Galvanic Skin Response Signals Research Articles

Wearable Ring-Shaped Biomedical Device for Physiological Monitoring through Finger-Based Acquisition of Electrocardiographic, Photoplethysmographic, and Galvanic Skin Response Signals: Design and Preliminary Measurements.

Wearable health devices (WHDs) are rapidly gaining ground in the biomedical field due to their ability to monitor the individual physiological state in everyday life scenarios, while providing a comfortable wear experience. This study introduces a novel wearable biomedical device capable of synchronously acquiring electrocardiographic (ECG), photoplethysmographic (PPG), galvanic skin response (GSR) and motion signals. The device has been specifically designed to be worn on a finger, enabling the acquisition of all biosignals directly on the fingertips, offering the significant advantage of being very comfortable and easy to be employed by the users. The simultaneous acquisition of different biosignals allows the extraction of important physiological indices, such as heart rate (HR) and its variability (HRV), pulse arrival time (PAT), GSR level, blood oxygenation level (SpO2), and respiratory rate, as well as motion detection, enabling the assessment of physiological states, together with the detection of potential physical and mental stress conditions. Preliminary measurements have been conducted on healthy subjects using a measurement protocol consisting of resting states (i.e., SUPINE and SIT) alternated with physiological stress conditions (i.e., STAND and WALK). Statistical analyses have been carried out among the distributions of the physiological indices extracted in time, frequency, and information domains, evaluated under different physiological conditions. The results of our analyses demonstrate the capability of the device to detect changes between rest and stress conditions, thereby encouraging its use for assessing individuals' physiological state. Furthermore, the possibility of performing synchronous acquisitions of PPG and ECG signals has allowed us to compare HRV and pulse rate variability (PRV) indices, so as to corroborate the reliability of PRV analysis under stationary physical conditions. Finally, the study confirms the already known limitations of wearable devices during physical activities, suggesting the use of algorithms for motion artifact correction.

Multimodal and Multidomain Feature Fusion for Emotion Classification Based on Electrocardiogram and Galvanic Skin Response Signals

Emotion classification using physiological signals is a promising approach that is likely to become the most prevalent method. Bio-signals such as those derived from Electrocardiograms (ECGs) and the Galvanic Skin Response (GSR) are more reliable than facial and voice recognition signals because they are not influenced by the participant’s subjective perception. However, the precision of emotion classification with ECG and GSR signals is not satisfactory, and new methods need to be developed to improve it. In addition, the fusion of the time and frequency features of ECG and GSR signals should be explored to increase classification accuracy. Therefore, we propose a novel technique for emotion classification that exploits the early fusion of ECG and GSR features extracted from data in the AMIGOS database. To validate the performance of the model, we used various machine learning classifiers, such as Support Vector Machine (SVM), Decision Tree, Random Forest (RF), and K-Nearest Neighbor (KNN) classifiers. The KNN classifier gives the highest accuracy for Valence and Arousal, with 69% and 70% for ECG and 96% and 94% for GSR, respectively. The mutual information technique of feature selection and KNN for classification outperformed the performance of other classifiers. Interestingly, the classification accuracy for the GSR was higher than for the ECG, indicating that the GSR is the preferred modality for emotion detection. Moreover, the fusion of features significantly enhances the accuracy of classification in comparison to the ECG. Overall, our findings demonstrate that the proposed model based on the multiple modalities is suitable for classifying emotions.

Factors Affecting Workers' Mental Stress in Handover Activities During Human-Robot Collaboration.

