Waveform difference between skin conductance and skin potential responses in relation to electrical and evaporative properties of skin

Task-state skin potential abnormalities can distinguish major depressive disorder and bipolar depression from healthy controls

Early detection of bipolar depression (BPD) and major depressive disorder (MDD) has been challenging due to the lack of reliable and easily measurable biological markers. This study aimed to investigate the accuracy of discriminating patients with mood disorders from healthy controls based on task state skin potential characteristics and their correlation with individual indicators of oxidative stress. A total of 77 patients with BPD, 53 patients with MDD, and 79 healthy controls were recruited. A custom-made device, previously shown to be sufficiently accurate, was used to collect skin potential data during six emotion-inducing tasks involving video, pictorial, or textual stimuli. Blood indicators reflecting individual levels of oxidative stress were collected. A discriminant model based on the support vector machine (SVM) algorithm was constructed for discriminant analysis. MDD and BPD patients were found to have abnormal skin potential characteristics on most tasks. The accuracy of the SVM model built with SP features to discriminate MDD patients from healthy controls was 78% (sensitivity 78%, specificity 82%). The SVM model gave an accuracy of 59% (sensitivity 59%, specificity 79%) in classifying BPD patients, MDD patients, and healthy controls into three groups. Significant correlations were also found between oxidative stress indicators in the blood of patients and certain SP features. Patients with depression and bipolar depression have abnormalities in task-state skin potential that partially reflect the pathological mechanism of the illness, and the abnormalities are potential biological markers of affective disorders.

Research on psychophysiological characteristics of construction workers during consciously unsafe behaviors

Workers' unsafe behavior is a primary cause leading to falling accidents on construction sites. This study aimed to explore how to utilize psychophysiological characteristics to predict consciously unsafe behaviors of construction workers. In this paper, a psychological questionnaire was compiled to measure risky psychology, and wireless wearable physiological recorders were employed to real-timely measure the physiological signals of subjects. The psychological and physiological characteristics were identified by correlation analysis and significance test, which were then utilized to develop unsafe behavior prediction models based on multiple linear regression and decision tree regressor. It was revealed that unsafe behavior performance was negatively correlated with task-related risk perception, while positively correlated with hazardous attitude. Subjects experienced remarkable increases in skin conductivity, while notable decreases in the inter-beat interval and skin temperature during consciously unsafe behavior. Both models developed for predicting unsafe behavior were reliably and well-fitted with coefficients of determination higher than 0.8. Whereas, each model exhibited its unique advantages in terms of prediction accuracy and interpretability. Not only could study results contribute to the body of knowledge on intrinsic mechanisms of unsafe behavior, but also provide a theoretical basis for the automatic identification of workers' unsafe behavior.

The Five Basic Human Senses Evoke Electrodermal Activity

Electrodermal activity (EDA) usually relates to variations in the electrical properties of palmar or plantar skin sites. EDA responses, namely skin conductance responses (SCRs), skin potential responses (SPRs) and skin susceptance responses (SSRs) are shown to be sensitive indexes of sympathetic nervous system activation and are studied in many research projects. However, the association between EDA responses and the five basic human senses has not been investigated yet. Our study aimed to explore the relationship between the three EDA responses (SCRs, SSRs and SPRs) and the five basic human senses. These three EDA responses were measured simultaneously at the same skin site on each of the 38 volunteers. The tested five senses were sight, hearing, touch, taste and smell. The results showed that the different tested senses led to different degrees of EDA responses due to activation of the sympathetic nervous system and corresponding secretion of sweat. Although a controlled study on the degree of EDA as a function of the strength of each stimulus was not performed, we noted that the largest EDA responses were typically associated with the smell sense test. We conclude that EDA responses could be utilized as measures for examining the sensitivity of the human senses. Hence, EDA devices may have important roles in sensory systems for future clinical applications.

The Impact of Diclofenac Gel on Ion Transport in the Rabbit (Oryctolagus cuniculus) Skin: An In Vitro Study

Diclofenac belongs to the non-steroidal anti-inflammatory drugs with analgesic, antipyretic and anti-inflammatory effects. Diclofenac administration on the skin may be associated with the appearance of side effects. The study aimed to evaluate the impact of diclofenac gel on transepithelial electrophysiological parameters of the 55 rabbit abdomen skin specimens. The electric parameters were analyzed in a modified Ussing chamber. The resistance (R) of the skin specimens treated with diclofenac gel significantly increased, which could be related to the reduction in the water content in intercellular spaces and, consequently, tighter adhesion of the cells. Increased electric potential (PD) was also observed in the skin specimens treated with diclofenac gel. The increase in both R and PD measured under stationary conditions was most likely caused by a transient and reversible increase in sodium ion transport, as the R and PD values decreased after the diclofenac gel was washed away. However, diclofenac gel did not affect the maximum and minimum PDs measured during stimulations. Therefore, it seems that diclofenac gel does not affect the perception of stimuli in the model system used.

