Galvanic Skin Response Measures Research Articles

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.

The Influence of Stress Noise and Music Stimulation on the Autonomous Nervous System

Music sound has shown to affect brain function and cardiac activity. Based on this premise, this study examines the effects of sound stimulation, such as music and stress noise, on the balance of the autonomous nervous system (ANS). To monitor such effects, physiological data was collected from a group of volunteers to analyze pulse rate variability (PRV), as well as electrodermal activity (EDA). The final goals of this study are supported by the possibility of automatically integrating musical stimuli into a smart environment to potentially lower individual’s stress levels, as well as to improve their well-being. To accomplish this objective, this research involved three different phases. The first was based on the development of a biomedical wearable sensor for measuring PRV and its validation using a reference sensing system. Good correlation results were obtained between both devices. The second consisted of a preliminary study of the effects of ambient, classic and metal music on PRV. Significant variations of PRV were measured between the three music genres. Considering the obtained results in the previous phase and overcoming the limitations presented in it, the third phase was based on a more in-depth study of the effects of musical stimuli and noise stress, through PRV analysis and the measurement of galvanic skin response (GSR). The obtained results suggested that noise stress contributes to the increasing of sympathetic activity. In contrast, ambient music proved to be very effective in increasing parasympathetic activity and stabilizing comfort levels.

Dynamics of Physiological, Biochemical and Psychological Markers during Single Session of Virtual Reality-Based Respiratory Biofeedback Relaxation.

Psychological stress exposure is associated with long-lasting health effects including memory problems, depression, aches and pains, eating disorders, and alcohol or drug use. Thus, there is a need to develop effective stress management strategies that are easy to learn and practice. Respiratory biofeedback is an evidence-based stress management technique presenting breathing-related information to help subjects learn specific breathing skills for relaxation. It is suggested that the use of biofeedback techniques in conjunction with virtual reality makes biofeedback training an even more effective tool for stress management. The current study aimed to investigate dynamics of distinct stress indicators before, after, as well as during one brief virtual reality-based respiratory biofeedback session. Thirty-nine healthy volunteers participated in the study. Individuals provided their saliva samples and evaluated their mood status, fatigue, and strain level before and after the session. The subjects' heart and respiratory rate, heart rate variability, and galvanic skin response measures were recorded during the session. The results showed that after single 12 min relaxation session, there was a significant decrease in salivary cortisol concentration, heart and respiratory rate, as well as decrease in skin conductance values. Self-reported strain, fatigue level, and mood status also significantly improved. VR-based respiratory-biofeedback-assisted relaxation sessions might serve as an effective stress management strategy, as even single session had positive effects on subjects' autonomic nervous system (ANS) and hypothalamic-pituitary-adrenal (HPA) axis activity, as well as self-reported fatigue, strain level, and mood status.

Assessing the impact of three intersection treatments on bicyclist safety using a bicycling simulator

Bicyclist safety at urban intersections is a critical element for encouraging an increase in bicycle commuting. With cyclist injury and fatality rates rising due to collisions with vehicles at signalized intersections, increasing the safety of riders continues to be an important consideration when promoting this mode of transportation. Previous research has addressed crash causality and helped to develop several roadway treatments to improve bicyclist safety, but little has been done to compare and contrast the benefits of the various treatment types. This bicycling simulator study examined the impacts of three different intersection treatments (i.e., bike box, mixing zone, and bicycle signals) to better understand their influence on bicyclists’ comfort, levels of stress, and riding behaviors. This improved understanding allowed researchers to make recommendations for which of the three designs proved to be most effective for reducing the risk of vehicle-bicycle collisions at signalized intersections.Forty participants successfully completed the study by responding to twenty-four scenarios while riding in the Oregon State University Bicycling Simulator. Time-space measurements revealed that the mixing zone treatment correlated with the most unpredictable riding behaviors. Analysis of the participants’ eye-movements revealed a lower rate of recognizing the conflict vehicle when traversing the bicycle signal treatments. Galvanic Skin Response measurements were used to measure participants stress levels but found no statistically significant results, although it was found that the mixing zone elicited slightly larger stress responses. Researchers found the bike box design to be the most versatile, providing a balance of increased safety while also requiring the participant to perceive potential danger and be ready to respond accordingly. The results of this research can provide a better understanding of how to best implement these intersection treatments to increase bicyclists’ safety at signalized intersections.