This study investigated the effects of different approach directions, movement speeds, and trajectories of a co-robot's end-effector on workers' mental stress during handover tasks. Human-robot collaboration (HRC) is gaining attention in industry and academia. Understanding robot-related factors causing mental stress is crucial for designing collaborative tasks that minimize workers' stress. Mental stress in HRC tasks was measured subjectively through self-reports and objectively through galvanic skin response (GSR) and electromyography (EMG). Robot-related factors including approach direction, movement speed, and trajectory were analyzed. Movement speed and approach direction had significant effects on subjective ratings, EMG, and GSR. High-speed and approaching from one side consistently resulted in higher fear, lower comfort, and predictability, as well as increased EMG and GSR signals, indicating higher mental stress. Movement trajectory affected GSR, with the sudden stop condition eliciting a stronger response compared to the constrained trajectory. Interaction effects between speed and approach direction were observed for "surprise" and "predictability" subjective ratings. At high speed, approach direction did not significantly differ, but at low speeds, approaching from the side was found to be more surprising and unpredictable compared to approaching from the front. The mental stress of workers during HRC is lower when the robot's end effector (1) approaches a worker within the worker's field of view, (2) approaches at a lower speed, or (3) follows a constrained trajectory. The outcome of this study can serve as a guide to design HRC tasks with a low level of workers' mental stress.

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

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

Emotion Classification Based on CWT of ECG and GSR Signals Using Various CNN Models

Emotions expressed by humans can be identified from facial expressions, speech signals, or physiological signals. Among them, the use of physiological signals for emotion classification is a notable emerging area of research. In emotion recognition, a person’s electrocardiogram (ECG) and galvanic skin response (GSR) signals cannot be manipulated, unlike facial and voice signals. Moreover, wearables such as smartwatches and wristbands enable the detection of emotions in people’s naturalistic environment. During the COVID-19 pandemic, it was necessary to detect people’s emotions in order to ensure that appropriate actions were taken according to the prevailing situation and achieve societal balance. Experimentally, the duration of the emotion stimulus period and the social and non-social contexts of participants influence the emotion classification process. Hence, classification of emotions when participants are exposed to the elicitation process for a longer duration and taking into consideration the social context needs to be explored. This work explores the classification of emotions using five pretrained convolutional neural network (CNN) models: MobileNet, NASNetMobile, DenseNet 201, InceptionResnetV2, and EfficientNetB7. The continuous wavelet transform (CWT) coefficients were detected from ECG and GSR recordings from the AMIGOS database with suitable filtering. Scalograms of the sum of frequency coefficients versus time were obtained and converted into images. Emotions were classified using the pre-trained CNN models. The valence and arousal emotion classification accuracy obtained using ECG and GSR data were, respectively, 91.27% and 91.45% using the InceptionResnetV2 CNN classifier and 99.19% and 98.39% using the MobileNet CNN classifier. Other studies have not explored the use of scalograms to represent ECG and GSR CWT features for emotion classification using deep learning models. Additionally, this study provides a novel classification of emotions built on individual and group settings using ECG data. When the participants watched long-duration emotion elicitation videos individually and in groups, the accuracy was around 99.8%. MobileNet had the highest accuracy and shortest execution time. These subject-independent classification methods enable emotion classification independent of varying human behavior.

Affective Impressions Recognition under Different Colored Lights Based on Physiological Signals and Subjective Evaluation Method.

The design of the light environment plays a critical role in the interaction between people and visual objects in space. Adjusting the space's light environment to regulate emotional experience is more practical for the observers under lighting conditions. Although lighting plays a vital role in spatial design, the effects of colored lights on individuals' emotional experiences are still unclear. This study combined physiological signal (galvanic skin response (GSR) and electrocardiography (ECG)) measurements and subjective assessments to detect the changes in the mood states of observers under four sets of lighting conditions (green, blue, red, and yellow). At the same time, two sets of abstract and realistic images were designed to discuss the relationship between light and visual objects and their influence on individuals' impressions. The results showed that different light colors significantly affected mood, with red light having the most substantial emotional arousal, then blue and green. In addition, GSR and ECG measurements were significantly correlated with impressions evaluation results of interest, comprehension, imagination, and feelings in subjective evaluation. Therefore, this study explores the feasibility of combining the measurement of GSR and ECG signals with subjective evaluations as an experimental method of light, mood, and impressions, which provided empirical evidence for regulating individuals' emotional experiences.

