Skin Admittance Measurement for Emotion Recognition: A Study over Frequency Sweep

Transcutaneous auricular Vagus Nerve Stimulation and Median Nerve Stimulation reduce acute stress in young healthy adults: a single-blind sham-controlled crossover study

Stress is a major determinant of health and wellbeing. Conventional stress management approaches do not account for the daily-living acute changes in stress that affect quality of life. The combination of physiological monitoring and non-invasive Peripheral Nerve Stimulation (PNS) represents a promising technological approach to quantify stress-induced physiological manifestations and reduce stress during everyday life. This study aimed to evaluate the effectiveness of three well-established transcutaneous PNS modalities in reducing physiological manifestations of stress compared to a sham: auricular and cervical Vagus Nerve Stimulation (taVNS and tcVNS), and Median Nerve Stimulation (tMNS). Using a single-blind sham-controlled crossover study with four visits, we compared the stress mitigation effectiveness of taVNS, tcVNS, and tMNS, quantified through physiological markers derived from five physiological signals peripherally measured on 19 young healthy volunteers. Participants underwent three acute mental and physiological stressors while receiving stimulation. Blinding effectiveness was assessed via subjective survey. taVNS and tMNS relative to sham resulted in significant changes that suggest a reduction in sympathetic outflow following the acute stressors: Left Ventricular Ejection Time Index (LVETI) shortening (tMNS: p = 0.007, taVNS: p = 0.015) and Pre-Ejection Period (PEP)-to-LVET ratio (PEP/LVET) increase (tMNS: p = 0.044, taVNS: p = 0.029). tMNS relative to sham also reduced Pulse Pressure (PP; p = 0.032) and tonic EDA activity (tonicMean; p = 0.025). The nonsignificant blinding survey results suggest these effects were not influenced by placebo. taVNS and tMNS effectively reduced stress-induced sympathetic arousal in wearable-compatible physiological signals, motivating their future use in novel personalized stress therapies to improve quality of life.

Current trends and opportunities in the methodology of electrodermal activity measurement

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

A Wearable Head Mounted Display Bio-Signals Pad System for Emotion Recognition

Multimodal bio-signals acquisition based on wearable devices and using virtual reality (VR) as stimulus source are promising techniques in emotion recognition research field. Numerous studies have shown that emotional states can be better evoked through Immersive Virtual Environments (IVE). The main goal of this paper is to provide researchers with a system for emotion recognition in VR environments. In this paper, we present a wearable forehead bio-signals acquisition pad which is attached to Head-Mounted Displays (HMD), termed HMD Bio Pad. This system can simultaneously record emotion-related two-channel electroencephalography (EEG), one-channel electrodermal activity (EDA), photoplethysmograph (PPG) and skin temperature (SKT) signals. In addition, we develop a human-computer interaction (HCI) interface which researchers can carry out emotion recognition research using VR HMD as stimulus presentation device. To evaluate the performance of the proposed system, we conducted different experiments to validate the multimodal bio-signals quality, respectively. To validate EEG signal, we have assessed the performance in terms of EEG eyes-blink task and eyes-open and eyes-closed task. The EEG eyes-blink task indicates that the proposed system can achieve comparable EEG signal quality in comparison to the dedicated bio-signals measuring device. The eyes-open and eyes-closed task proves that the proposed system can efficiently record alpha rhythm. Then we used signal-to-noise ratio (SNR) and Skin Conductance Reaction (SCR) signal to validate the performance for EDA acquisition system. A filtered EDA signal, with a high mean SNR of 28.52 dB, is plotted on HCI interface. Moreover, the SCR signal related to stimulus response can be correctly extracted from EDA signal. The SKT acquisition system has been validated effectively by the temperature change experiment when subjects are in unpleasant emotion. The pulse rate (PR) estimated from PPG signal achieved the low mean average absolute error (AAE), which is 1.12 beats per minute (BPM) over 8 recordings. In summary, the proposed HMD Bio Pad offers a portable, comfortable and easy-to-wear device for recording bio-signals. The proposed system could contribute to emotion recognition research in VR environments.