Evaluation of Takeover Time Performance of Drivers in Partially Autonomous Vehicles Using a Wearable Sensor

Advanced driver assistance systems and conditional automation (SAE level L3) bring full of challenges, central to the human role in such automated driving and are the transitions from automated to manual driving mode: driver monitoring systems should be able to adapt to the detected human state adjusting time given for takeover request. In this study, the human driver’s control authority during the transition from the automated system is evaluated by using a large set of sensors, including the galvanic skin response sensor, the speed, and the current sensor of the electric vehicle. The Local Outlier Factor (LOF) method detects the driver’s readiness to take control during the control authority transition transient stage. Eighteen drivers (8 females and 10 males) between 24 and 65 years of age participate in the experimental evaluation and data collection campaign. Takeover request time analysis evaluates each driver and three driver categories, such as experienced, semiexperienced, and inexperienced, and the questionnaire validates the takeover request time analysis. Galvanic skin response measurements and the LOF approach illustrate successful detection of the driver’s status about his/her readiness to take over and regulate the driving task when the transition of driving authority from the automated system is requested in a real road driving scenario. The takeover request time is personalized for each driver, which may improve the current conditional automated driving technologies’ penetration and acceptance.

Speech Related Anxiety in Adults Who Stutter

Abstract. The relationship between anxiety and stuttering has always been a topic of debate with a great emphasis on research focused on examining whether speech-related anxiety can exacerbate stuttering. This investigation compares some speech-related anticipatory anxiety indices in fluent and dysfluent utterances in adults who stutter (AWS). We scored the level of cognitive speech-related anxiety (anticipatory anxiety) using a self-reporting method and also evaluated the autonomic aspects of anxiety (state anxiety) through recording changes in Galvanic Skin Response (GSR) signals. Explaining the link between stuttering and anxiety is expected to assist practitioners in stuttering assessment and subsequent treatment strategies. Phasic GSR values of six events related to answering the verbal stimuli through fluent and dysfluent responses were registered to measure sympathetic arousal as an index of state anxiety in 20 AWS ( Mage = 35 ± 4 years, range: 21–42). To quantitatively examine the cognitive aspects of speech-related anticipatory anxiety, two questionnaires were rated by participants addressing the stuttering anticipation and semantic difficulty of verbal stimuli. GSR measures of fluent events were significantly higher than dysfluent counterparts within time windows before and during answering aloud the verbal stimuli ( p < .001). Later in the experiment, GSR values of dysfluent events were found to be higher than their fluent counterparts ( p < .001). Stuttering anticipation yielded a weak negative meaningful correlation with the scores of fluency ( r = −0.283, p = .046) and a positive yet nonsignificant correlation with the stuttering scores. The semantic difficulty had a moderately significant correlation with stuttering anticipation ( r = 0.354, p = .012) but not a meaningful correlation with fluency state. Autonomic and cognitive indices of speech-related anticipatory anxiety are not robust predictors of fluency. Anxiety seems to be more of a consequence of stuttering than a cause.

A Shrewd Artificial Neural Network-Based Hybrid Model for Pervasive Stress Detection of Students Using Galvanic Skin Response and Electrocardiogram Signals.

