Electrodermal Activity (EDA) Morphologies and Prediction of Engagement with Simple Moving Average Crossover: A Mixed-Method Study.

Electrodermal Activity (EDA), which primarily indicates arousal through sympathetic nervous system activity, serves as a tool to measure constructs like engagement, cognitive load, performance, and stress. Despite its potential, empirical studies have often yielded mixed results and found it of limited use. To better understand EDA, we conducted a mixed-methods study in which quantitative EDA profiles and survey data were investigated using qualitative interviews. This study furnishes an EDA dataset measuring the engagement levels of seven participants who watched three videos for 4-10 min. The subsequent interviews revealed five EDA morphologies with varying short-term signatures and long-term trends. We used this dataset to demonstrate the moving average crossover, a novel metric for EDA analysis, in predicting engagement-disengagement dynamics in such data. Our contributions include the creation of the detailed dataset, comprising EDA profiles annotated with qualitative data, the identification of five distinct EDA morphologies, and the proposition of the moving average crossover as an indicator of the beginning of engagement or disengagement in an individual.

Robots as Mental Health Coaches: A Study of Emotional Responses to Technology-Assisted Stress Management Tasks Using Physiological Signals.

The current study investigated the effectiveness of social robots in facilitating stress management interventions for university students by evaluating their physiological responses. We collected electroencephalogram (EEG) brain activity and Galvanic Skin Responses (GSRs) together with self-reported questionnaires from two groups of students who practiced a deep breathing exercise either with a social robot or a laptop. From GSR signals, we obtained the change in participants' arousal level throughout the intervention, and from the EEG signals, we extracted the change in their emotional valence using the neurometric of Frontal Alpha Asymmetry (FAA). While subjective perceptions of stress and user experience did not differ significantly between the two groups, the physiological signals revealed differences in their emotional responses as evaluated by the arousal-valence model. The Laptop group tended to show a decrease in arousal level which, in some cases, was accompanied by negative valence indicative of boredom or lack of interest. On the other hand, the Robot group displayed two patterns; some demonstrated a decrease in arousal with positive valence indicative of calmness and relaxation, and others showed an increase in arousal together with positive valence interpreted as excitement. These findings provide interesting insights into the impact of social robots as mental well-being coaches on students' emotions particularly in the presence of the novelty effect. Additionally, they provide evidence for the efficacy of physiological signals as an objective and reliable measure of user experience in HRI settings.

One-Channel Wearable Mental Stress State Monitoring System.

Assessments of stress can be performed using physiological signals, such as electroencephalograms (EEGs) and galvanic skin response (GSR). Commercialized systems that are used to detect stress with EEGs require a controlled environment with many channels, which prohibits their daily use. Fortunately, there is a rise in the utilization of wearable devices for stress monitoring, offering more flexibility. In this paper, we developed a wearable monitoring system that integrates both EEGs and GSR. The novelty of our proposed device is that it only requires one channel to acquire both physiological signals. Through sensor fusion, we achieved an improved accuracy, lower cost, and improved ease of use. We tested the proposed system experimentally on twenty human subjects. We estimated the power spectrum of the EEG signals and utilized five machine learning classifiers to differentiate between two levels of mental stress. Furthermore, we investigated the optimum electrode location on the scalp when using only one channel. Our results demonstrate the system's capability to classify two levels of mental stress with a maximum accuracy of 70.3% when using EEGs alone and 84.6% when using fused EEG and GSR data. This paper shows that stress detection is reliable using only one channel on the prefrontal and ventrolateral prefrontal regions of the brain.

Cognition-Emotion Interaction during L2 Sentence Comprehension: The Correlation of ERP and GSR Responses to Sense Combinations.

