Journal articles: 'Galvanic skin response test'

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Tuzemen, Gokhan, Oguz Basut, Omer Afsin Ozmen, and Hamdi Hakan Coskun. "Galvanic Skin Response Test." Journal of Craniofacial Surgery 24, no. 4 (July 2013): 1280–84. http://dx.doi.org/10.1097/scs.0b013e318286038d.

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Dolce, G., F. Riganello, M. Quintieri, A. Candelieri, and D. Conforti. "Personal Interaction in the Vegetative State." Journal of Psychophysiology 22, no. 3 (January 2008): 150–56. http://dx.doi.org/10.1027/0269-8803.22.3.150.

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Background and purpose: Brain processing at varying levels of functional complexity and emotional reactions to relatives are anecdotally reported by the caregivers of patients in a vegetative state. In this study, computer-assisted machine-learning procedures were applied to identify heart rate variability changes or galvanic skin responses to a relative’s presence. Methods: The skin conductance (galvanic skin response) and heart beats were continuously recorded in 12 patients in a vegetative state, at rest (baseline) and while approached by a relative (usually the mother; test condition) or by a nonfamiliar person (control condition). The cardiotachogram (the series of consecutive intervals between heart beats) was analyzed in the time and frequency domains by computing the parametric and nonparametric frequency spectra. A machine-learning algorithm was applied to sort out the significant spectral parameter(s). For all patients, each condition (baseline, test, control) was characterized by the values of its spectral parameters, and the association between spectral parameters values and experimental condition was tested (WEKA machine-learning software). Results and comments: A galvanic skin response was obtained in two patients. The machine-learning procedure independently selected the nu_LF spectral parameter and attributed each nu_LF measure to any of the three experimental conditions. 69.4% of attributions were correct (baseline: 58%; test condition: 75%; control. 75%). In seven patients, attribution changed when the subject was approached by the test person; specifically, sequential shifts from baseline to test condition (“the Mom effect”) to control condition were identified in four patients (30.0%); the change from test to control was attributed correctly in seven patients (58%). The observation of heart rate changes tentatively attributable to emotional reaction in a vegetative state suggest residual rudimentary personal interaction, consistent with functioning limbic and paralimbic systems after massive brain damage. Machine-learning proved applicable to sort significant measure(s) out of large samples and to control for statistical alpha inflation.

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Shivakumar, G., and P. A. Vijaya. "Analysis of Human Emotions Using Galvanic Skin Response and Finger Tip Temperature." International Journal of Synthetic Emotions 2, no. 1 (January 2011): 15–25. http://dx.doi.org/10.4018/jse.2011010102.

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Emotion is the excited mental state of a person caused by internal and external factors. In this work, a person’s physiological parameters are measured to decide emotional status. A generalized system measures changes occurring in the body of a subject, such as heart rate, blood pressure, respiratory rate, electro-dermal (Galvanic skin resistance) activity, and arm and leg motions. These measurements are then compared with the normal levels of the subject. The present work monitors the physiological parameters by connecting sensors at specific points on a test body. Two physiological parameters are considered: galvanic skin response (GSR) and finger tip temperature (FTT). The heart rate is predominant in deciding the emotion of a person. This system, in conjunction with a certified examiner, is used to analyze a subject’s stress. A system is constructed that measures physiological parameters along with signal conditioning units. These measurements are transmitted to a LabVIEW add-on card for further data processing and analysis. LabVIEW is a graphical programming language that includes all tools necessary for data acquisition, data analysis, and presentation of results. The results obtained are realistic and provide a measure of accuracy.

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Sanchez-Comas, Andres, Kåre Synnes, Diego Molina-Estren, Alexander Troncoso-Palacio, and Zhoe Comas-González. "Correlation Analysis of Different Measurement Places of Galvanic Skin Response in Test Groups Facing Pleasant and Unpleasant Stimuli." Sensors 21, no. 12 (June 19, 2021): 4210. http://dx.doi.org/10.3390/s21124210.

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

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Kumar, Mahendra, Priyamvada Srivastava, Manoj K. Sahu, and Saparya Tripathi. "Effect of computerized biofeedback relaxation on stress related physiological parameters." International Journal Of Community Medicine And Public Health 8, no. 6 (May 25, 2021): 2977. http://dx.doi.org/10.18203/2394-6040.ijcmph20212003.

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Background: Mental health diseases such as insomnia, anxiety, stress and depression all have a close relationship with the autonomic nervous system. The physiological parameters of autonomic activity viz. galvanic skin resistance, electromyography, respiration and pulse rate can be regulated with the help of computerized biofeedback relaxation training. The main objective of this study was to see the effect of computerized biofeedback relaxation training on psychophysiological parameters of autonomic activity.Methods: In the present study 40 high stress post graduate students were selected. All participants were randomly divided into two group i.e. computerized biofeedback relaxation training (group-1) and placebo group (group-2). Forehead muscle tension, respiration rate, pulse rate and galvanic skin resistance were assessed, and inventories measuring stress were administered pre-randomization. Descriptive, Paired sample ‘t’ test, F-test and Mann-Whitney U test were used to analyze the data with the help of SPSS 16 version.Results: Biofeedback group reported a signiﬁcant change in muscle tension (p=0.27), respiration rate (p=0.01) and galvanic skin response (p=0.35) after relaxation but at the same time control group reported moderate increase in muscle tension. Additionally, the computerized biofeedback group was able to maintain the stress level while the control group had a signiﬁcant increase in the stress level over the 10 days of relaxation training.Conclusions: Biofeedback relaxation useful alternative therapy for management of stress and emotional disturbance in graduate students. During a stressful period this may also be helpful to promote overall psychological health.

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Demirci, Ugur, Oguz Basut, Behzat Noyan, Uygar Levent Demir, O. Afsin Ozmen, Fikret Kasapoglu, H. Hakan Coskun, and Selcuk Onart. "The Efficiacy of Sternocleidomastoid Muscle Flap on Frey’s Syndrome via a Novel Test: Galvanic Skin Response." Indian Journal of Otolaryngology and Head & Neck Surgery 66, S1 (January 31, 2012): 291–98. http://dx.doi.org/10.1007/s12070-012-0492-y.

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Konovalov, V. F., and I. S. Serikov. "Characteristics of the galvanic skin response and electrocardiogram in active and passive subjects under test conditions." Human Physiology 32, no. 5 (October 2006): 578–83. http://dx.doi.org/10.1134/s0362119706050124.

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Dogan, Daghan, Seta Bogosyan, and Tankut Acarman. "Evaluation of driver stress level with survey, galvanic skin response sensor data, and force-sensing resistor data." Advances in Mechanical Engineering 11, no. 12 (December 2019): 168781401989155. http://dx.doi.org/10.1177/1687814019891555.

