and varying music conditions. Additionally, the study explored the potential role of emotions to explain these changes. A crossover experimental design was employed using 50 participants (17 males and 33 females) who observed lunch food items during a silent condition (control), or while listening to either liked or disliked music. The findings demonstrate the cross-modal influence of music on hunger and food liking ratings when viewing food. Hunger ratings were higher and more negative emotions were evoked while viewing lunch food items and listening to disliked music. In contrast, in the silent and liked music conditions, which elicited more positive emotions, there were increased ratings of healthy and unhealthy food pleasantness, overall food liking, and food satisfaction. Electrophysiological measures of heart rate (HR) and skin conductance (SC) were obtained while listening to music and viewing a lunch meal. Viewing food items while listening to disliked music evoked negative emotions and significantly increased SC compared to liked music or silent conditions. Viewing the food items under the silent condition evoked positive emotions and significantly increased HR compared to listening to liked and disliked music. This study showed that the participants’ emotions, hunger level, liking, and electrophysiological responses when viewing food are influenced by music that varied with liking. Results from this study may assist in enhancing dining experiences, as well as influencing food choices and satisfaction with meals.

Acquiring musical knowledge increases music liking: Evidence from a neurophysiological study.

People possessing musical knowledge tend to enjoy music more, but the linkage remains to be determined. Based on the shared affective motion experience model for music appreciation, we hypothesized that acquiring musical knowledge about the music itself, for example, an analytical understanding of music elements and the related emotional expressions, would increase music liking. To test the hypothesis, we asked 48 participants to learn analytical or historical information about a piece of music by watching a pre-recorded teaching video. Learners' physiological responses, such as skin conductance and heart rate, were recorded during learning. The increase of music liking was observed after both types of knowledge acquisition, but more so for analytical knowledge. Notably, acquiring analytical knowledge made learners' skin conductance more similar, indicating the alignment of physiological responses. This physiological similarity, correlated with analytical knowledge similarity, could mediate the effect of knowledge acquisition on music liking. In sum, this study reveals the impact of analytical knowledge on music enjoyment and the associated neurophysiological mechanism. It extends the theoretical framework of shared affective motion experience to explain how musical knowledge influences music appreciation.

Physiological audience synchrony in classical concerts linked with listeners’ experiences and attitudes

A series of eleven public concerts (staging chamber music by Ludwig van Beethoven, Brett Dean, Johannes Brahms) was organized with the goal to analyze physiological synchronies within the audiences and associations of synchrony with psychological variables. We hypothesized that the music would induce synchronized physiology, which would be linked to participants’ aesthetic experiences, affect, and personality traits. Physiological measures (cardiac, electrodermal, respiration) of 695 participants were recorded during presentations. Before and after concerts, questionnaires provided self-report scales and standardized measures of participants’ affectivity, personality traits, aesthetic experiences and listening modes. Synchrony was computed by a cross-correlational algorithm to obtain, for each participant and physiological variable (heart rate, heart-rate variability, respiration rate, respiration, skin-conductance response), how much each individual participant contributed to overall audience synchrony. In hierarchical models, such synchrony contribution was used as the dependent and the various self-report scales as predictor variables. We found that physiology throughout audiences was significantly synchronized, as expected with the exception of breathing behavior. There were links between synchrony and affectivity. Personality moderated the synchrony levels: Openness was positively associated, Extraversion and Neuroticism negatively. Several factors of experiences and listening modes predicted synchrony. Emotional listening was associated with reduced, whereas both structual and sound-focused listening was associated with increased synchrony. We concluded with an updated, nuanced understanding of synchrony on the timescale of whole concerts, inviting elaboration by synchony studies on shorter timescales of music passages.

Engaging students of color in physiological computing with insights from eye-tracking

Computing involving physiological data such as heart rate and electrodermal activity has been used as a way to enrich K-12 students’ computing learning experiences. This study explored a novel way to engage elementary students of color as “developers” through a series of physiological computing lessons during a summer learning program. Learners used real-time physiological data (i.e. muscle energy) as computer inputs to build computer programs. The study used an explanatory mixed method by combining multi-dimensional eye-tracking metrics data with qualitative investigation to reveal the visual attention of students with different programming proficiency levels. The regression analysis revealed three statistically significant predictors of students’ programming performances. Subsequent qualitative case studies provided additional insights into students’ problem-solving processes. Interpretations and caveats are presented and discussed.