This study mainly examined the role of the combination of three senses (i.e., auditory, visual, and tactile) and five senses (i.e., auditory, visual, tactile, olfactory, and gustatory) in the correlation between electrophysiological and electrodermal responses underlying second language (L2) sentence comprehension. Forty subjects did two acceptability judgment tasks, encompassing congruent and semantically/pragmatically incongruent sentences. The event-related potential (ERP) and galvanic skin response (GSR) data for both the target and final words of the sentences were collected and analyzed. The results revealed that there is an interaction between cognitive and emotional responses in both semantically and pragmatically incongruent sentences, yet the timing of the interaction is longer in sentences with pragmatic incongruity due to their complexity. Based on the ERP and GSR correlation results, it was further found that the five-sense combination approach improves L2 sentence comprehension and interest in learning materials yet reduces the level of excitement or arousal. While this approach might be beneficial for some learners, it might be detrimental for those in favor of stimulating learning environments.

A Study on the correlation of the physico-chemical properties of Gunshot Residue (GSR) particles with distance using a 9×19 mm Indian ammunition.

The trio elements found in Gunshot Residue (GSR) are considered the key elements that are characteristic of GSR. To date, most forensic laboratories have mainly concentrated on employing carbon stubs analyzed by Scanning Electron Microscopy (SEM) coupled with Energy Dispersive Spectroscopy (EDS) to find IGSR on the hands and clothing of a person. A little elevated from the normal practice, this work is focused on the evaluation of compositional and morphological variations of GSR collected from muzzle end, trajectory, and target obtained by firing the ammunition of choice (9×19 mm Indian ammunition). Even though there may be variations in IGSR compositions within various locations of a weapon, this hasn't been investigated or documented up to this point. To ascertain whether it is possible to identify any variation in GSR particles gathered from these three different locations, the objective of this study is to investigate the structural characteristics and elemental composition of GSR to identify the distinctive parameters that allow for comparison and to establish the composition of the primer. The study also focuses on assessing any possible surface modification that may occur to GSR upon striking the target and establishing a correlation between GSR particles and propellant powder. The collected GSR samples were analyzed using a digital microscope, SEM/EDS, and EDXRF. It was discovered that the primer type showed a strong correlation to the elemental composition and morphology of GSR. By analyzing the GSR particles collected from the various sites as mentioned above, it was possible to identify the primer mixture used in the ammunition and its diversity in elemental concentration. The obtained GSR samples were not spherical but showed an elongated structure and possessed a diameter ranging from 695.4 µm-1.640 mm, 536.2 µm-1.412 mm, and 775.8 µm-1.772 mm respectively. However, the morphology and the size distribution of the particles collected from all three different points showed slight deviation as moving from ME towards TG. The obtained results could identify the primer mixture and diversity in its elemental concentration. The morphology and size distribution of GSR collected from three different points showed deviations.

Beneficial Rhizobacterium Triggers Induced Systemic Resistance of Maize to Gibberella Stalk Rot via Calcium Signaling.

Gibberella stalk rot (GSR) caused by the fungus Fusarium graminearum is a devastating disease of maize (Zea mays L.), but we lack efficient methods to control this disease. Biological control agents, including beneficial microorganisms, can be used as an effective and eco-friendly approach to manage crop diseases. For example, Bacillus velezensis SQR9, a bacterial strain isolated from the rhizosphere of cucumber plants, promotes growth and suppresses diseases in several plant species. However, it is not known whether and how SQR9 affects maize resistance to GSR. In this study, we found that treatment with SQR9 increased maize resistance to GSR by activating maize induced systemic resistance (ISR). RNA-seq and quantitative reverse transcription-PCR analysis showed that phenylpropanoid biosynthesis, amino acid metabolism, and plant-pathogen interaction pathways were enriched in the root upon colonization by SQR9. Also, several genes associated with calcium signaling pathways were up-regulated by SQR9 treatment. However, the calcium signaling inhibitor LaCl3 weakened the SQR9-activated ISR. Our data suggest that the calcium signaling pathway contributes to maize GSR resistance via the activation of ISR induced by SQR9. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Uncertainty model for automated gunshot residue particle length measurements obtained from electron microscopy images.