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Thousands of lives are lost in traffic accidents every year, and most traffic accidents are caused by driver errors. Causes and impairments such as fatigue, inattentiveness, alcohol usage, stress, and drugs are the main factors of these accidents. When a driver is subject to changing and complicated driving tasks in traffic, he or she should be able to assure driving authority to prevent potential hazards and accidents. In this context, the purpose of this study is to determine the stress level of the driver when driving in urban traffic in such situations requiring delegation of driving authority. Thus, the work combines stress questionnaire and galvanic skin response sensor to validate results and fuses with a force-sensing resistor. In this study, a prototype electric vehicle is equipped with sensors providing various drivers’ data including the responses of a force-sensing resistor sensor while galvanic skin is being collected on a specified route. At the end of the trip, the stress level of the drivers is determined by the collected data. Results indicate that the galvanic skin sensor stress results are consistent with the results of the survey with an average accuracy of 87.5%. The force-sensing resistor sensor is only used to determine gender stress. And the force-sensing resistor sensor gender-stress results are consistent with results of the survey with an accuracy of 100%. These results are used to validate the results of post-driving stress survey evaluated by SPSS 23.0 windows statistics software. Data analysis is particularly focused on demographic properties of participators, factor analysis, reliability tests, correlation, T-test, and one-way analysis of variance.

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Sepehri, Shiva, Mohsen Aliabadi, Rostam Golmohammadi, and Mohammad Babamiri. "Human cognitive functions and psycho-physiological responses under low thermal conditions in a simulated office environment." Work 69, no. 1 (May 26, 2021): 197–207. http://dx.doi.org/10.3233/wor-213469.

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BACKGROUND: In office environments, thermal comfort is one of the most significant factor affecting employees’ performance. OBJECTIVE: This study aimed to determine the effects of exposure to low air temperatures on human cognitive performance, physiological responses, and thermal perceptions during mental work. METHODS: Twenty-four volunteers with an age range of 18–30 years participated in this study. The subjects were exposed to four different air temperatures (10, 14, 18, and 22°C) in a climate chamber based on a within-subject design. The n-back, CPT, and PVT tests were employed to evaluate some basic aspects of cognitive performance. Body physiological responses and the subjective thermal comfort were also measured. RESULTS: When the thermal condition deviated from relatively neutral temperature, the subjects’ cognitive responses significantly disturbed (P < 0.05), such that the response accuracy was more affected by reduction of air temperature. The blood pressures and heart rate, galvanic skin response, and respiration rate increased as the air temperature decreased (P < 0.05), such that the galvanic skin response as a stress indicator was more affected. In the test configurations, as a result of decrease in air temperature of 1°C, the finger and body skin temperatures reduced to 0.74°C and 0.25°C, respectively. CONCLUSIONS: The findings confirmed that low thermal condition can considerably affect cognitive performance and physiological responses during some office work tasks. The subjects’ thermal comfort votes proposed that air temperature lower than 14 °C can be intolerable for employees during routine mental work. It is suggested that personalized conditioning systems should be used to provide individual thermal comfort in moderate cold air conditions.

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Schell, Dennis, and Carol Seefeldt. "Development of a Death Anxiety Scale for Children." OMEGA - Journal of Death and Dying 23, no. 3 (November 1991): 227–34. http://dx.doi.org/10.2190/nyj6-5q3q-mh4m-7787.

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The purpose of this study was to develop a valid, reliable, and efficient instrument that could be used to explore children's responses to death and dying. A rating scale consisting of neutral and anxiety producing words was constructed; scale construction consisted of a number of steps. Test/retest reliability of the scale was assessed as .87 for death words and .79 for neutral words using Cronbach's alpha. Validity was established by reading the words to children between the ages of five and twelve and recording galvanic skin response, response time, and pulse rate. Children responded with greater skin resistance to death words, than to the neural words ( t(154) = −1.93, p < .06) leading to the conclusion that the Death Anxiety Scale for Children (DASC) was a valid, reliable, and efficient measure of children's death anxiety.

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Bali, Anjana, and Amteshwar Singh Jaggi. "Clinical experimental stress studies: methods and assessment." Reviews in the Neurosciences 26, no. 5 (October 1, 2015): 555–79. http://dx.doi.org/10.1515/revneuro-2015-0004.

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AbstractStress is a state of threatened homeostasis during which a variety of adaptive processes are activated to produce physiological and behavioral changes. Stress induction methods are pivotal for understanding these physiological or pathophysiological changes in the body in response to stress. Furthermore, these methods are also important for the development of novel pharmacological agents for stress management. The well-described methods to induce stress in humans include the cold pressor test, Trier Social Stress Test, Montreal Imaging Stress Task, Maastricht Acute Stress Test, CO2 challenge test, Stroop test, Paced Auditory Serial Addition Task, noise stress, and Mannheim Multicomponent Stress Test. Stress assessment in humans is done by measuring biochemical markers such as cortisol, cortisol awakening response, dexamethasone suppression test, salivary α-amylase, plasma/urinary norepinephrine, norepinephrine spillover rate, and interleukins. Physiological and behavioral changes such as galvanic skin response, heart rate variability, pupil size, and muscle and/or skin sympathetic nerve activity (microneurography) and cardiovascular parameters such as heart rate, blood pressure, and self-reported anxiety are also monitored to assess stress response. This present review describes these commonly employed methods to induce stress in humans along with stress assessment methods.

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Zuccalà, Veronica Chiara, Riccardo Favilla, and Giuseppe Coppini. "Recognition of Stress Activation by Unobtrusive Multi Sensing Setup." Applied Sciences 11, no. 14 (July 10, 2021): 6381. http://dx.doi.org/10.3390/app11146381.

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It is recognized that stress conditions play an important role in the definition of individual wellness and represent a major risk factor for most non-communicable diseases. Most studies focus on the evaluation of response to maximal stress conditions while a few of them reports results about the detection/monitoring of response to mild stimulations. In this study, we investigate the capability of some physiological signs and indicators (including Heart Rate, Heart Rate Variability, Respiratory Rate, Galvanic Skin Response) to recognize stress in response to moderate cognitive activation in daily life settings. To achieve this goal, we built up an unobtrusive platform to collect signals from healthy volunteers (10 subjects) undergoing cognitive activation via Stroop Color Word Test. We integrated our dataset with data from the Stress Recognition in the Automobile Drivers dataset. Following data harmonization, signal recordings in both datasets were split into five-minute blocks and a set of 12 features was extracted from each block. A feature selection was implemented by two complementary approaches: Sequential Forward Feature Selection (SFFS) and Auto-Encoder (AE) neural networks. Finally, we explored the use of Self-Organizing Map (SOM) to provide a flexible representation of an individual status. From the initial feature set we have determined, by SFFS analysis, that 2 of them (median Respiratory Rate and number peaks in Galvanic Skin Response signals) can discriminate activation statuses from resting ones. In addition, AE experiments also support that two features can suffice for recognition. Finally, we showed that SOM can provide a comprehensive but compact description of activation statuses allowing a fine prototypical representation of individual status.

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Rahman, Nur Athilah Abdul, and Asral Bahari Jambek. "Biomedical health monitoring system design and analysis." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 3 (March 1, 2019): 1056. http://dx.doi.org/10.11591/ijeecs.v13.i3.pp1056-1064.