Оптимизация параметров обработки сигналов в психофизиологических исследованиях на примере КГР и ФПГ

<p>When analyzing physiological signals, the problem of setting data processing parameters arises due to the blurring of the boundary between signal and noise properties, as well as the fundamental lack of objective criteria for the quality of data processing in psychophysiology. This paper describes an approach to optimizing processing parameters on the example of galvanic skin response (GSR) and photoplethysmogram (PPG), based on the use of stimuli that are significant for a person, selected on the basis of biographical data, which can be considered as criteria validation. As a metric for the optimization, we used the frequency of coincidence of the stimuli identified as a result of the analysis with the a priori given ones (human names, including the name of the volunteer, and also visit cards selected by the volunteer). GSR and PPG signals were recorded using an MRI-compatible polygraph under conditions of functional magnetic resonance imaging (N=46 volunteers). In the first part of the work, optimization of frequency filters and analysis intervals (epochs) was performed. It has been established that the following processing parameters are optimal for analyzing the amplitude properties of the GSR signal: first-order Butterworth filters, frequency range is 0.025-0.25 Hz, interval of analysisis1-7 s from a stimulus. To analyze the PPG signal using the length of the curve, the following processing parameters are optimal: second-order Butterworth filters, frequency range is 1.25—12.5 Hz, interval of analysis is 3—10 s from a stimulus. Using the same criterion, several alternative signal processing methods were tested: change in the amplitude of the GSR signal over the analysis interval compared to the classical method by the amplitude maximum relative to the baseline; several types of ranking of reactions within a block of stimuli compared to simple averaging of all responses. The parameters and methods of processing of the GSR and PPG signals obtained in the work demonstrate universality in relation to the variety of initial data and could be applicable in applied and fundamental research. The general approach described in the work can also be used to optimize the processing parameters of other physiological signals including fMRI.</p>

The neurophysiological basis of leadership: a machine learning approach

PurposeThis research employed two neurophysiological techniques (electroencephalograms (EEG) and galvanic skin response (GSR)) and machine learning algorithms to capture and analyze relationship-oriented leadership (ROL) and task-oriented leadership (TOL). By grounding the study in the theoretical perspectives of transformational leadership and embodied leadership, the study draws connections to the human body's role in activating ROL and TOL styles.Design/methodology/approachEEG and GSR signals were recorded during resting state and event-related brain activity for 52 study participants. Both leadership styles were assessed independently using a standard questionnaire, and brain activity was captured by presenting subjects with emotional stimuli.FindingsROL revealed differences in EEG baseline over the frontal lobes during emotional stimuli, but no differences were found in GSR signals. TOL style, on the other hand, did not present significant differences in either EEG or GSR responses, as no biomarkers showed differences. Hence, it was concluded that EEG measures were better at recognizing brain activity associated with ROL than TOL. EEG signals were also strongest when individuals were presented with stimuli containing positive (specifically, happy) emotional content. A subsequent machine learning model developed using EEG and GSR data to recognize high/low levels of ROL and TOL predicted ROL with 81% accuracy.Originality/valueThe current research integrates psychophysiological techniques like EEG with machine learning to capture and analyze study variables. In doing so, the study addresses biases associated with self-reported surveys that are conventionally used in management research. This rigorous and interdisciplinary research advances leadership literature by striking a balance between neurological data and the theoretical underpinnings of transformational and embodied leadership.

The Impact of Emotional States on Construction Workers' Recognition Ability of Safety Hazards Based on Social Cognitive Neuroscience.