Currently, the use of algorithms and computer vision systems for metrological purposes has increased in different areas of knowledge to reduce human error and process deviations, consequently increasing reliability and reducing measurement uncertainties. This study presents a model for estimating the uncertainty of Feret's diameter (DF ) measurements of scanning electron microscopy (SEM) images from regular and irregular gunshot residue (GSR) particles at different magnifications. The data were extracted using the automatic measurement algorithm developed by the Brazilian Institute of Metrology, Quality and Technology (Inmetro). The proposed uncertainty model was based on the recommendations of the guide to the expression of uncertainty in measurement (GUM). The gold standard technique to identify and detect GSR particles is the SEM coupled to energy dispersive X-ray spectroscopy (SEM/EDS), which was used in the study. The low uncertainty values obtained in this study are justified by the refinement of the measurements performed at each stage of digital image procedures. The proposed uncertainty model contributes in an innovative way to the metrological evaluation of regular and irregular GSR particles at different images magnifications. The correct morphometry definition of these particles allows to study their distinction from other possible sources of GSR and, above all, their correlation with the type of ammunition used when firing the firearm. These measurement uncertainty calculations can be applied to any object images acquired by SEM, which provides more confidence in the results of measurements of the object of interest.

Uninterrupted real-time cerebral stress level monitoring using wearable biosensors: A review.

Stress is the major unseen bug for the health of humans with the increasing workaholic era. Long periods of avoidance are the main precursor for chronic disorders that are quite tough to treat. As precaution is better than cure, stress detection and monitoring are vital. Although there are ways to measure stress clinically, there is still a constant need and demand for methods that measure stress personally and in an ex vitro manner for the convenience of the user. The concept of continuous stress monitoring has been introduced to tackle the issue of unseen stress accumulating in the body simultaneously with being user-friendly and reliable. Stress biosensors nowadays provide real-time, noninvasive, and continuous monitoring of stress. These biosensors are innovative anthropogenic creations that are a combination of biomarkers and indicators like heart rate variation, electrodermal activity, skin temperature, galvanic skin response, and electroencephalograph of stress in the body along with machine learning algorithms and techniques. The collaboration of biological markers, artificial intelligence techniques, and data science tools makes stress biosensors a hot topic for research. These attributes have made continuous stress detection a possibility with ease. The advancement in stress biosensing technologies has made a great impact on the lives of human beings so far. This article focuses on the comprehensive study of stress-indicating biomarkers and the techniques along with principles of the biosensors used for continuous stress detection. The precise overview of wearable stress monitoring systems is also sectioned to pave a pathway for possible future research studies.

Monitoring Inattention in Construction Workers Caused by Physical Fatigue Using Electrocardiograph (ECG) and Galvanic Skin Response (GSR) Sensors.

Physical fatigue is frequent for heavy manual laborers like construction workers, but it causes distraction and may lead to safety incidents. The purpose of this study is to develop predictive models for monitoring construction workers' inattention caused by physical fatigue utilizing electrocardiograph (ECG) and galvanic skin response (GSR) sensors. Thirty participants were invited to complete an attention-demanding task under non-fatigued and physically fatigued conditions. Supervised learning algorithms were utilized to develop models predicting their attentional states, with heart rate variability (HRV) features derived from ECG signals and skin electric activity features derived from GSR signals as data inputs. The results demonstrate that using HRV features alone could obtain a prediction accuracy of 88.33%, and using GSR features alone could achieve an accuracy of 76.67%, both through the KNN algorithm. The accuracy increased to 96.67% through the SVM algorithm when combining HRV and GSR features. The findings indicate that ECG sensors used alone or in combination with GSR sensors can be applied to monitor construction workers' inattention on job sites. The findings would provide an approach for detecting distracted workers at job sites. Additionally, it might reveal the relationships between workers' physiological features and attention.

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.

Observation of Magnetic Domains in Amorphous Magnetic Wires with a Diameter of 10 µm Used in GSR Sensors.