Physiological characterization of electrodermal activity enables scalable near real-time autonomic nervous system activation inference

Electrodermal activities (EDA) are any electrical phxenomena observed on the skin. Skin conductance (SC), a measure of EDA, shows fluctuations due to autonomic nervous system (ANS) activation induced sweat secretion. Since it can capture psychophysiological information, there is a significant rise in the research work for tracking mental and physiological health with EDA. However, the current state-of-the-art lacks a physiologically motivated approach for real-time inference of ANS activation from EDA. Therefore, firstly, we propose a comprehensive model for the SC dynamics. The proposed model is a 3D state-space representation of the direct secretion of sweat via pore opening and diffusion followed by corresponding evaporation and reabsorption. As the input to the model, we consider a sparse signal representing the ANS activation that causes the sweat glands to produce sweat. Secondly, we derive a scalable fixed-interval smoother-based sparse recovery approach utilizing the proposed comprehensive model to infer the ANS activation enabling edge computation. We incorporate a generalized-cross-validation to tune the sparsity level. Finally, we propose an Expectation-Maximization based deconvolution approach for learning the model parameters during the ANS activation inference. For evaluation, we utilize a dataset with 26 participants, and the results show that our comprehensive state-space model can successfully describe the SC variations with high scalability, showing the feasibility of real-time applications. Results validate that our physiology-motivated state-space model can comprehensively explain the EDA and outperforms all previous approaches. Our findings introduce a whole new perspective and have a broader impact on the standard practices of EDA analysis.

Influence of ambient temperature on tonic and phasic electrodermal activity components

OBJECTIVE

Electrodermal Activity (EDA) is a reliable indicator for variations in the skin electrical properties attributed to sympathetic nerve system activity. EDA recordings can be influenced by various internal and external factors including environmental ones. Ambient temperature can be considered as one of the possible factors which might influence EDA recordings. Hence, this study aimed to precisely investigate influence of ambient temperature on tonic and phasic EDA components by employing a new EDA measurement technique, which depends on simultaneously recording of several EDA parameters.

APPROACH

Tonic and phasic EDA components during three different ambient temperature levels were recorded from 36 healthy participants. In addition, for evoking electrodermal responses, participants were exposed to cognitive, visual and breathing external stimuli.

MAIN RESULTS

Significant effects of temperature on tonic skin conductance (SC), skin susceptance (SS) and skin potential (SP) were obtained, whereas such significant effects were not observed with phasic SC, SS and SP. Tonic EDA parameters were increased as a function of temperature, but changes in phasic component were fluctuating.

SIGNIFICANCE

This should mean that, keeping recording of tonic EDA component in normal room temperature is highly crucial, but for recording or analysis of phasic component it is not important as they are more robust in this context. This is important in applications of EDA instruments, particularly in wearable devices where environmental temperature typically cannot be controlled.

Analysis of Physiological Signals for Stress Recognition with Different Car Handling Setups

When designing a car, the vehicle dynamics and handling are important aspects, as they can satisfy a purpose in professional racing, as well as contributing to driving pleasure and safety, real and perceived, in regular drivers. In this paper, we focus on the assessment of the emotional response in drivers while they are driving on a track with different car handling setups. The experiments were performed using a dynamic professional simulator prearranged with different car setups. We recorded various physiological signals, allowing us to analyze the response of the drivers and analyze which car setup is more influential in terms of stress arising in the subjects. We logged two skin potential responses (SPRs), the electrocardiogram (ECG) signal, and eye tracking information. In the experiments, three car setups were used (neutral, understeering, and oversteering). To evaluate how these affect the drivers, we analyzed their physiological signals using two statistical tests (t-test and Wilcoxon test) and various machine learning (ML) algorithms. The results of the Wilcoxon test show that SPR signals provide higher statistical significance when evaluating stress among different drivers, compared to the ECG and eye tracking signals. As for the ML classifiers, we count the number of positive or “stress” labels of 15 s SPR time intervals for each subject and each particular car setup. With the support vector machine classifier, the mean value of the number of positive labels for the four subjects is equal to 13.13% for the base setup, 44.16% for the oversteering setup, and 39.60% for the understeering setup. In the end, our findings show that the base car setup appears to be the least stressful, and that our system enables us to effectively recognize stress while the subjects are driving in the different car configurations.