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<span>e-Health remote monitoring systems have bloomed rapidly with a myriad of applications. This paper discusses a design of a remote monitoring device for biomedical field. Four biomedical sensors which are electrocardiography (ECG), airflow, galvanic skin response and temperature with two boards which are the e-Health Shield Board V2.0 and Arduino Uno Board are used. The results show satisfactory output for each experiment using two test subjects. The device able to achieve high accuracy where percentage of temperature difference is less than 1% compared to the commercial devices with an average power consumption of each working sensor on board is ≤9W.</span>

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Bland, Mark W., and Elizabeth Morrison. "The Experimental Detection of an Emotional Response to the Idea of Evolution." American Biology Teacher 77, no. 6 (August 1, 2015): 413–20. http://dx.doi.org/10.1525/abt.2015.77.6.413.

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Evolution is widely regarded as biology's unifying theme, yet rates of rejection of evolutionary science remain high. Anecdotal evidence suggests that cognitive dissonance leading to an emotional response is a barrier to learning about and accepting evolution. We explored the hypothesis that students whose worldviews may be inconsistent with the acceptance of evolution generate detectable emotional responses in the form of physiological changes when exposed to evolutionary themes. Physiological data (respiratory rate, galvanic skin response, and heart rate) were collected from participants while they were asked “yes/no” questions, some of which referenced evolution. Questions were of three categories: relevant, irrelevant, and control. Authenticity of response rates to relevant questions such as “Do you believe in evolution?” were verified using visual inspection to compare degree of response rates with control questions, such as “Have you ever cheated on a test?” Our results support our hypothesis. Of the 33 participants included in our study, a majority of them produced detectable physiological changes indicating emotional responses when asked questions referencing evolution. The highest response rate (79%) was generated by the question “Do you believe in evolution?” The implications of an emotional response in students when presented with instruction in evolutionary theory are discussed.

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Ancella Hendrika, Clara Theresia, and Thedy Yogasara. "Cybersickness Testing Of Gender And Experience Factors Using Virtual Reality." International Journal of Engineering Technology and Natural Sciences 2, no. 2 (April 5, 2021): 63–69. http://dx.doi.org/10.46923/ijets.v2i2.79.

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One of the technologies that people are starting to get interested in is virtual reality (VR). VR is widely used as a means of entertainment, even more so at this time, the e-sports industry is developing rapidly. However, the use of VR can cause cybersickness, a disease arising from sensory and perceptual mismatches between the visual and vestibular systems. The emergence of cybersickness can be related to gender and experience using VR. There have been studies on cybersickness, but the results obtained had not come to the same conclusion. This research aims to identify the effect of gender and experience using VR, predict the timing of cybersickness by using physiological measurements, and provide recommendations that can minimize cybersickness in activities using VR. The measuring instruments used are the galvanic skin response (GSR) and a simulator sickness questionnaire (SSQ). In this study, the influence test is conducted by using ANOVA and Kruskal-Wallis to determine whether gender and experience using VR affect the potential for cybersickness. Based on the GSR measurement results, it found that gender and experiences of using VR do not affect a person's potential for cybersickness. From the result of SSQ measurement, gender does not affect the cybersickness, but the experience of using VR affected a person's potential for cybersickness. Qualitatively, cybersickness symptoms appear in the 15-20 minutes after the VR game has set in. Therefore, it is recommended to limit the usage of VR to less than 15 to 20 minutes per session. Keywords: cybersickness, galvanic skin response (GSR), simulator sickness questionnaire (SSQ), virtual reality

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Nilachal, Nilachal, and Piyush Trivedi. "A case study of the effect of Yagya on the level of stress and anxiety." Interdisciplinary Journal of Yagya Research 2, no. 2 (December 31, 2019): 07–10. http://dx.doi.org/10.36018/ijyr.v2i2.44.

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The fundamental principle of human life, nature or the universe is to give or to serve others. This whole mechanism of sharing is the core takeaway of the process of Yagya – an ancient rite of fire oblations. Yagya therapy is an ancient Indian therapy which is a holistic health management. It provides the therapeutic approach in mental disorders and state of mind. The present study evaluated effect of Yagya on the mental health. A study was conducted on 4 subjects for 30 days and levels of stress and anxiety were evaluated. Biofeedback Galvanic Skin Response (GSR) and Sinha’s Comprehensive Anxiety Test (SCAT) were used for measuring stress and anxiety levels respectively. The result showed improvement in both the conditions of the participants. Their stress (p=0.11; Student’s two tail test) and anxiety (p=0.08; Student’s two tail test) levels showed trend of reduction with 30 days of treatment. This study indicated the utility of Yagya in daily life for combating mental conditions of stress and anxiety.

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Durán-Acevedo, Cristhian Manuel, Jeniffer Katerine Carrillo-Gómez, and Camilo Andrés Albarracín-Rojas. "Electronic Devices for Stress Detection in Academic Contexts during Confinement Because of the COVID-19 Pandemic." Electronics 10, no. 3 (January 27, 2021): 301. http://dx.doi.org/10.3390/electronics10030301.

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

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Alanazi, Saad Awadh, Madallah Alruwaili, Fahad Ahmad, Alaa Alaerjan, and Nasser Alshammari. "Estimation of Organizational Competitiveness by a Hybrid of One-Dimensional Convolutional Neural Networks and Self-Organizing Maps Using Physiological Signals for Emotional Analysis of Employees." Sensors 21, no. 11 (May 28, 2021): 3760. http://dx.doi.org/10.3390/s21113760.

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The theory of modern organizations considers emotional intelligence to be the metric for tools that enable organizations to create a competitive vision. It also helps corporate leaders enthusiastically adhere to the vision and energize organizational stakeholders to accomplish the vision. In this study, the one-dimensional convolutional neural network classification model is initially employed to interpret and evaluate shifts in emotion over a period by categorizing emotional states that occur at particular moments during mutual interaction using physiological signals. The self-organizing map technique is implemented to cluster overall organizational emotions to represent organizational competitiveness. The analysis of variance test results indicates no significant difference in age and body mass index for participants exhibiting different emotions. However, a significant mean difference was observed for the blood volume pulse, galvanic skin response, skin temperature, valence, and arousal values, indicating the effectiveness of the chosen physiological sensors and their measures to analyze emotions for organizational competitiveness. We achieved 99.8% classification accuracy for emotions using the proposed technique. The study precisely identifies the emotions and locates a connection between emotional intelligence and organizational competitiveness (i.e., a positive relationship with employees augments organizational competitiveness).

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Kurth, Luisa, Anna Engelniederhammer, Heide Sasse, and Georgios Papastefanou. "Effects of a short mindful-breathing intervention on the psychophysiological stress reactions of German elementary school children." School Psychology International 41, no. 3 (January 29, 2020): 218–38. http://dx.doi.org/10.1177/0143034320903480.