HRV analysis: a non-invasive approach to discriminate between newborns with and without seizures. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021;2021:52-55. [PMID: 34891237 DOI: 10.1109/embc46164.2021.9629741]

Early neonatal seizures detection is one of the most challenging issues in Neonatal Intensive Care Units. Several EEG-based Neonatal Seizure Detectors were proposed to support the clinical staff. However, less invasive and more easily interpretable methods than EEG are still missing. In this work, we investigated if Heart Rate Variability analysis and related measures as input features of supervised classifiers could be a valid support for discriminating between newborns with seizures and seizure-free ones. The proposed methods were validated on 52 subjects (33 with seizures and 19 seizure-free) of a public dataset collected at the Helsinki University Hospital. Encouraging results are achieved using a Linear Support Vector Machine, obtaining about 87% Area Under ROC Curve. This suggests that Heart Rate Variability analysis might be a non-invasive pre-screening tool to identify newborns with seizures.Clinical Relevance- Heart Rate Variability analysis for detecting newborns with seizures in NICUs could speed up the diagnosis process and appropriate treatments for a better neurodevelopmental outcome of the infant.

Experience with an Affective Robot Assistant for Children with Hearing Disabilities

This study presents an assistive robotic system enhanced with emotion recognition capabilities for children with hearing disabilities. The system is designed and developed for the audiometry tests and rehabilitation of children in a clinical setting and includes a social humanoid robot (Pepper), an interactive interface, gamified audiometry tests, sensory setup and a machine/deep learning based emotion recognition module. Three scenarios involving conventional setup, tablet setup and setup with the robot+tablet are evaluated with 16 children having cochlear implant or hearing aid. Several machine learning techniques and deep learning models are used for the classification of the three test setups and for the classification of the emotions (pleasant, neutral, unpleasant) of children using the recorded physiological signals by E4 wristband. The results show that the collected signals during the tests can be separated successfully and the positive and negative emotions of children can be better distinguished when they interact with the robot than in the other two setups. In addition, the children’s objective and subjective evaluations as well as their impressions about the robot and its emotional behaviors are analyzed and discussed extensively.

The Concept of Advanced Multi-Sensor Monitoring of Human Stress

Many people live under stressful conditions which has an adverse effect on their health. Human stress, especially long-term one, can lead to a serious illness. Therefore, monitoring of human stress influence can be very useful. We can monitor stress in strictly controlled laboratory conditions, but it is time-consuming and does not capture reactions, on everyday stressors or in natural environment using wearable sensors, but with limited accuracy. Therefore, we began to analyze the current state of promising wearable stress-meters and the latest advances in the record of related physiological variables. Based on these results, we present the concept of an accurate, reliable and easier to use telemedicine device for long-term monitoring of people in a real life. In our concept, we ratify with two synchronized devices, one on the finger and the second on the chest. The results will be obtained from several physiological variables including electrodermal activity, heart rate and respiration, body temperature, blood pressure and others. All these variables will be measured using a coherent multi-sensors device. Our goal is to show possibilities and trends towards the production of new telemedicine equipment and thus, opening the door to a widespread application of human stress-meters.

Human Emotion Recognition: Review of Sensors and Methods

Automated emotion recognition (AEE) is an important issue in various fields of activities which use human emotional reactions as a signal for marketing, technical equipment, or human-robot interaction. This paper analyzes scientific research and technical papers for sensor use analysis, among various methods implemented or researched. This paper covers a few classes of sensors, using contactless methods as well as contact and skin-penetrating electrodes for human emotion detection and the measurement of their intensity. The results of the analysis performed in this paper present applicable methods for each type of emotion and their intensity and propose their classification. The classification of emotion sensors is presented to reveal area of application and expected outcomes from each method, as well as their limitations. This paper should be relevant for researchers using human emotion evaluation and analysis, when there is a need to choose a proper method for their purposes or to find alternative decisions. Based on the analyzed human emotion recognition sensors and methods, we developed some practical applications for humanizing the Internet of Things (IoT) and affective computing systems.