The construction industry is one of the most dangerous industries with grave situation owing to high accident rate and mortality rate, which accompanied with a series of security management issues that need to be tackled urgently. The unsafe behavior of construction workers is a critical reason for the high incidence of safety accidents. Affective Events Theory suggests that individual emotional states interfere with individual decisions and behaviors, which means the individual emotional states can significantly influence construction workers’ unsafe behaviors. As the complexity of the construction site environment and the lack of attention to construction workers’ emotions by managers, serious potential emotional problems were planted, resulting in the inability of construction workers to effectively recognize safety hazards, thus leading to safety accidents. Consequently, the study designs a behavioral experiment with E-prime software based on social cognitive neuroscience theories. Forty construction workers’ galvanic skin response signals were collected by a wearable device (HKR-11C+), and the galvanic skin response data were classified into different emotional states with support vector machine (SVM) algorithm. Variance analysis, correlation analysis and regression analysis were used to analyze the influence of emotional states on construction workers’ recognition ability of safety hazards. The research findings indicate that the SVM algorithm could effectively classify galvanic skin response data. The construct ion workers’ the reaction time to safety hazards and emotional valence were negatively correlated, while the accuracy of safety hazards recognition and the perception level of safety hazard separately had an inverted “U” type relationship with emotional valence. For construction workers with more than 20 years of working experience, work experience could effectively reduce the influence of emotional fluctuations on the accuracy of safety hazards identification. This study contributes to the application of physiological measurement techniques in construction safety management and shed a light on improving the theoretical system of safety management.

An Analysis of Emotion Recognition Based on GSR Signal

In our day-to-day life, the proper perception of emotion plays an important role in human decision making and behavior. Nowadays, a lot of research is focused on the evocation and precise detection of human emotion, which can be later utilized in a different set of arena. There is good amount of research on emotion detection through parameters extracted via Face Recognition and Speech Modulation, etc. However, there is a huge question on the accuracy or effectiveness of these results as these features can be controlled or manipulated by the subject/person. So, the next approach is the usage of Physiological Signals. These signals are generated by the Central Nervous System (CNS) and cannot be controlled or manipulated by the subject/person. In the proposed work, we have used Galvanic Skin Response (GSR) signals for emotion detection. It is an easily available off-the-shelf, non-invasive sensing device, and is easy to use. We have used different machine learning models to classify the various emotional states with better accuracy. The different classifiers that are used are the k-Nearest Neighbors (kNN), Support Vector Machine (SVM), and Logistic Regression (LR).

Chirplet transform‐based machine‐learning approach towards classification of cognitive state change using galvanic skin response and photoplethysmography signals

AbstractRecent advances in the field of cognitive sciences have provided evidence to classify activities with different cognitive engagement levels. High cognitive load and sustained attention may predispose an individual to psychological and physical stress and may result in a lack of performance. Cognitive load monitoring of professionals in high precision fields has gathered significant attention in the current research scenario. The proposed work in continuity has tried to demonstrate the use of two non‐invasive physiological sensing modalities viz. galvanic skin response (GSR) and photoplethysmography (PPG) to elucidate effectiveness in picking up differences of two induced cognitive state categories based on machine learning and statistical methods. This paper demonstrates how GSR and PPG signals data can be effectively utilized to examine the shift in cognitive load. The present work proposes the use of general linear chirplet transform (GLCT) to evaluate the time‐frequency characteristic of GSR and PPG signals and utilize the statistical features in classification. Random Forest, Decision Tree, and k‐Nearest Neighbours demonstrated an accuracy of 92.13%, 88.0%, and 86.13% respectively on a dataset of 20 subjects against an optimized feature set, thus demonstrating the effectiveness of the proposed methodology for differentiating pre‐defined categories of cognitive load. The study shows the potential of GSR and PPG signals attributes and time‐frequency representation using GLCT to monitor cognitive load in real‐life conditions. The proposed work also indicates the possibility to extend the same to attention‐demanding fields such as aviation, medicine, and manufacturing for effective remediation of fatigue periods with increased cognitive demand, which, if sustained, may lead to cognitive decline in the long run.

Correlating Personal Resourcefulness and Psychomotor Skills: An Analysis of Stress, Visual Attention and Technical Metrics.