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This research investigates whether a short mindfulness exercise can reduce children’s psychophysiological stress reactions in the face of a performance task. To answer the question, a randomized controlled trial with 106 elementary school children, aged between 5 and 11 years, was conducted. An intervention group completed a two-minute breathing exercise, whilst a comparison group watched a short video, before both groups performed a stress-evoking Stroop test. The stress levels of both groups were measured via galvanic skin response and compared. It was hypothesized that the comparison group would show a higher stress reaction during the stress-evoking task than the intervention group. Contrary to the hypothesis, results show that the intervention group had a higher psychophysiological stress reaction during the task than the comparison group. However, the stress reaction to the announced difficulty of the task was smaller in the mindfulness group than in the comparison group. Results are discussed based on different theoretical mechanisms of mindfulness. Directions for future research include the use of different techniques and durations of mindfulness interventions, different control group activities and stress-test operationalizations, as well as the distinction between age groups.

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Goshvarpour, Atefeh, Ataollah Abbasi, and Ateke Goshvarpour. "GENDER DIFFERENCES IN RESPONSE TO AFFECTIVE AUDIO AND VISUAL INDUCTIONS: EXAMINATION OF NONLINEAR DYNAMICS OF AUTONOMIC SIGNALS." Biomedical Engineering: Applications, Basis and Communications 28, no. 04 (August 2016): 1650024. http://dx.doi.org/10.4015/s1016237216500241.

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Physiological reflection of emotions can be tracked by autonomic signals. Several studies have been conducted using autonomic signal processing to examine men and women differences during the exposure of affective stimuli. Emotional pictures and music are two commonly used methods to induce affects in an experimental setup. The biological changes have been commonly monitored during a certain emotional inducement protocol, solely. This study was aimed to examine two induction paradigms involved auditory and visual cues using nonlinear dynamical approaches. To this end, various nonlinear parameters of galvanic skin response (GSR) and pulse signals of men and women were examined. The nonlinear analysis was performed using lagged Poincare parameters, detrended fluctuation indices (DFAs), Lyapunov exponents (LEs), some entropy measures, and recurrence quantification analysis (RQA). The Wilcoxon rank sum test was used to show significant differences between the groups. The results indicate that besides the type of affect induction, physiological differences of men and women are notable in negative emotions (sadness and fear). Regardless to the inducements, lagged Poincare parameters of the pulse signals and DFA indices of the GSR have shown significant differences in gender affective responses. However, applying pictorial stimuli, LEs are appropriate indicators for gender discrimination. It is also concluded that GSR dynamics are intensely affected by the kind of stimuli; while this is not validated for the pulse. These findings suggest that different emotional inductions evoked different autonomic responses in men and women, which can be appropriately monitored using nonlinear signal processing approaches.

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Fox, Alexa K., George D. Deitz, Marla B. Royne, and Joseph D. Fox. "The face of contagion: consumer response to service failure depiction in online reviews." European Journal of Marketing 52, no. 1/2 (February 12, 2018): 39–65. http://dx.doi.org/10.1108/ejm-12-2016-0887.

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Purpose Online consumer reviews (OCRs) have emerged as a particularly important type of user-generated information about a brand because of their widespread adoption and influence on consumer decision-making. Much of the existing OCR research focuses on quantifiable OCR features such as star ratings and volume. More research that examines the influence of review elements, aside from numeric ratings, such as the verbatim text, particularly in services contexts is needed. The purpose of this research is to investigate the impact of service failures on consumer arousal and emotions. Design/methodology/approach The authors present three behavioral experiments that manipulate service failure and linguistic elements of OCRs by using galvanic skin response, survey measures and automated facial expression analysis. Findings Negative OCRs lead to the greatest levels of arousal when consumers read OCRs. Service failure severity impacts anger, and referential cohesion, an observable property of text that helps a reader better understand ideas in the text, negatively moderates the relationship between service failure severity and anger. Originality/value The authors are among the first to empirically test the effect of emotional contagion in a user-generated content context, demonstrating that it can occur when consumers read such content, even if they did not experience the events being described. The research uses a self-report and physiological measures to assess consumer perceptions, arousal and emotions related to service failures, increasing the robustness of the literature. These findings contribute to the marketing literature on OCRs in service failures, physiological measures of consumers’ emotions, the negativity bias and emotional contagion in a user-generated content context.

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Rickards, Caroline A., Nisarg Vyas, Kathy L. Ryan, Kevin R. Ward, David Andre, Gennifer M. Hurst, Chelsea R. Barrera, and Victor A. Convertino. "Are you bleeding? Validation of a machine-learning algorithm for determination of blood volume status: application to remote triage." Journal of Applied Physiology 116, no. 5 (March 1, 2014): 486–94. http://dx.doi.org/10.1152/japplphysiol.00012.2013.

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Due to limited remote triage monitoring capabilities, combat medics cannot currently distinguish bleeding soldiers from those engaged in combat unless they have physical access to them. The purpose of this study was to test the hypothesis that low-level physiological signals can be used to develop a machine-learning algorithm for tracking changes in central blood volume that will subsequently distinguish central hypovolemia from physical activity. Twenty-four subjects underwent central hypovolemia via lower body negative pressure (LBNP), and a supine-cycle exercise protocol. Exercise workloads were determined by matching heart rate responses from each LBNP level. Heart rate and stroke volume (SV) were measured via Finometer. ECG, heat flux, skin temperature, galvanic skin response, and two-axis acceleration were obtained from an armband (SenseWear Pro2) and used to develop a machine-learning algorithm to predict changes in SV as an index of central blood volume under both conditions. The algorithm SV was retrospectively compared against Finometer SV. A model was developed to determine whether unknown data points could be correctly classified into these two conditions using leave-one-out cross-validation. Algorithm vs. Finometer SV values were strongly correlated for LBNP in individual subjects (mean r = 0.92; range 0.75–0.98), but only moderately correlated for exercise (mean r = 0.50; range −0.23–0.87). From the first level of LBNP/exercise, the machine-learning algorithm was able to distinguish between LBNP and exercise with high accuracy, sensitivity, and specificity (all ≥90%). In conclusion, a machine-learning algorithm developed from low-level physiological signals could reliably distinguish central hypovolemia from exercise, indicating that this device could provide battlefield remote triage capabilities.

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Strathearn, Carl, and Minhua Ma. "Modelling User Preference for Embodied Artificial Intelligence and Appearance in Realistic Humanoid Robots." Informatics 7, no. 3 (July 31, 2020): 28. http://dx.doi.org/10.3390/informatics7030028.

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Realistic humanoid robots (RHRs) with embodied artificial intelligence (EAI) have numerous applications in society as the human face is the most natural interface for communication and the human body the most effective form for traversing the manmade areas of the planet. Thus, developing RHRs with high degrees of human-likeness provides a life-like vessel for humans to physically and naturally interact with technology in a manner insurmountable to any other form of non-biological human emulation. This study outlines a human–robot interaction (HRI) experiment employing two automated RHRs with a contrasting appearance and personality. The selective sample group employed in this study is composed of 20 individuals, categorised by age and gender for a diverse statistical analysis. Galvanic skin response, facial expression analysis, and AI analytics permitted cross-analysis of biometric and AI data with participant testimonies to reify the results. This study concludes that younger test subjects preferred HRI with a younger-looking RHR and the more senior age group with an older looking RHR. Moreover, the female test group preferred HRI with an RHR with a younger appearance and male subjects with an older looking RHR. This research is useful for modelling the appearance and personality of RHRs with EAI for specific jobs such as care for the elderly and social companions for the young, isolated, and vulnerable.