Mental illness issues are a very common health issue in youths and adults across the world. The usage of real-time data analytics in health care has a great potential to improve and enhance the quality of health care services, including diagnosis and medical prescription. Stress is one of the major health issues these days, which leads to many acute and sometimes incurable diseases to the students of very young age. Stress affects physiological parameters of the human body; due to these, human emotions may also change. This research paper proposes a hybrid model for pervasive stress detection, which deals with imbalance class problems using real-time data analytics and Internet of Things and it also presents a new stress analysis system to detect stressful conditions of the student, and to diagnose whether they are stressed or relaxed by using a designed set of experimental tasks. Regular monitoring of students'/professionals' health, including measurement of Galvanic Skin Response (GSR) and Electrocardiogram (ECG) data, provides a good understanding of their stress level. Data are acquired by using GSR and ECG sensors for 34 participants while undertaking five different tasks discussed in the proposed experiment. The graphical relationship between heart rate, blood pressure, and skin conductance across various experimental activities highlights the fact as to how physiological parameters of the human body get affected along with the mental status of the students. This article performs accuracy computation by using different machine-learning models such as Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Bagging Classifiers (BAG), Random Forest (RF), Gradient Boosting (GB), and Artificial Neural Network (ANN) followed by tuning with the best set of hyper parameters for each model. The proposed hybrid classification model deals with the class imbalance problem by using the Synthetic Minority Oversampling Technique. The shrewd ANN-based hybrid model achieves 99.4% accuracy on the self-generated dataset for the mental state classification of the students, which is best among other classifiers such as LR, SVM, KNN, BAG, RF, GB, and ANN. The prediction result of all 34 participants of the experiment is also classified into four categories: relaxed, stressed, partially stressed, and happy.

Method for detecting human stress state for video surveillance systems

The work is devoted to the development of a technique for identifying a stressed state (stress) using a video camera. Experiments were carried out to determine the stress state by two methods: measurement of galvanic skin response using the "Activatiometer-6" and by the method of pupillography. Comparative analysis of pupillograms and graphs of galvanic skin response (GSR) showed that the results of measurements by the two methods are consistent under the condition of the presence of emotion: correlation p=0.88 in the presence of a response to the test object and its decrease with a decrease in the response. In combination with other methods (eye tracking, vibraimage), it is possible to carry out remote diagnostics of a stress state, which will prevent aggressive behavior of people, suicides, etc.

Stress in different travel modes: an observational study of Galvanic Skin Response measurements (GSR)

BACKGROUND AND AIM: Stress is often associated with urban living, although active travel may reduce stress compared to other modes. Earlier work, however, has relied on self-reported measures of stress, and on study designs that limit our ability to establish causation. METHODS: Galvanic Skin Response (GSR), a proxy for stress, was collected alongside confounders and travel information from 122 participants across 3 European cities during 3 weeks as part of the Physical Activity through Sustainable Transport Approaches (PASTA) study. Causal effects of daily travel in different modes on GSR were evaluated through propensity score matching (PSM) and linear mixed models (LMMs). Separately, cycling, then walking then motorized (public or private) travel were compared to “any other activity”. RESULTS:Cycling reduces 1-minute GSR by 7.6% [95% CI: 2.6-22%] to 10.5% [95% CI: 4.9-22.7%] compared to any other activity. Walking is also beneficial, reducing GSR by 4.3% [95% CI: 1.6-11.9%] to 6.2% [95% CI: 2.9-13.3%] compared to any other activity; motorized mode (private or public) in reverse increases GSR by up to 1.4% [95% CI: 0.6-3.2%]. CONCLUSIONS:Active travel offers a welcome way to reduce stress in urban dwellers’ daily lives. Stress can be added to the growing number of evidence based reasons for promoting active travel in cities. KEYWORDS: Built environment, walkability, causal inference

Adaptive immersive Virtual Environments as a treatment for depersonalization disorder