Energy Autonomous Sweat-Based Wearable Systems

The continuous operation of wearable electronics demands reliable sources of energy, currently met through Li-ion batteries and various energy harvesters. These solutions are being used out of necessity despite potential safety issues and unsustainable environmental impact. Safe and sustainable energy sources can boost the use of wearables systems in diverse applications such as health monitoring, prosthetics, and sports. In this regard, sweat- and sweat-equivalent-based studies have attracted tremendous attention through the demonstration of energy-generating biofuel cells, promising power densities as high as 3.5 mW cm-2 , storage using sweat-electrolyte-based supercapacitors with energy and power densities of 1.36 Wh kg-1 and 329.70 W kg-1 , respectively, and sweat-activated batteries with an impressive energy density of 67 Ah kg-1 . A combination of these energy generating, and storage devices can lead to fully energy-autonomous wearables capable of providing sustainable power in the µW to mW range, which is sufficient to operate both sensing and communication devices. Here, a comprehensive review covering these advances, addressing future challenges and potential solutions related to fully energy-autonomous wearables is presented, with emphasis on sweat-based energy storage and energy generation elements along with sweat-based sensors as applications.

Evaluation of tractor driving vibration fatigue based on multiple physiological parameters

The vibration generated by tractor field operations will seriously affect the comfort and health of the driver. The low frequency vibration generated by the engine and ground excitation is similar to the natural frequency of human organs. Long term operation in this environment will resonate with the organs and affect drivers' health. To investigate this possibility, in this paper we carried out a collection experiment of human physiological indicators relevant to vibration fatigue. Four physiological signals of surface electromyography, skin electricity, skin temperature, and photoplethysmography signal were collected while the subjects experienced vibration. Several features of physiological signals as well as the law of signal features changing with fatigue are studied. The test results show that with the increase of human fatigue, the overall physiological parameters show the following trends: The median frequency of the human body surface electromyography and the slope of skin surface temperature decreases, the value of skin conductivity and the mean value of the photoplethysmography signal increases. Furthermore, this paper proposes a vibration comfort evaluation method based on multiple physiological parameters of the human body. An artificial neural network model is trained with test samples, and the prediction accuracy rate reaches 88.9%. Finally, the vibration conditions are changed by the shock-absorbing suspension of a tractor, verifying the effectiveness of the physiological signal changing with the vibration of the human body. The established prediction model can also be used to objectively reflect the discomfort of the human body under different working conditions and provide a basis for structural design optimization.

Short-term usage of three non-invasive ventilation interfaces causes progressive discomfort in healthy adults

Purpose

To evaluate the effect of three different non-invasive ventilation (NIV) interfaces on the subjective discomfort of healthy individuals, and on a set of physiological parameters hypothesized to change in correspondence to discomfort.

Methods

Continuous pressure NIV was applied to 20 subjects using Total Face, Nasal, and Face masks for 10 min each. Tidal volume (VT) and respiratory period (RP) were estimated from respiratory inductance plethysmography. Electrodermal activity was estimated from conductance signals. Heart rate variability was measured using the time-domain indices SDNN and RMSSD, and the respiratory sinus arrhythmia amplitude (RSAp). Parameters were referenced to 5-min rest periods at beginning and end of protocol. A Likert-like scale of subjective discomfort with the masks and the ventilation was applied after 1, 5, and 9 min using each mask.

Results

RP and VT increased with the three mask models. Whereas the mean heart rate and RSAp did not change, both SDNN and RMSSD increased during NIV with Nasal and Face masks. Spontaneous electrodermal activity fluctuations were less frequent during NIV than at rest, with significant differences for Total Face and Nasal masks. Discomfort with all masks increased from minutes 1 to 9, markedly in the Total Face mask, considered most uncomfortable by 11 subjects.

Conclusion

In healthy subjects, the three masks resulted in similar respiratory responses to NIV. Correspondence between changes in physiological parameters and discomfort with NIV interface could not be detected, whereas self-report with the Likert-like scale identified progressive discomfort and the Total Face mask as the most uncomfortable interface.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42600-020-00114-3.