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Tartas, Maylis, Philippe Bouyé, Audrey Koïtka, Sylvain Durand, Yves Gallois, Jean Louis Saumet, and Pierre Abraham. "Early vasodilator response to anodal current application in human is not impaired by cyclooxygenase-2 blockade." American Journal of Physiology-Heart and Circulatory Physiology 288, no. 4 (April 2005): H1668—H1673. http://dx.doi.org/10.1152/ajpheart.00415.2004.

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It is generally acknowledged that cutaneous vasodilatation in response to monopolar galvanic current application would result from an axon reflex in primary afferent fibers and the neurogenic inflammation resulting from neuropeptide release. Previous studies suggested participation of prostaglandin (PG) in anodal current-induced cutaneous vasodilatation. Thus the inducible cyclooxygenase (COX) isoform (COX-2), assumed to play a key role in inflammation, should be involved in the synthesis of the PG that is released. Skin blood flow (SkBF) variations induced by 5 min of 0.1-mA monopolar anodal current application were evaluated with laser-Doppler flowmetry on the forearm of healthy volunteers treated with indomethacin (COX-1 and COX-2 inhibitor), celecoxib (COX-2 inhibitor), or placebo. SkBF was indexed as cutaneous vascular conductance (CVC), expressed as percentage of heat-induced maximal CVC (%MVC). Urinalyses were performed to test celecoxib treatment efficiency. No difference was found in CVC values at rest: 14.3 ± 4.0, 11.9 ± 3.2, and 10.9 ± 2.0% MVC after indomethacin, celecoxib, and placebo treatment, respectively. At 10 min after the onset of anodal current application, CVC values were 22.2 ± 4.9% MVC (not significantly different from rest) with indomethacin, 85.7 ± 15.3% MVC ( P < 0.001 vs. rest) with celecoxib, and 70.4 ± 13.1% MVC ( P < 0.001 vs. rest) with placebo. Celecoxib significantly depressed the urinary prostacyclin metabolite 6-keto-PGF1α ( P < 0.05 vs. placebo). Indomethacin, but not celecoxib, significantly inhibited the anodal current-induced vasodilatation. Thus, although they are assumed to result from an axon reflex in primary afferent fibers and neurogenic inflammation, these results suggest that the early anodal current-induced vasodilatation is mainly dependent on COX-1-induced PG synthesis.

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Tonacci, Alessandro, Alessandro Dellabate, Andrea Dieni, Lorenzo Bachi, Francesco Sansone, Raffaele Conte, and Lucia Billeci. "Can Machine Learning Predict Stress Reduction Based on Wearable Sensors’ Data Following Relaxation at Workplace? A Pilot Study." Processes 8, no. 4 (April 10, 2020): 448. http://dx.doi.org/10.3390/pr8040448.

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Nowadays, psychological stress represents a burdensome condition affecting an increasing number of subjects, in turn putting into practice several strategies to cope with this issue, including the administration of relaxation protocols, often performed in non-structured environments, like workplaces, and constrained within short times. Here, we performed a quick relaxation protocol based on a short audio and video, and analyzed physiological signals related to the autonomic nervous system (ANS) activity, including electrocardiogram (ECG) and galvanic skin response (GSR). Based on the features extracted, machine learning was applied to discriminate between subjects benefitting from the protocol and those with negative or no effects. Twenty-four healthy volunteers were enrolled for the protocol, equally and randomly divided into Group A, performing an audio-video + video-only relaxation, and Group B, performing an audio-video + audio-only protocol. From the ANS point of view, Group A subjects displayed a significant difference in the heart rate variability-related parameter SDNN across the test phases, whereas both groups displayed a different GSR response, albeit at different levels, with Group A displaying greater differences across phases with respect to Group B. Overall, the majority of the volunteers enrolled self-reported an improvement of their well-being status, according to structured questionnaires. The use of neural networks helped in discriminating those with a positive effect of the relaxation protocol from those with a negative/neutral impact based on basal autonomic features with a 79.2% accuracy. The results obtained demonstrated a significant heterogeneity in autonomic effects of the relaxation, highlighting the importance of maintaining a structured, well-defined protocol to produce significant benefits at the ANS level. Machine learning approaches can be useful to predict the outcome of such protocols, therefore providing subjects less prone to positive responses with personalized advice that could improve the effect of such protocols on self-relaxation perception.

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Markiewicz, Renata, and Beata Dobrowolska. "Reinforcement of Self-Regulated Brain Activity in Schizophrenia Patients Undergoing Rehabilitation." BioMed Research International 2021 (March 26, 2021): 1–9. http://dx.doi.org/10.1155/2021/8030485.

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The experiment was aimed to compare the effects of different forms of rehabilitation applied in patients with schizophrenia. Verification of the obtained results was based on the analysis of the level of cognitive and social functioning of the subjects. For this purpose, the following clinical tools were used: Positive and Negative Syndrome Scale (PANSS), Beck Cognitive Insight Scale (BCIS), Color Trial Test (CTT-1, CTT-2), d2 psychological tests, Acceptance of Illness Scale (AIS), Self-efficacy Scale (GSES), Quantitative Electroencephalogram Biofeedback (QEEG-BF), auditory event-related potentials (ERPs), and serum levels of brain-derived neurotrophic factor (BDNF). The subjects were mentally stable male schizophrenia patients who had been in remission. They were divided into two groups which received different types of rehabilitation for three months. Group 1 patients followed a standard rehabilitation and Group 2 patients received GSR Biofeedback (galvanic skin response Biofeedback, GSR-BF) training. Pretherapy and posttherapy measurements were made for each group. Experimental rehabilitation based on GSR-BF training resulted in regulatory control of neurophysiological mechanisms, and the parameters obtained demonstrated improvement in the subjects’ cognitive and social function. The following therapy outcomes were observed: (1) reduce psychopathological symptoms (2) improving cognitive (concentration, attention) and social functions (3) increase in the neurotrophic factor BDNF. GSR-BF can be used as an alternative to conventional rehabilitation in schizophrenia patients.

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Markiewicz, Renata, and Beata Dobrowolska. "Cognitive and Social Rehabilitation in Schizophrenia—From Neurophysiology to Neuromodulation. Pilot Study." International Journal of Environmental Research and Public Health 17, no. 11 (June 5, 2020): 4034. http://dx.doi.org/10.3390/ijerph17114034.