Combining Electrodermal Activity and Speech Analysis towards a more Accurate Emotion Recognition System

Current research in the emotion recognition field is exploring the possibility of merging the information from physiological signals, behavioural data, and speech. Electrodermal activity (EDA) is amongst the main psychophysiological arousal indicators. Nonetheless, it is quite difficult to be analyzed in ecological scenarios, like, for instance, when the subject is speaking. On the other hand, speech carries relevant information of subject emotional state and its potential in the field of affective computing is still to be fully exploited. In this work, we aim at exploring the possibility of merging the information from electrodermal activity (EDA) and speech to improve the recognition of human arousal level during the pronunciation of single affective words. Unlike the majority of studies in the literature, we focus on speakers' arousal rather than the emotion conveyed by the spoken word. Specifically, a support vector machine with recursive feature elimination strategy (SVM-RFE) is trained and tested on three datasets, i.e using the two channels (i.e., speech and EDA) separately and then jointly. The results show that the merging of EDA and speech information significantly improves the marginal classifier (+11.64%). The six selected features by the RFE procedure will be used for the development of a future multivariate model of emotions.

Brain Dynamics Induced by Pleasant/Unpleasant Tactile Stimuli Conveyed by Different Fabrics

In this study, we investigated brain dynamics from electroencephalographic (EEG) signals during affective tactile stimulation conveyed by the dynamical contact with different fabrics. Thirty-three healthy subjects (16 females) were enrolled to interact with a haptic device able to mimic caress-like stimuli conveyed by strips of different fabrics moved back and forth at different velocities. Specifically, two velocity levels (i.e., 9.4 and 65 mm/sec) and two kinds of fabric (i.e., burlap and silk) were selected to deliver pleasant and unpleasant affective elicitations, according to subjects' self-assessment. EEG power spectra and functional connectivity were then calculated and analyzed. Experimental results, reported in terms of p-value topographic maps, demonstrated that caresses administered through unpleasant fabrics increased brain activity in the θ (4-8 Hz), α (8-14 Hz), and β (14-30 Hz) bands, whereas the use of pleasant fabrics enhanced functional connections in specific areas (e.g., frontal, occipital, and temporal cortices) depending on the oscillations frequency and caressing velocity. Furthermore, we adopted K-NN algorithms to automatically recognize the pleasantness of the haptic stimulation at a single-subject level using EEG power spectra, achieving a recognition accuracy up to 74.24%. Finally, we showed how brain oscillation power in the α and β bands over contralateral frontal- and central-cortex were the most informative features characterizing the pleasantness of a tactile stimulus on the forearm.

Breathable Dry Silver/Silver Chloride Electronic Textile Electrodes for Electrodermal Activity Monitoring

The focus of this study is to design and integrate silver/silver chloride (Ag/AgCl) electronic textile (e-textile) electrodes into different textile substrates to evaluate their ability to monitor electrodermal activity (EDA). Ag/AgCl e-textiles were stitched into woven textiles of cotton, nylon, and polyester to function as EDA monitoring electrodes. EDA stimulus responses detected by dry e-textile electrodes at various locations on the hand were compared to the EDA signals collected by dry solid Ag/AgCl electrodes. 4-h EDA data with e-textile and clinically conventional rigid electrodes were compared in relation to skin surface temperature. The woven cotton textile substrate with e-textile electrodes (0.12 cm² surface area, 0.40 cm distance) was the optimal material to detect the EDA stimulus responses with the highest average Pearson correlation coefficient of 0.913 ± 0.041 when placed on the distal phalanx of the middle finger. In addition, differences with EDA waveforms recorded on various fingers were observed. Trends of long-term measurements showed that skin surface temperature affected EDA signals recorded by non-breathable electrodes more than when e-textile electrodes were used. The effective design criteria outlined for e-textile electrodes can promote the development of comfortable and unobtrusive EDA monitoring systems, which can help improve our knowledge of the human neurological system.