Emotion Recognition Based on Skin Potential Signals with a Portable Wireless Device

Emotion recognition is of great importance for artificial intelligence, robots, and medicine etc. Although many techniques have been developed for emotion recognition, with certain successes, they rely heavily on complicated and expensive equipment. Skin potential (SP) has been recognized to be correlated with human emotions for a long time, but has been largely ignored due to the lack of systematic research. In this paper, we propose a single SP-signal-based method for emotion recognition. Firstly, we developed a portable wireless device to measure the SP signal between the middle finger and left wrist. Then, a video induction experiment was designed to stimulate four kinds of typical emotion (happiness, sadness, anger, fear) in 26 subjects. Based on the device and video induction, we obtained a dataset consisting of 397 emotion samples. We extracted 29 features from each of the emotion samples and used eight well-established algorithms to classify the four emotions based on these features. Experimental results show that the gradient-boosting decision tree (GBDT), logistic regression (LR) and random forest (RF) algorithms achieved the highest accuracy of 75%. The obtained accuracy is similar to, or even better than, that of other methods using multiple physiological signals. Our research demonstrates the feasibility of the SP signal’s integration into existing physiological signals for emotion recognition.

Simultaneous measurement of electrodermal activity components correlated with age-related differences

Electrodermal activity (EDA) measurements are influenced by various factors. Age-related psychological and physiological changes may be considered as one of the possible factors which may influence EDA measurements. In order to properly investigate the effects of such factors on EDA, techniques of precisely and simultaneously recording more than one EDA parameter are recommended. This study aims to explore the impact of age-related differences on EDA components through employing a new measuring technique, which is composed of a small front-end electronic box, DAQ card, and a laptop running LabVIEW software. It is dependent on the simultaneous recording of three EDA parameters: skin conductance (SC), skin potential (SP), and skin susceptance (SS) at the same skin site. EDA components as results of breathing, mathematical tasks, and image stimuli were recorded from 60 healthy participants simultaneously at the same skin site. They were categorized by age into young adults (ages 18-25), middle-aged adults (ages 30-40), and old adults (ages 50-70) years. It was found that skin potential responses (SPRs), and skin conductance level (SCL) (p < 0.001), were significantly decreased due to aging, but changes in other EDA parameters were nonsignificant (p > 0.05). Moreover, both tonic and phasic SS were the least affected and found to be more robust than SC and SP with respect to aging. The study suggests that it is important to take age into account in research studies where the mean aim of the study is to compare EDA parameters; however, in the meantime, the results from our small number and specific study population cannot be generalized to clinical applications.

Innovations in Electrodermal Activity Data Collection and Signal Processing: A Systematic Review

The electrodermal activity (EDA) signal is an electrical manifestation of the sympathetic innervation of the sweat glands. EDA has a history in psychophysiological (including emotional or cognitive stress) research since 1879, but it was not until recent years that researchers began using EDA for pathophysiological applications like the assessment of fatigue, pain, sleepiness, exercise recovery, diagnosis of epilepsy, neuropathies, depression, and so forth. The advent of new devices and applications for EDA has increased the development of novel signal processing techniques, creating a growing pool of measures derived mathematically from the EDA. For many years, simply computing the mean of EDA values over a period was used to assess arousal. Much later, researchers found that EDA contains information not only in the slow changes (tonic component) that the mean value represents, but also in the rapid or phasic changes of the signal. The techniques that have ensued have intended to provide a more sophisticated analysis of EDA, beyond the traditional tonic/phasic decomposition of the signal. With many researchers from the social sciences, engineering, medicine, and other areas recently working with EDA, it is timely to summarize and review the recent developments and provide an updated and synthesized framework for all researchers interested in incorporating EDA into their research.

Psychological Correlates of Nonspecific Electrodermal Responses

Spontaneous fluctuations in electrodermal responses are known as nonspecific electrodermal responses (NS.EDRs). The use of NS.EDRs as a tool in applied psychophysiological research has resulted in a variety of publications. NS.EDRs are examined separately as associated with the (as a biomarker of) levels of anxiety. The aim of this study was to compare changes (in terms of amplitude, frequency and time components) in NS.EDRs at two different (pre and post of an external stimulus) resting phases. NS.EDRs (nonspecific skin conductance responses (NS.SCRs), nonspecific skin potential responses (NS.SPRs), and nonspecific skin susceptance responses (NS.SSRs)) were recorded from 50 apparently healthy volunteers simultaneously at the same skin area. They were scored as NS.SCRs and NS.SSRs for changes greater than 0.02 μS and NS.SPRs greater than 0.02 mV. It was found that NS.EDRs, in particular NS.SCRs and NS.SPRs, were significantly changed in the second resting period, following the specific stimulus. More specifically, the amplitude of NS.EDRs were significantly decreased for NS.SCRs (p<0.001) and for NS.SPRs (p<0.005), but NS.SSRs remained stable. Moreover, the rise time of NS.SCRs was decreased in the second resting time. Furthermore, the frequency of responses was also changed. The computed NS.EDRs, in particular NS.SCRs and NS.SPRs could be of psychological interest and be used to study the electrodermal responses in detail. NS.SSRs were found to be robust with respect to nonspecific stimuli at various relaxation periods and their role was found to be less important in analysis of NS.EDRs in comparison to NS.SCRs and NS.SPRs at low frequency (20 Hz AC current). This should be considered in analysis of NS.EDRs. The computed NS.EDRs, especially NS.SCRs and NS.SPRs may be used as a useful measure of arousal due to their fast response and sensitivity to nonspecific stimuli and may also be used in assessment of individual differences.