Depersonalization is a dissociative disorder associated to a profound disruption of self-awareness in the form of emotional numbing and feelings of disembodiment. The salient feature of depersonalization is a breakdown in the familiarity of one's psychological and somatic self (and surroundings when derealization is also present), in spite of being aware of the unreality of the change. At an early stage of research it was realized that people inclined to dissociation find it harder to tolerate discontinuity in perceptual environments, possibly due to a rigid perceptual attitude. Consequently, perceptual discontinuity experienced during momentary immersion into a VE would be expected to increase symptoms of dissociation among individuals prone to develop them. It has been put forward that a tendency toward immersion or absorption, linking to imaginative processes underlying the dissociative experience, significantly relates to the level of change in virtual reality-induced dissociative symptoms. Consequently, it has been implied that increased tolerability of perceptual discontinuities and a more flexible perceptual attitude in people suffering depersonalization/derealization disorder may be of help. We propose the use of adaptive immersive virtual environments to the treatment of depersonalization. In particular, we propose that implementation of biofeedback electrical stimulation to detect somato-sensory processing bias may contribute to selectively targeting deranged neurocognitive processing components, and as an indirect consequence promote, to some extent, the diagnostic process. Psychophysiological approaches may be of help in the treatment of depersonalization via additional series of afferent inputs - virtual reality (VR) stimuli - to alter the receptive fields of the affected proprioceptive systems and reorganize them. The aim of this paper is to stimulate future research towards the development of potential virtual rehabilitation programs based on biofeedback, electrical stimulation and concurrent measurement of galvanic skin response and EEG targeting selective somatosensory stimulation in patients with depersonalization. Our research hypotheses might constitute a starting point for the development of new treatment tools for depersonalization in particular and depersonalization/derealization disorder in general.

Color aesthetics: A transatlantic comparison of psychological and physiological impacts of warm and cool colors in garden landscapes

Evidence shows green space exposure has beneficial impacts on psychological and physiological wellbeing. However, aesthetic differences in color use in cultivated garden landscapes on wellbeing remains unexplored. This study investigates how warm and cool colored garden landscapes affect psychological and physiological wellbeing and how responses differ geographically.Our between subjects design used USA and UK participants exposed to videos of static garden landscapes consisting of (a) warm colors, (b) cool colors and (c) control images. Measures of subjective psychological wellbeing (UWIST Mood Adjective Checklist (MACL)) and biometrics of stress using the Empatica E4 watch (Heart rate; Heart Rate Variability (HRV); Skin Temperature; Galvanic Skin Response (GSR) and Photoplethysmography) were obtained to ascertain if warm and cool colored cultivated garden landscapes affected psychological and physiological responses.Results showed statistical differences between locations in psychological and physiological wellbeing. USA participants experienced increases in hedonic tone and decreases in perceived stress after viewing warm and cool colored garden landscapes, a result not found in UK participants. Physiological indicators show geographical differences with beneficial effects of warm colors in the USA, shown in HRV and GSR measures relative to control. The UK sample presented mixed evidence regarding positive effects of warm and cool colored garden landscapes on physiological measures.These findings show stronger psychological and physiological responses to color in the US sample compared to a UK sample, suggesting geographic disparities in these responses to plant color. This should be further explored to understand color choice for landscape design to optimize outdoor settings that maximize wellbeing.

Biosensors Show Promise as a Measure of Student Engagement in a Large Introductory Biology Course.

This study measured student engagement in real time through the use of skin biosensors, specifically galvanic skin response (GSR), in a large undergraduate lecture classroom. The study was conducted during an intervention in an introductory-level biology course (N = 420) in which one section of the course was taught with active-learning approaches and the other with traditional didactic teaching. GSR results were aligned and correlated with the Classroom Observation Protocol for Undergraduate STEM, or COPUS, and student self-reflections on their own engagement. Results showed that the active-learning section spent more time working in groups, resulting in GSR measures that trended higher and self-reported engagement, while showing indications of higher content learning gains compared with the traditional lecture section. Comparisons between COPUS scores and GSR readings indicate that engagement increased during group work and decreased during listening activities. Throughout a class period, GSR activity of the active-learning group showed increased trends compared with baseline measures, while the traditional lecture group showed decreased trends. Results indicate that GSR is a promising measure of real-time student engagement in the undergraduate classroom, bringing a new technique to discipline-based education researchers who aim to better measure student engagement; however, some limitations exist for broad-scale implementation.