The core of a Gigahertz Spin Rotation (GSR) sensor, a compact and highly sensitive magnetic sensor, is composed of Co-Fe-based amorphous magnetic wire with a diameter of 10 µm. Observations of the magnetic domain structure showed that this magnetic wire has unusual magnetic noise characteristics. Bamboo-shaped magnetic domains a few hundred micrometers in width were observed to form inside the wire, and smaller domains a few micrometers across were observed to form inside these larger domains. The magnetic domain pattern changed abruptly when an external magnetic field was applied to the wire. Herein is shown how these changes may be a source of magnetic noise in the wire.

Behavior and Task Classification Using Wearable Sensor Data: A Study across Different Ages.

In this paper, we face the problem of task classification starting from physiological signals acquired using wearable sensors with experiments in a controlled environment, designed to consider two different age populations: young adults and older adults. Two different scenarios are considered. In the first one, subjects are involved in different cognitive load tasks, while in the second one, space varying conditions are considered, and subjects interact with the environment, changing the walking conditions and avoiding collision with obstacles. Here, we demonstrate that it is possible not only to define classifiers that rely on physiological signals to predict tasks that imply different cognitive loads, but it is also possible to classify both the population group age and the performed task. The whole workflow of data collection and analysis, starting from the experimental protocol, data acquisition, signal denoising, normalization with respect to subject variability, feature extraction and classification is described here. The dataset collected with the experiments together with the codes to extract the features of the physiological signals are made available for the research community.

Evaluation of physiological responses to mental workload in n-back and arithmetic tasks.

Working memory tasks, such as n-back and arithmetic tasks, are frequently used in studying mental workload. The present study investigated and compared the sensitivity of several physiological measures at three levels of difficulty of n-back and arithmetic tasks. The results showed significant differences in fixation duration and pupil diameter among three task difficulty levels for both n-back and arithmetic tasks. Pupil diameters increase with increasing mental workload, whereas fixation duration decreases. Blink duration and heart rate (HR) were significantly increased as task difficulty increased in the n-back task, while root mean square of successive differences (RMSSD) and standard deviation of R-R intervals (SDNN) were significantly decreased in the arithmetic task. On the other hand, blink rate and Galvanic Skin Response (GSR) were not sensitive enough to assess the differences in task difficulty for both tasks. All significant physiological measures yielded significant differences between low and high task difficulty except for SDNN.Practitioner summary: This study aimed to assess the sensitivity levels of several physiological measures of mental workload in n-back and arithmetic tasks. It showed that pupil diameter was the most sensitive in both tasks. This study also found that most physiological indices are sensitive to an extreme change in task difficulty levels.

Examining the relationship of personality traits with online teaching using emotive responses and physiological signals.

In the education sector, there is a rapid increase in using online teaching and learning scenarios. Making these scenarios more effective is the main purpose of this study. Though there are a lot of factors that affect it, however, the primary focus is to find out the relationship between a teacher's personality and their liking for online teaching. To conduct the study, a framework has been proposed which is a mixed design of self-reported (emotions and personality) data and physiological responses of a teacher. In self-reported data, along with teachers, learners' perception of a teacher's personality is also considered which explores their relationship with online teaching. The final results reveal that teachers with a high level of agreeableness, conscientiousness, and openness personality traits are more comfortable with online teaching as compared to extraversion and neuroticism traits. To validate the self-reported data analysis, the physiological responses of teachers were recorded that ensure the authenticity of the collected data. It also ensures that the physiological responses along with emotions are also good indicators of personality recognition.

On the intersection between data quality and dynamical modelling of large-scale fMRI signals.