A Circuit for Simultaneous Measurements of Skin Electrical Conductance, Susceptance, and Potential

A circuit is presented that enables measurement of skin electrical conductance, susceptance, and potential simultaneously beneath the same monopolar electrode. Example measurements are shown to confirm the function of the circuit. The measurements are also in accordance with earlier findings that changes in skin conductance and potential do not always correspond and hence contain unique information.

Electrodermal Activity Responses for Quantitative Assessment of Felt Pain

Accurate assessment of experienced pain is a well-known problem in the clinical practices. Therefore, a proper method for pain detection is highly desirable. Electrodermal activity (EDA) is known as a measure of the sympathetic nervous system activity, which changes during various mental stresses. As pain causes mental stress, EDA measures may reflect the felt pain. This study aims to evaluate changes in skin conductance responses (SCRs), skin potential responses (SPRs), and skin susceptance responses (SSRs) simultaneously as a result of sequences of electrical (painful) stimuli with different intensities. EDA responses as results of painful stimuli were recorded from 40 healthy volunteers. The stimuli with three different intensities were produced by using an electrical stimulator. EDA responses significantly changed (increased) with respect to the intensity of the stimuli. Both SCRs and SSRs showed linear relationship with the painful stimuli. It was found that the EDA responses, particularly SCRs (p < 0.001) and SSRs (p = 0.001) were linearly affected by the intensity of the painful stimuli. EDA responses, in particular SCRs, may be used as a useful indicator for assessment of experienced pain in clinical settings.

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.

Comparison between the AC and DC measurement of electrodermal activity

Recording electrodermal activity is a well-accepted physiological measurement for clinical approaches and research. Historically, applying a DC (direct current) signal to the skin to measure the conductance is the most common practice for exogenous recordings. However, this method can be subject to error due to electrode polarization even with "nonpolarizing" electrodes-a problem that can be eliminated with alternating current (AC) methodology. For that reason, Boucsein et al. () called for research demonstrating an AC method that is validated by comparison to standard DC methodology. Additionally, the complex structure of human skin has electrical properties that include both resistance and capacitance, and AC recording enables the measurement of skin susceptance (associated with current flow through capacitors). Finally, AC recording permits the simultaneous recording of the endogenous skin potential. In this paper, the results from a direct comparison between both methods are presented, which has not been reported previously. The results demonstrated excellent agreement between a 20 Hz AC method and a standard DC method, supporting the validity of the AC recording methodology employed. The results also showed that an applied voltage of 0.2 V is sufficient for DC recordings.

The microfluidics of the eccrine sweat gland, including biomarker partitioning, transport, and biosensing implications

Non-invasive and accurate access of biomarkers remains a holy grail of the biomedical community. Human eccrine sweat is a surprisingly biomarker-rich fluid which is gaining increasing attention. This is especially true in applications of continuous bio-monitoring where other biofluids prove more challenging, if not impossible. However, much confusion on the topic exists as the microfluidics of the eccrine sweat gland has never been comprehensively presented and models of biomarker partitioning into sweat are either underdeveloped and/or highly scattered across literature. Reported here are microfluidic models for eccrine sweat generation and flow which are coupled with review of blood-to-sweat biomarker partition pathways, therefore providing insights such as how biomarker concentration changes with sweat flow rate. Additionally, it is shown that both flow rate and biomarker diffusion determine the effective sampling rate of biomarkers at the skin surface (chronological resolution). The discussion covers a broad class of biomarkers including ions (Na(+), Cl(-), K(+), NH4 (+)), small molecules (ethanol, cortisol, urea, and lactate), and even peptides or small proteins (neuropeptides and cytokines). The models are not meant to be exhaustive for all biomarkers, yet collectively serve as a foundational guide for further development of sweat-based diagnostics and for those beginning exploration of new biomarker opportunities in sweat.