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The aim of this pilot study was to analyse the influence of Galvanic Skin Response (GSR) Biofeedback training in a group of 18 men with schizophrenia at the remission stage. The results were verified according to: Positive and Negative Syndrome Scale (PANSS), Acceptance of Illness Scale (AIS), Self-efficacy Scale (GSES), Beck Cognitive Insight Scale (BCIS) scales, Colour Trial Test (CTT-1, CTT-2), d2 psychological tests, Quantitative Electroencephalogram (QEEG) Biofeedback, auditory event-related potentials (ERPs), and serum levels of brain-derived neurotrophic factor (BDNF). The results were compared in the same patients after 3 months. Statistically significant changes were noted in results for the variables on the PANSS scale. For the BDNF variable, a statistically significant increase occurred, indicating that GSR Biofeedback training may influence serum levels of the neurotrophic factor. Statistically significant changes were noted in results for the variables on the BCIS, AIS, and GSES indicating an improvement in the cognitive and social functioning. Changes were noted for results for theta/beta and theta/Sensory Motor Rhythm (SMR) ratios, which indicate an improvement in concentration and attention. Changes were noted for the N1 wave amplitude in the frontal brain region (F-z), and for the P2 wave latency in the central brain region (C-z), which indicates an improvement in the initial perceptual analysis. The use of GSR Biofeedback in a group of patients with schizophrenia gives interesting results, but requires further in-depth research.

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Bălan, Oana, Gabriela Moise, Alin Moldoveanu, Marius Leordeanu, and Florica Moldoveanu. "An Investigation of Various Machine and Deep Learning Techniques Applied in Automatic Fear Level Detection and Acrophobia Virtual Therapy." Sensors 20, no. 2 (January 15, 2020): 496. http://dx.doi.org/10.3390/s20020496.

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In this paper, we investigate various machine learning classifiers used in our Virtual Reality (VR) system for treating acrophobia. The system automatically estimates fear level based on multimodal sensory data and a self-reported emotion assessment. There are two modalities of expressing fear ratings: the 2-choice scale, where 0 represents relaxation and 1 stands for fear; and the 4-choice scale, with the following correspondence: 0—relaxation, 1—low fear, 2—medium fear and 3—high fear. A set of features was extracted from the sensory signals using various metrics that quantify brain (electroencephalogram—EEG) and physiological linear and non-linear dynamics (Heart Rate—HR and Galvanic Skin Response—GSR). The novelty consists in the automatic adaptation of exposure scenario according to the subject’s affective state. We acquired data from acrophobic subjects who had undergone an in vivo pre-therapy exposure session, followed by a Virtual Reality therapy and an in vivo evaluation procedure. Various machine and deep learning classifiers were implemented and tested, with and without feature selection, in both a user-dependent and user-independent fashion. The results showed a very high cross-validation accuracy on the training set and good test accuracies, ranging from 42.5% to 89.5%. The most important features of fear level classification were GSR, HR and the values of the EEG in the beta frequency range. For determining the next exposure scenario, a dominant role was played by the target fear level, a parameter computed by taking into account the patient’s estimated fear level.

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Seo, Jungryul, Teemu H. Laine, and Kyung-Ah Sohn. "An Exploration of Machine Learning Methods for Robust Boredom Classification Using EEG and GSR Data." Sensors 19, no. 20 (October 20, 2019): 4561. http://dx.doi.org/10.3390/s19204561.

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In recent years, affective computing has been actively researched to provide a higher level of emotion-awareness. Numerous studies have been conducted to detect the user’s emotions from physiological data. Among a myriad of target emotions, boredom, in particular, has been suggested to cause not only medical issues but also challenges in various facets of daily life. However, to the best of our knowledge, no previous studies have used electroencephalography (EEG) and galvanic skin response (GSR) together for boredom classification, although these data have potential features for emotion classification. To investigate the combined effect of these features on boredom classification, we collected EEG and GSR data from 28 participants using off-the-shelf sensors. During data acquisition, we used a set of stimuli comprising a video clip designed to elicit boredom and two other video clips of entertaining content. The collected samples were labeled based on the participants’ questionnaire-based testimonies on experienced boredom levels. Using the collected data, we initially trained 30 models with 19 machine learning algorithms and selected the top three candidate classifiers. After tuning the hyperparameters, we validated the final models through 1000 iterations of 10-fold cross validation to increase the robustness of the test results. Our results indicated that a Multilayer Perceptron model performed the best with a mean accuracy of 79.98% (AUC: 0.781). It also revealed the correlation between boredom and the combined features of EEG and GSR. These results can be useful for building accurate affective computing systems and understanding the physiological properties of boredom.

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Cummins, Pearl N., James Kent, Timothy Weng, Vincent Magnottta, Gary Pierce, and Michelle Voss. "EXERCISE ACUTELY IMPROVES COGNITION IN HEALTHY OLDER ADULTS: THE ROLE OF AROUSAL." Innovation in Aging 3, Supplement_1 (November 2019): S836. http://dx.doi.org/10.1093/geroni/igz038.3079.

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Abstract Previous researchers have reported that aerobic exercise improves cognition in older adults; however, few researchers have examined the role of arousal on improvements in cognition after exercise. The purpose of this study was to understand how changes in arousal acutely affect changes in cognitive performance after a single session of light compared to moderate intensity aerobic exercise. Cognitively normal older adults (N = 34) were enrolled in a randomized controlled trial where they were asked to complete the N-back task with faces, a cognitive task used to test working memory, in an fMRI scanner. On separate days, the task was completed before and 15 to 20 minutes after light and moderate intensity exercise. An intervention was also completed, but our question focuses on the acute effects of exercise rather than training. Arousal was measured before and after exercise through a questionnaire and a direct measure of physiological activation of the sympathetic nervous system with galvanic skin response (GSR). On average, resting GSRs decreased from pre- to post-exercise scan; however, the change was not statistically significant. The decrease in arousal after light exercise indicated that older adults had decreased sympathetic activity after both light and moderate intensity exercise. By contrast, N-back task performance improved most after moderate compared to light intensity exercise. Together, evidence that sympathetic activity tended to decrease generally for both intensities, whereas cognitive improvements were more specific, suggests that changes in arousal at rest were not a critical factor connecting exercise and improved working memory in this study.

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Kim, Hodam, Laehyun Kim, and Chang-Hwan Im. "Machine-Learning-Based Detection of Craving for Gaming Using Multimodal Physiological Signals: Validation of Test-Retest Reliability for Practical Use." Sensors 19, no. 16 (August 9, 2019): 3475. http://dx.doi.org/10.3390/s19163475.