Large-scale dynamics of the brain are routinely modelled using systems of nonlinear dynamical equations that describe the evolution of population-level activity, with distinct neural populations often coupled according to an empirically measured structural connectivity matrix. This modelling approach has been used to generate insights into the neural underpinnings of spontaneous brain dynamics, as recorded with techniques such as resting state functional MRI (fMRI). In fMRI, researchers have many degrees of freedom in the way that they can process the data and recent evidence indicates that the choice of pre-processing steps can have a major effect on empirical estimates of functional connectivity. However, the potential influence of such variations on modelling results are seldom considered. Here we show, using three popular whole-brain dynamical models, that different choices during fMRI preprocessing can dramatically affect model fits and interpretations of findings. Critically, we show that the ability of these models to accurately capture patterns in fMRI dynamics is mostly driven by the degree to which they fit global signals rather than interesting sources of coordinated neural dynamics. We show that widespread deflections can arise from simple global synchronisation. We introduce a simple two-parameter model that captures these fluctuations and performs just as well as more complex, multi-parameter biophysical models. From our combined analyses of data and simulations, we describe benchmarks to evaluate model fit and validity. Although most models are not resilient to denoising, we show that relaxing the approximation of homogeneous neural populations by more explicitly modelling inter-regional effective connectivity can improve model accuracy at the expense of increased model complexity. Our results suggest that many complex biophysical models may be fitting relatively trivial properties of the data, and underscore a need for tighter integration between data quality assurance and model development.

Genomic and Phenotypic Analyses Reveal Mechanisms Underlying Homing Ability in Pigeon.

The homing pigeon was selectively bred from the domestic pigeon for a homing ability over long distances, a very fascinating but complex behavioral trait. Here, we generate a total of 95 whole genomes from diverse pigeon breeds. Comparing the genomes from the homing pigeon population with those from other breeds identifies candidate positively selected genes, including many genes involved in the central nervous system, particularly spatial learning and memory such as LRP8. Expression profiling reveals many neuronal genes displaying differential expression in the hippocampus, which is the key organ for memory and navigation and exhibits significantly larger size in the homing pigeon. In addition, we uncover a candidate gene GSR (encoding glutathione-disulfide reductase) experiencing positive selection in the homing pigeon. Expression profiling finds that GSR is highly expressed in the wattle and visual pigment cell layer, and displays increased expression levels in the homing pigeon. In vitro, a magnetic field stimulates increases in calcium ion concentration in cells expressing pigeon GSR. These findings support the importance of the hippocampus (functioning in spatial memory and navigation) for homing ability, and the potential involvement of GSR in pigeon magnetoreception.

Molybdenum in Gunshot Residue: Experimental Evidences and Detection Challenges in the Presence of Lead and Sulfur.

Inorganic gunshot residue (GSR) analysis is carried out by scanning electron microscopy/energy-dispersive X-ray spectroscopy (SEM/EDS) in many forensic laboratories. Characteristic GSR often consists of lead­barium­antimony, commonly associated with sulfur. The strength of forensic GSR evidence increases when unusual elements are found in residues collected both from the suspect and from the discharged firearm. The presence of molybdenum in GSR, due to the use of MoS2 lubricants in firearms, is experimentally demonstrated here for the first time. The most intense molybdenum X-ray emissions are MoL peaks at 2.3 keV which overlap with PbM and SK families due to the poor resolution of EDS detectors. When Pb, S, and Mo are allegedly present in the same particle, the reliability of automatic EDS routines is at risk. Missing identifications or false detections and exclusions may then occur. Molybdenum should be considered as detected only if MoK emissions meet the peak-to-background ratio minimum requirements. A strategy to spot Mo-containing residues is described, based on the automated search of MoS2, using a new "Sulfur only" class added to the classification scheme, followed by careful manual review of all GSR particles at an acceleration voltage of 30 kV. Our proposal improves commonly adopted forensic procedures currently followed in casework.

The Sample Size Matters: To What Extent the Participant Reduction Affects the Outcomes of a Neuroscientific Research. A Case-Study in Neuromarketing Field.