The Study of Facial Muscle Movements for Non-Invasive Thermal Discomfort Detection via Bio-Sensing Technology. Part I: Development of the Experimental Design and Description of the Collected Data

In the time of climate change, as heat waves become a more regular occurrence, indoor thermal comfort is an important factor in day to day life. Due to such circumstances, many researchers have focused their studies on finding an effective solution that will not only enable thermal comfort, but also increase satisfaction within the indoor environment and, as a result, productivity. The fast development of the biometrical field encouraged the study focused on the investigation of how bio-markers, in combination with artificial intelligence algorithms, can be collected within an experimental setting to create a new approach for non-invasive thermal discomfort detection. The developed experimental design provides synergy between automatic facial coding, pulse, and galvanic skin response measurements via iMotions software in a controlled environment. The iMotions software has built-in machine vision algorithms, and with Shimmer sensors and a post-processing tool through Affectiva AFFDEX, is able to collect facial action data through detection of the facial muscle movements and various bio-markers. The Zero Emission Building (ZEB) Test Cell laboratory was used as the control environment and transformed to imitate an office space for the data collection campaign at NTNU in Trondheim. The given experimental design provides an opportunity to create an immense database with bio-markers that are linked to the subcortical level of the brain, indoor parameters, and direct feedback on the comfort level of occupants within an office-like environment. In total, 111 data collection sessions were registered with iMotions. The discomfort button was pressed 240 times and 1080 planned indoor comfort evaluation surveys were held during experiment. The discomfort button was pressed 49 times to indicate that participant felt discomfort due to low temperature and 52 due to high temperature. Collected data revealed a big deviation in the discomfort temperature values for experiment participants with respect to performed temperature ramps. While it is common to use the same predefined temperature range for facility management, it became clear that the complexity of the task is greater and should not be approached on a human computational level. Implementation of AI can potentially provide higher value accuracy within thermal discomfort detection and enable unique personal user experience at the workplace.

MP22: Using galvanic skin response to identify resuscitation expertise in a pulmonary embolism simulation exercise

Introduction: As physicians gain expertise in clinical settings, they are able to handle progressively more information, in both complexity and magnitude, as an organized schema. Expert physicians then, will be more likely to function with less cognitive load —the amount of mental effort someone exerts within their short-term working memory. Expert physicians will also retain more working memory capacity to process information during medical emergencies than novice physicians. While a physician's ability to process medical information may have implications for handling medical emergencies, there is a paucity of empirical research examining the link between physicians’ expertise and biometric measures of cognitive load. Using galvanic skin response (GSR) as a surrogate measure of cognitive load, we assess whether average cognitive load differs significantly between expert and novice physicians in a pulmonary embolism simulation exercise. Methods: We analyzed GSR data (n = 39) from a 10-minute simulated pulmonary embolism exercise among 18 faculty physicians and 21 residents. Cluster and factor analyses were used to identify novice, intermediate, advanced, and expert physicians with based on participants’ GRS scores. One-way ANOVA was used to analyze group differences. Descriptive statistical techniques were also used to describe the distribution of GRS expertise by participants’ level of training. Results: Contrary to expectation, we found more than two groups of resuscitation expertise in the simulation exercise. Respectively, we identified 7.7% and 20.5% of participants as novice and expert physicians. About 36% of participants were classified as intermediate (35.9%) physicians while another 36% were classified as having an advanced (35.9%) expertise in resuscitation. All the novice physicians identified were found to be PGY1 and PGY2 resident residents. A third (33.3%) of faculty physicians and 9.5% of residents were identified as experts. As expected, average GSR score for experts (x¯ = 0.60μS, SD = 0.26) was significantly (F = 137.6, p < 0.001) lower than the average GSR for novices (x¯ = 5.55μS, SD = 0.99), intermediate (x¯ = 2.84 μS, SD = 0.40), and advanced (x¯ = 1.57 μS, SD = 0.28) physicians. Conclusion: GSR measures of cognitive load may be used to identify resuscitation expertise in managing pulmonary embolism and related medical conditions through simulation exercises.