The effect of music on stress recovery

ObjectivePrevious research has suggested beneficial effects of music in reducing stress levels. However, there is no consistent conclusion demonstrating that music can contribute to stress recovery, primarily due to limitations in stress measurement, and inconsistent methodology within existing studies. Our study explores whether relaxing music, especially when self-selected, outperforms non-music acoustic, and silence conditions, fostering both subjective and biological stress recovery. MethodsOne hundred and five healthy female participants underwent the Trier Social Stress Test (TSST) before being randomly allocated to one of four conditions: condition 1 (n = 25) listened to researcher-selected relaxing music; condition 2 (n = 27) listened to self-selected relaxing music; condition 3 (n = 26) listened to the sound of rippling water; and condition 4 (n = 27) remained in silence. Stress parameters were repeatedly measured nine times before and after the TSST. Saliva samples were collected for cortisol and saliva alpha-amylase (sAA) analysis, Movisens equipment was used to measure heart rate (HR) and skin conductance levels (SCL), and Visual Analogue Scales (VAS) were used for subjective stress measurement. ResultsThe examination of SCL, and VAS scores revealed no significant changes following the four relaxation interventions. Although sAA displayed a significant main effect of condition, post hoc tests did not pinpoint specific differences. HR recovery patterns varied among the four relaxation interventions, with the sound of rippling water condition exhibiting a later significant decrease compared to the other conditions. Exploratory analyses revealed that cortisol levels continued increase in all conditions during intervention phase except the researcher-selected music condition. ConclusionsThe subjective and biological stress markers did not exhibit better recovery after the music stimulus, except for a tendency in the researcher-selected music condition to mitigate the continued increase in cortisol levels after the stress test. Our study provides the first evidence comparing the impact of researcher- and participant-selected music with silence and a non-music acoustic stimulus, on both subjective and biological stress recovery. Our findings contribute to a more nuanced understanding of the impact of music on stress recovery.

Sensing, feeling and regulating: investigating the association of focal braindamage with voluntary respiratoryand motor control.

Breathing is a complex, vital function that can be modulated to influence physical and mental well-being. However, the role of cortical and subcortical brain regions in voluntary control of human respiration is underexplored. Here we investigated the influence of damage to human frontal, temporal or limbic regions on the sensation and regulation of breathing patterns. Participants performed a respiratory regulation task across regular and irregular frequencies ranging from 6 to 60 breaths per minute (bpm), with a counterbalanced hand motor control task. Interoceptive and affective states induced by each condition were assessed via questionnaire, and autonomic signals were indexed via skin conductance. Participants with focal lesions to the bilateral frontal lobe, right insula/basal ganglia and left medial temporal lobe showed reduced performance relative to individually matched healthy comparisons during the breathing and motor tasks. They also reported significantly higher anxiety during the 60bpm regular and irregular breathing trials, with anxiety correlating with difficulty in rapid breathing specifically within this group. This study demonstrates that damage to frontal, temporal or limbic regions is associated with abnormal voluntary respiratory and motor regulation and tachypnoea-related anxiety, highlighting the role of the forebrain in affective and motor responses during breathing. This article is part of the theme issue 'Sensing and feeling: an integrative approach to sensory processing and emotional experience'.

Reliability Assessment and Multi-Physics Simulation of Additively Printed Wearable Humidity Sensor with Super Capacitive Material for Astronaut in Extreme Condition

Abstract Additive technologies, such as aerosol jet printing, direct write printing are increasingly being used in the production of printed circuit boards because they eliminate the need for costly tooling, such as photomasks or etching containers. This is because additive methods allow for the direct deposition of printing materials onto a substrate. This versatility in a broad variety of applications allows engineers to create diverse applications, such as sensing devices with ECG sensors, pulse-oxygen sensors, galvanic skin response sensors, body temperature sensors, humidity sensors, and so on. Due to its potential for adaptability and integration, the development of additively printed humidity sensors has been the subject of several prior investigations. There are still issues with the reliability of current humidity sensor technology when flexing force is coupled with the humidity sensor. For the avoidance of stability issues, it is required to develop a better printing technique, process recipe, and sensing material encapsulation. In this research, the direct-write (D-write) printing approach with a nScrypt printer was employed to print the humidity sensor as a test vehicle in a laboratory setting. The sensor was characterized by analyzing the print recipe and its interaction with humidity in regards to resistance and humidity sensitivity. Additionally, the characterization of sensor accuracy, hysteresis, linearity, and stability in relation to temperature and humidity variation has been measured. Furthermore, a multi-physics simulation model was created in order to comprehend the electrochemical processes that occur when the humidity sensor is exposed to a very humid environment.