The sample size is a crucial concern in scientific research and even more in behavioural neurosciences, where besides the best practice it is not always possible to reach large experimental samples. In this study we investigated how the outcomes of research change in response to sample size reduction. Three indices computed during a task involving the observations of four videos were considered in the analysis, two related to the brain electroencephalographic (EEG) activity and one to autonomic physiological measures, i.e., heart rate and skin conductance. The modifications of these indices were investigated considering five subgroups of sample size (32, 28, 24, 20, 16), each subgroup consisting of 630 different combinations made by bootstrapping n (n = sample size) out of 36 subjects, with respect to the total population (i.e., 36 subjects). The correlation analysis, the mean squared error (MSE), and the standard deviation (STD) of the indexes were studied at the participant reduction and three factors of influence were considered in the analysis: the type of index, the task, and its duration (time length). The findings showed a significant decrease of the correlation associated to the participant reduction as well as a significant increase of MSE and STD (p < 0.05). A threshold of subjects for which the outcomes remained significant and comparable was pointed out. The effects were to some extents sensitive to all the investigated variables, but the main effect was due to the task length. Therefore, the minimum threshold of subjects for which the outcomes were comparable increased at the reduction of the spot duration.

Driving Stress Detection Using Multimodal Convolutional Neural Networks with Nonlinear Representation of Short-Term Physiological Signals.

Mental stress can lead to traffic accidents by reducing a driver's concentration or increasing fatigue while driving. In recent years, demand for methods to detect drivers' stress in advance to prevent dangerous situations increased. Thus, we propose a novel method for detecting driving stress using nonlinear representations of short-term (30 s or less) physiological signals for multimodal convolutional neural networks (CNNs). Specifically, from hand/foot galvanic skin response (HGSR, FGSR) and heart rate (HR) short-term input signals, first, we generate corresponding two-dimensional nonlinear representations called continuous recurrence plots (Cont-RPs). Second, from the Cont-RPs, we use multimodal CNNs to automatically extract FGSR, HGSR, and HR signal representative features that can effectively differentiate between stressed and relaxed states. Lastly, we concatenate the three extracted features into one integrated representation vector, which we feed to a fully connected layer to perform classification. For the evaluation, we use a public stress dataset collected from actual driving environments. Experimental results show that the proposed method demonstrates superior performance for 30-s signals, with an overall accuracy of 95.67%, an approximately 2.5-3% improvement compared with that of previous works. Additionally, for 10-s signals, the proposed method achieves 92.33% classification accuracy, which is similar to or better than the performance of other methods using long-term signals (over 100 s).

A New Dynamical Method for Bearing Fault Diagnosis Based on Optimal Regulation of Resonant Behaviors in a Fluctuating-Mass-Induced Linear Oscillator.

Stochastic resonance (SR), a typical randomness-assisted signal processing method, has been extensively studied in bearing fault diagnosis to enhance the feature of periodic signal. In this study, we cast off the basic constraint of nonlinearity, extend it to a new type of generalized SR (GSR) in linear Langevin system, and propose the fluctuating-mass induced linear oscillator (FMLO). Then, by generalized scale transformation (GST), it is improved to be more suitable for exacting high-frequency fault features. Moreover, by analyzing the system stationary response, we find that the synergy of the linear system, internal random regulation and external excitement can conduct a rich variety of non-monotonic behaviors, such as bona-fide SR, conventional SR, GSR, and stochastic inhibition (SI). Based on the numerical implementation, it is found that these behaviors play an important role in adaptively optimizing system parameters to maximally improve the performance and identification ability of weak high-frequency signal in strong background noise. Finally, the experimental data are further performed to verify the effectiveness and superiority in comparison with traditional dynamical methods. The results show that the proposed GST-FMLO system performs the best in the bearing fault diagnoses of inner race, outer race and rolling element. Particularly, by amplifying the characteristic harmonics, the low harmonics become extremely weak compared to the characteristic. Additionally, the efficiency is increased by more than 5 times, which is significantly better than the nonlinear dynamical methods, and has the great potential for online fault diagnosis.