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Internet gaming disorder in adolescents and young adults has become an increasing public concern because of its high prevalence rate and potential risk of alteration of brain functions and organizations. Cue exposure therapy is designed for reducing or maintaining craving, a core factor of relapse of addiction, and is extensively employed in addiction treatment. In a previous study, we proposed a machine-learning-based method to detect craving for gaming using multimodal physiological signals including photoplethysmogram, galvanic skin response, and electrooculogram. Our previous study demonstrated that a craving for gaming could be detected with a fairly high accuracy; however, as the feature vectors for the machine-learning-based detection of the craving of a user were selected based on the physiological data of the user that were recorded on the same day, the effectiveness of the reuse of the machine learning model constructed during the previous experiments, without any further calibration sessions, was still questionable. This “high test-retest reliability” characteristic is of importance for the practical use of the craving detection system because the system needs to be repeatedly applied to the treatment processes as a tool to monitor the efficacy of the treatment. We presented short video clips of three addictive games to nine participants, during which various physiological signals were recorded. This experiment was repeated with different video clips on three different days. Initially, we investigated the test-retest reliability of 14 features used in a craving detection system by computing the intraclass correlation coefficient. Then, we classified whether each participant experienced a craving for gaming in the third experiment using various classifiers—the support vector machine, k-nearest neighbors (kNN), centroid displacement-based kNN, linear discriminant analysis, and random forest—trained with the physiological signals recorded during the first or second experiment. Consequently, the craving/non-craving states in the third experiment were classified with an accuracy that was comparable to that achieved using the data of the same day; thus, demonstrating a high test-retest reliability and the practicality of our craving detection method. In addition, the classification performance was further enhanced by using both datasets of the first and second experiments to train the classifiers, suggesting that an individually customized game craving detection system with high accuracy can be implemented by accumulating datasets recorded on different days under different experimental conditions.

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Widacki, Jan. "Discoverers of the Galvanic Skin Response." European Polygraph 9, no. 4 (December 1, 2015): 209–20. http://dx.doi.org/10.1515/ep-2015-0008.

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Zahed, Karim, Farzan Sasangohar, Ranjana Mehta, Madhav Erraguntla, and Khalid Qaraqe. "Diabetes Management Experience and the State of Hypoglycemia: National Online Survey Study." JMIR Diabetes 5, no. 2 (June 17, 2020): e17890. http://dx.doi.org/10.2196/17890.

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Background Hypoglycemia, or low blood sugar levels, in people with diabetes can be a serious life-threatening condition, and serious outcomes can be avoided if low levels of blood sugar are proactively detected. Although technologies exist to detect the onset of hypoglycemia, they are invasive or costly or exhibit a high incidence of false alarms. Tremors are commonly reported symptoms of hypoglycemia and may be used to detect hypoglycemic events, yet their onset is not well researched or understood. Objective This study aimed to understand diabetic patients’ perceptions of hypoglycemic tremors, as well as their user experiences with technology to manage diabetes, and expectations from a self-management tool to ultimately inform the design of a noninvasive and cost-effective technology that detects tremors associated with hypoglycemia. Methods A cross-sectional internet panel survey was administered to adult patients with type 1 diabetes using the Qualtrics platform in May 2019. The questions focused on 3 main constructs: (1) perceived experiences of hypoglycemia, (2) experiences and expectations about a diabetes management device and mobile app, and (3) beliefs and attitudes regarding intention to use a diabetes management device. The analysis in this paper focuses on the first two constructs. Nonparametric tests were used to analyze the Likert scale data, with a Mann-Whitney U test, Kruskal-Wallis test, and Games-Howell post hoc test as applicable, for subgroup comparisons to highlight differences in perceived frequency, severity, and noticeability of hypoglycemic tremors across age, gender, years living with diabetes, and physical activity. Results Data from 212 respondents (129 [60.8%] females) revealed statistically significant differences in perceived noticeability of tremors by gender, whereby males noticed their tremors more (P<.001), and age, with the older population reporting lower noticeability than the young and middle age groups (P<.001). Individuals living longer with diabetes noticed their tremors significantly less than those with diabetes for ≤1 year but not in terms of frequency or severity. Additionally, the majority of our participants (150/212, 70.7%) reported experience with diabetes-monitoring devices. Conclusions Our findings support the need for cost-efficient and noninvasive continuous monitoring technologies. Although hypoglycemic tremors were perceived to occur frequently, such tremors were not found to be severe compared with other symptoms such as sweating, which was the highest rated symptom in our study. Using a combination of tremor and galvanic skin response sensors may show promise in detecting the onset of hypoglycemic events.

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Hu, Yan, Majed Elwardy, and Hans-Jürgen Zepernick. "On the Effect of Standing and Seated Viewing of 360° Videos on Subjective Quality Assessment: A Pilot Study." Computers 10, no. 6 (June 12, 2021): 80. http://dx.doi.org/10.3390/computers10060080.

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Due to the advances in head-mounted displays (HMDs), hardware and software technologies, and mobile connectivity, virtual reality (VR) applications such as viewing 360° videos on HMDs have seen an increased interest in a wide range of consumer and vertical markets. Quality assessment of digital media systems and services related to immersive visual stimuli has been one of the challenging problems of multimedia signal processing. Specifically, subjective quality assessment of 360° videos presented on HMDs is needed to obtain a ground truth on the visual quality as perceived by humans. Standardized test methodologies to assess the subjective quality of 360° videos on HMDs are currently not as developed as for conventional videos and are subject to further study. In addition, subjective tests related to quality assessment of 360° videos are commonly conducted with participants seated on a chair but neglect other options of consumption such as standing viewing. In this paper, we compare the effect that standing and seated viewing of 360° videos on an HMD has on subjective quality assessment. A pilot study was conducted to obtain psychophysical and psychophysiological data that covers explicit and implicit responses of the participants to the shown 360° video stimuli with different quality levels. The statistical analysis of the data gathered in the pilot study is reported in terms of average rating times, mean opinion scores, standard deviation of opinion scores, head movements, pupil diameter, galvanic skin response (GSR), and simulator sickness scores. The results indicate that the average rating times consumed for 360° video quality assessment are similar for standing and seated viewing. Further, the participants showed higher resolving power among different 360° video quality levels and were more confident about the given opinion scores for seated viewing. On the other hand, a larger scene exploration of 360° videos was observed for standing viewing which appears to distract from the quality assessment task. A slightly higher pupil dilation was recorded for standing viewing which suggests a slightly more immersed experience compared to seated viewing. GSR data indicate a lower degree of emotional arousal in seated viewing which seems to allow the participants to better conduct the quality assessment task. Similarly, simulator sickness symptoms are kept significantly lower when seated. The pilot study also contributes to a holistic view of subjective quality assessment and provides indicative ground truth that can guide the design of large-scale subjective tests.

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Marchenko, Alla, Alenka Temeljotov-Salaj, Victor Rizzardi, and Odne Oksavik. "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." Applied Sciences 10, no. 20 (October 19, 2020): 7315. http://dx.doi.org/10.3390/app10207315.

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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.

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Dascălu, Loredana, Oana-Roxana Chivu, Claudiu Babiș, Anamaria Feier, and Alin Ion Țăpîrdea. "MEASUREMENTS OF GALVANIC SKIN RESPONSE WITH ACCELEROMETER GLOVE." Journal of Research and Innovation for Sustainable Society 2, no. 2 (December 2, 2020): 21–26. http://dx.doi.org/10.33727/jriss.2020.2.3:21-26.