Measurements of Galvanic Skin Response on Subjects Affected by Stress

Abstract The aim of the present paper is to study the Galvanic Skin Response (GSR) level on subjects affected by stress. The device that we have used, connects to the people by finger electrodes to record GSR. The purpose was to find statistical differences between the activities (mental task, walking, sitting and to fill out a survey about their lives) and their stress level. During the experiment, it was found that the survey caused the source of high stress and increasing skin conductance was caused by sweat secretion (mental, physical activity). Is needed to work of collecting data from more subjects because GSR is depended on human behaviour, is variable upon many factors (their eating habits, their emotional state, their gender, their relationship with family, etc) and we need to build a substantial data set for a valid research.

Wearables and the Quantified Self: Systematic Benchmarking of Physiological Sensors.

Wearable sensors are increasingly used in research, as well as for personal and private purposes. A variety of scientific studies are based on physiological measurements from such rather low-cost wearables. That said, how accurate are such measurements compared to measurements from well-calibrated, high-quality laboratory equipment used in psychological and medical research? The answer to this question, undoubtedly impacts the reliability of a study’s results. In this paper, we demonstrate an approach to quantify the accuracy of low-cost wearables in comparison to high-quality laboratory sensors. We therefore developed a benchmark framework for physiological sensors that covers the entire workflow from sensor data acquisition to the computation and interpretation of diverse correlation and similarity metrics. We evaluated this framework based on a study with 18 participants. Each participant was equipped with one high-quality laboratory sensor and two wearables. These three sensors simultaneously measured the physiological parameters such as heart rate and galvanic skin response, while the participant was cycling on an ergometer following a predefined routine. The results of our benchmarking show that cardiovascular parameters (heart rate, inter-beat interval, heart rate variability) yield very high correlations and similarities. Measurement of galvanic skin response, which is a more delicate undertaking, resulted in lower, but still reasonable correlations and similarities. We conclude that the benchmarked wearables provide physiological measurements such as heart rate and inter-beat interval with an accuracy close to that of the professional high-end sensor, but the accuracy varies more for other parameters, such as galvanic skin response.

Analysis and monitoring of IoT-assisted human physiological galvanic skin responsefactor for smart e-healthcare

PurposeBackground: Every so often, one experiences different physically unstable situations which may lead to possibilities of suffering through vicious physiological risks and extents. Dynamic physiological activities are such a key metric that they are perceived by means of measuring galvanic skin response (GSR). GSR represents impedance of human skin that frequently changes based on different human respiratory and physical instability. Existing solutions, paved in literature and market, focus on the direct measurement of GSR by two sensor-attached leads, which are then parameterized against the standard printed circuit board mechanism. This process is sometimes cumbersome to use, resulting in lower user experience provisioning and adaptability in livelihood activities. The purpose of this study is to validate the novel development of the cost-effective GSR sensing system for affective usage for smart e-healthcare.Design/methodology/approachThis paper proposes to design and develop a flexible circuit strip, populated with essential circuitry assemblies, to assess and monitor the level of GSR. Ordinarily, this flexible system would be worn on the back palm of the hand where two leads would contact two sensor strips worn on the first finger.FindingsThe system was developed on top of Pyralux. Initial goals of this work are to design and validate a flexible film-based GSR system to detect an individual’s level of human physiological activities by acquiring, amplifying and processing GSR data. The measured GSR value is visualized “24 × 7” on a Bluetooth-enabled smartphone via a pre-incorporated application. Conclusion: The proposed sensor-system is capable of raising the qualities such as adaptability, user experience, portability and ubiquity for possible application of monitoring of human psychodynamics in a more cost-effective way, i.e. less than US$50.Practical implicationsSeveral novel attributes are envisaged in the development process of the GSR system that made it different from and unique as compared to the existing alternatives. The attributes are as follows: (i) use of reproductive sensor-system fabrication process, (ii) use of flexible-substrate for hosting the system as proof of concept, (iii) use of miniaturized microcontroller, i.e. ATTiny85, (iv) deployment of energy-efficient passive electrical circuitry for noise filtering, (v) possible use case scenario of using CR2032 coin battery for provisioning powering up the system, (vi) provision of incorporation of internet of things (IoT)-cloud integration in existing version while fixing related APIs and (vii) incorporation of heterogeneous software-based solutions to validate and monitor the GSR output such as MakerPlot, Arduino IDE, Fritzing and MIT App Inventor 2.Originality/valueThis paper is a revised version R1 of the earlier reviewed paper. The proposed paper provides novel knowledge about the flexible sensor system development for GSR monitoring under IoT-based environment for smart e-healthcare.