Muscle fatigue assessment through electrodermal activity analysis during isometric contraction

We studied the effects of muscle fatigue on the Autonomic Nervous System (ANS) dynamics. Specifically, we monitored the electrodermal activity (EDA) on 32 healthy subjects performing isometric biceps contraction. As assessed by means of an electromyography (EMG) analysis, 15 subjects showed muscle fatigue and 17 did not. EDA signals were analyzed using the recently proposed cvxEDA model in order to decompose them into their phasic and tonic components and extract effective features to study ANS dynamics. A statistical comparison between the two groups of subjects was performed. Results revealed that relevant phasic EDA features significantly increased in the fatigued group. Moreover, a pattern recognition system was applied to the EDA dataset in order to automatically discriminate between fatigued and non-fatigued subjects. The proposed leave-one-subject-out KNN classifier showed an accuracy of 75.69%. These results suggest the use of EDA as correlate of muscle fatigue, providing integrative information to the standard indices extracted from the EMG signals.

Effects of Flexible Dry Electrode Design on Electrodermal Activity Stimulus Response Detection

OBJECTIVE

The focus of this research is to evaluate the effects of design parameters including surface area, distance between and geometry of dry flexible electrodes on electrodermal activity (EDA) stimulus response detection.

METHODS

EDA is a result of the autonomic nervous system being stimulated, which causes sweat and changes the electrical characteristics of the skin. Standard silver/silver chloride (Ag/AgCl) EDA electrodes are rigid and lack conformability in contact with skin. In this study, flexible dry Ag/AgCl EDA electrodes were fabricated on a compliant substrate, used to monitor EDA stimulus responses and compared to results simultaneously collected by rigid dry Ag/AgCl electrodes.

RESULTS

A repeatable fabrication process for flexible Ag/AgCl electrodes has been established. Surface area, distance between and geometry of electrodes are shown to affect the detectability of the EDA response and the minimum number of sweat glands to be covered by the electrodes has been estimated at 140, or more, in order to maintain functionality. The optimal flexible EDA electrode is a serpentine design with a 0.15 cm² surface area and a 0.20 cm distance with an average Pearson correlation coefficient of .

CONCLUSION

Fabrication of flexible electrodes is described and an understanding of the effects of electrode designs on the EDA stimulus response detection has been established and is potentially related to the coverage of sweat glands.

SIGNIFICANCE

This work presents a novel systematic approach to understand the effects of electrode designs on monitoring EDA which is of importance for the design of wearable EDA monitoring devices.

Electrodermal responses to discrete stimuli measured by skin conductance, skin potential, and skin susceptance

BACKGROUND/AIM

Presently, electrodermal activity (EDA) is the preferred term for changes in electrical properties of the skin. Change in the skin conductance responses (SCRs) and skin potential responses (SPRs) due to external stimuli have previously been investigated in a number of studies, but very little for skin susceptance responses (SSRs) recorded simultaneously at the same skin site. This study aimed to investigate the association between the three parameters of EDA, skin conductance (SC), skin potential (SP), and skin susceptance (SS) responses generated by different types of psychological stimuli.

METHODS

SCRs, SPRs, and SSRs were recorded from 20 healthy test subjects simultaneously at the same skin area. EDA responses were induced by five different external stimuli, which were shown in the form of PowerPoint slides on a PC monitor that situated in front of participants.

RESULTS

All stimuli evoked EDA responses, but with significantly different magnitudes, dependent on stimulus type. Both SC and SP waveforms yielded positive responses with respect to the stimuli; however, SS showed negative response and its role was found to be significant at low frequency (20 Hz).

CONCLUSIONS

This study illustrated that different discrete stimuli showed different passive and active electrodermal responses at the same skin site. SCRs, SPRs, and SSRs were dependent on the stimulus type, and the highest response was associated with the sound stimulus, which can be attributed to orienting response or startle reflex. In addition, it was found that the SSRs have a significant contribution at 20 Hz. In spite of a high correlation found between average amplitude values of SCRs and SSRs, no significant association was seen between average amplitudes values of SPRs and SSRs, and between SCRs and SPRs.