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Setyohadi, Djoko Budiyanto, Sri Kusrohmaniah, Sebastian Bagya Gunawan, Pranowo Pranowo, and Anton Satria Prabuwono. "Galvanic Skin Response Data Classification for Emotion Detection." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (October 1, 2018): 4004. http://dx.doi.org/10.11591/ijece.v8i5.pp4004-4014.

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Emotion detection is a very exhausting job and needs a complicated process; moreover, these processes also require the proper data training and appropriate algorithm. The process involves the experimental research in psychological experiment and classification methods. This paper describes a method on detection emotion using Galvanic Skin Response (GSR) data. We used the Positive and Negative Affect Schedule (PANAS) method to get a good data training. Furthermore, Support Vector Machine and a correct preprocessing are performed to classify the GSR data. To validate the proposed approach, Receiver Operating Characteristic (ROC) curve, and accuracy measurement are used. Our method shows that the accuracy is about 75.65% while ROC is about 0.8019. It means that the emotion detection can be done satisfactorily and well performed.

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Boller, F., N. C. Keefe, and P. Zoccolotti. "Luigi Galvani, body electricity, and the 'galvanic skin response'." Neurology 39, no. 6 (June 1, 1989): 868. http://dx.doi.org/10.1212/wnl.39.6.868.

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Nishigawa, G., N. Natsuaki, Y. Maruo, M. Okamoto, and S. Minagi. "Galvanic skin response of oral cancer patients during speech." Journal of Oral Rehabilitation 30, no. 5 (May 2003): 522–25. http://dx.doi.org/10.1046/j.1365-2842.2003.01131.x.

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Navea, Roy Francis, Paul John Buenvenida, and Christian Dave Cruz. "Stress Detection using Galvanic Skin Response: An Android Application." Journal of Physics: Conference Series 1372 (November 2019): 012001. http://dx.doi.org/10.1088/1742-6596/1372/1/012001.

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Gogate, Uttara Dhananjay, and Dr Jagdish Wasudeorao Bakal. "Hunger and stress monitoring system using galvanic skin response." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 3 (March 1, 2019): 861. http://dx.doi.org/10.11591/ijeecs.v13.i3.pp861-865.

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In the world, roughly more than 3 million people are affected by muscle power loss diseases which cause lack of communication. Further, this lack of communication is a severe problem with hunger and stress, which sometimes proves to be fatal. It is essential to monitor physical parameters such as hunger and mental stress of the patient. To observe hunger and stress, galvanic skin response (GSR) sensor is a most preferred method because it measures the conductance of skin due to sweating. We have designed new hunger and stress monitoring system (HS-MS) that helps patients and their caretakers to monitor stress and hunger level. HS-MS is two electrodes system that attaches to two fingers (index and middle) of the patient and monitors starvation and tension. According to the response of the sensor, caretakers can identify hunger and even the mental stress conditions of the patient. In all, 35 patients were examined using HS-MS system and 20 of them were used as a control. Overall system accuracy was found out to be 86.6% and response time of HS-MS 5 seconds. Sensitivity with respect to hunger and stress was about 99.9% and 73.3% respectively. Thus HS-MS can be identified as a helpful tool to provide comfort to the patient easily and can be used in hospitals or homes with lonely elderly people.

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Dascălu, Loredana Cristina, Claudiu Babiș, Oana Chivu, Gabriel Iacobescu, Ana Maria Alecusan, and Augustin Semenescu. "Measurements of Galvanic Skin Response on Subjects Affected by Stress." Acta Universitatis Cibiniensis. Technical Series 71, no. 1 (December 1, 2019): 25–29. http://dx.doi.org/10.2478/aucts-2019-0006.

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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.

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원규왕 and CHAKyungWhan. "Utilizing Galvanic Skin Response (GSR) for Measuring English Listening Anxiety." English Language Teaching 27, no. 2 (June 2015): 39–55. http://dx.doi.org/10.17936/pkelt.2015.27.2.003.

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Muller, Joop, Wim Pet, Ellen ReatschPet, Riek Servaas, Femke Ansems, Daniel Schwander, Gary Firer, Harald Lothaller, and P. C. Endler. "Repeatability of Measurements of Galvanic Skin Response – A Pilot Study." Open Complementary Medicine Journal 5, no. 1 (May 3, 2014): 11–17. http://dx.doi.org/10.2174/1876391x01305010011.

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Sun, Xiao, Tao Hong, Changliang Li, and Fuji Ren. "Hybrid spatiotemporal models for sentiment classification via galvanic skin response." Neurocomputing 358 (September 2019): 385–400. http://dx.doi.org/10.1016/j.neucom.2019.05.061.

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Tsutsumi, Takeshi, and Atsushi Komatsuzaki. "An adequate parameter evaluating the galvanic body sway test: comparison with the caloric test in patients with vestibular schwannomas." Journal of Laryngology & Otology 113, no. 2 (February 1999): 118–21. http://dx.doi.org/10.1017/s002221510014335x.

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AbstractIt has been reported that the galvanic body sway test does not correlate with the caloric test. We evaluated the galvanic body sway test in patients with vestibular schwannomas using three parameters: the angle of deviation response onset, the maximum value of the deviation response, and the area of deviation. These parameters reflect velocity, position, and locus of the centre of pressure respectively. Among these parameters, only the angle of deviation response onset showed unilateral weakness of the response correlating with the canal paresis value, which indicates that velocity is responsible for conduction in the vestibular nerve. However, the galvanic body sway test is apt to be preferred to the caloric test. This might be attributed to the decreased sensitivity of this test.

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Martinez, Raquel, Asier Salazar-Ramirez, Andoni Arruti, Eloy Irigoyen, Jose Ignacio Martin, and Javier Muguerza. "A Self-Paced Relaxation Response Detection System Based on Galvanic Skin Response Analysis." IEEE Access 7 (2019): 43730–41. http://dx.doi.org/10.1109/access.2019.2908445.

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Mohanraj, Saravanan, and Krishnamurthy Narayanan. "SYMPATHETIC SKIN RESPONSE AND GALVANIC SKIN RESISTANCE IN MALES WITH TYPE 2 DIABETES MELLITUS." Journal of Evidence Based Medicine and Healthcare 3, no. 50 (June 22, 2016): 2544–49. http://dx.doi.org/10.18410/jebmh/2016/560.

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Makone, Hemant Bhaskar, G. R. Naghate, and Syeda Afroz. "Effect of Rajyoga meditation on galvanic skin response: Cross-sectional study." MedPulse International Journal of Physiology 9, no. 1 (2019): 01–03. http://dx.doi.org/10.26611/103911.

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Park, Sun Hwa, Jihye Park, Han Nah Park, Hyun Min Park, and Jae Yong Song. "Flexible galvanic skin response sensor based on vertically aligned silver nanowires." Sensors and Actuators B: Chemical 273 (November 2018): 804–8. http://dx.doi.org/10.1016/j.snb.2018.06.125.

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Journal articles on the topic 'Galvanic skin response test'

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