Fabrication of Stress and Temperature Measurement Sensors and Possible Integration with Fabricated Biodegradable Drug Delivery Device

ABSTRACT This paper presents the design and fabrication of flexible skin conductance and temperature sensor using PDMS with a minimized skin contact area and increased device flexibility. The design and fabrication of reservoir-based (RB) drug delivery system (DDS) using microfabrication technology is also presented in this paper with possible future integration with these sensors. While the sensors can be used for the measurement of galvanic skin response for detecting emotional activity and monitoring of human stress in daily life, the RB-DDS can be used for delivering drug like insulin in a controlled and sustained manner to the target site. The sensor performance has been experimentally verified using the BITalino (r)evolution Board Kit while the DDS has been tested in-vivo and in-vitro. A comparative study on the sticking property of PDMS with aluminium was also conducted by exposing PDMS with oxygen and argon plasma.

A Classification Model for Sensing Human Trust in Machines Using EEG and GSR

Today, intelligent machines interact and collaborate with humans in a way that demands a greater level of trust between human and machine. A first step toward building intelligent machines that are capable of building and maintaining trust with humans is the design of a sensor that will enable machines to estimate human trust level in real time. In this article, two approaches for developing classifier-based empirical trust-sensor models are presented that specifically use electroencephalography and galvanic skin response measurements. Human subject data collected from 45 participants is used for feature extraction, feature selection, classifier training, and model validation. The first approach considers a general set of psychophysiological features across all participants as the input variables and trains a classifier-based model for each participant, resulting in a trust-sensor model based on the general feature set (i.e., a “general trust-sensor model”). The second approach considers a customized feature set for each individual and trains a classifier-based model using that feature set, resulting in improved mean accuracy but at the expense of an increase in training time. This work represents the first use of real-time psychophysiological measurements for the development of a human trust sensor. Implications of the work, in the context of trust management algorithm design for intelligent machines, are also discussed.

A case study of vocal features associated with galvanic skin response to stressors in a clinical interaction

ObjectiveWe investigated vocal characteristics associated with physiologically determined stressful episodes by means of post-hoc acoustic analyses of speech recorded in a clinical setting. Our research addressed the understudied question of which vocal features may serve as cues naturally occurring stress and is the first to explore this issue in a pitch accent language. MethodsThe vocal profile of a single female patient interacting with a physician was analyzed with standard speech analysis software for acoustic indicators of stress-related arousal determined by galvanic skin response measurements. ResultsVocal jitter, representing an aspect of voice quality perceived as hoarseness, appeared to increase during and immediately after skin conductance response intervals. Skin conductance levels during the response intervals were negatively correlated with acoustic features used to approximate the perception of voice unsteadiness (slope and standard deviation of fundamental frequency). ConclusionAn acoustic analysis of vocal properties of speech uttered during independently detected skin conductance response intervals revealed individual patterns for some acoustic features linked to stress in earlier studies. Practice implicationsNon-invasive methods of arousal detection in physician-patient communication based on acoustic analyses of vocal profiles may, in combination with other analyses, help identify stressful events and thus improve the process of medical information gathering and decision-making.