Modern surgical education is focused on making use of the available technologies in order to train and assess surgical skill acquisition. Innovative technologies for the automatic, objective assessment of nontechnical skills are currently under research. The main aim of this study is to determine whether personal resourcefulness can be assessed by monitoring parameters that are related to stress and visual attention and whether there is a relation between these and psychomotor skills in surgical education. For this purpose, we implemented an application in order to monitor the electrocardiogram (ECG), galvanic skin response (GSR), gaze and performance of surgeons-in-training while performing a laparoscopic box-trainer task so as to obtain technical and personal resourcefulness’ metrics. Eight surgeons (6 nonexperts and 2 experts) completed the experiment. A total of 22 metrics were calculated (7 technical and 15 related to personal resourcefulness) per subject. The average values of these metrics in the presence of stressors were compared with those in their absence and depending on the participants’ expertise. The results show that both the mean normalized GSR signal and average surgical instrument’s acceleration change significantly when stressors are present. Additionally, the GSR and acceleration were found to be correlated, which indicates that there is a relation between psychomotor skills and personal resourcefulness.

1D Convolutional Autoencoder-Based PPG and GSR Signals for Real-Time Emotion Classification

To apply emotion recognition and classification technology to the field of human-robot interaction, it is necessary to implement fast data processing and model weight reduction. This paper proposes a new photoplethysmogram (PPG) and galvanic skin response (GSR) signals-based labeling method using Asian multimodal data, a real-time emotion classification method, a 1d convolutional neural network autoencoder model, and a lightweight model obtained using knowledge distillation. In addition, the model performance was verified using the public DEAP dataset and the Asian multi-modal dataset ’MERTI-Apps’. For emotion classification, bio-signal data were window-sliced in 1-pulse units, and the label was reset to reflect the characteristics of the PPG and GSR signals. Simple data pre-processing, such as the prevention of loss and waveform duplication, was performed without using handcrafted features. The experiment showed that the accuracy of the proposed model using MERTI-Apps was 79.18% and 74.84% in the case of arousal and valence, respectively, for 3-class criteria, and the accuracy of the proposed model using DEAP was 81.33% and 80.25% in the case of arousal and valence, respectively, for 2-class criteria. The accuracy of the lightweight model was 77.87% and 73.49% in the case of arousal and valence, respectively, for 3-class criteria and its calculation time was reduced by more than 80% compared to the proposed 1d convolutional autoencoder model. We also confirmed that the proposed model improved computational time and accuracy compared to previous studies using MERTI-Apps and the lightweight model used in limited hardware environments enabled fast computation and real-time emotion classification.

Emotion recognition from multimodal physiological measurements based on an interpretable feature selection method.

Many studies in literature successfully use classification algorithms to classify emotions by means of physiological signals. However, there are still important limitations in interpretability of the results, i.e. lack of feature specific characterizations for each emotional state. To this extent, our study proposes a feature selection method that allows to determine the most informative subset of features extracted from physiological signals by maintaining their original dimensional space. Results show that features from the galvanic skin response are confirmed to be relevant in separating the arousal dimension, especially fear from happiness and relaxation. Furthermore, the average and the median value of the galvanic skin response signal together with the ratio between SD1 and SD2 from the Poincarè analysis of the electrocardiogram signal, were found to be the most important features for the discrimination along the valence dimension. A Linear Discriminant Analysis model using the first ten features sorted by importance, as defined by their ability to discriminate emotions with a bivariate approach, led to a three-class test accuracy in discriminating happiness, relaxation and fear equal to 72%, 67% and 89% respectively.Clinical relevance This study demonstrates the ability of physiological signals to assess the emotional state of different subjects, by providing a fast and efficient method to select most important indexes from the autonomic nervous system. The approach has high clinical relevance as it could be extended to assess other emotional states (e.g. stress and pain) characterizing pathological states such as post traumatic stress disorder and depression.

Measuring human physiological indices for thermal comfort assessment through wearable devices: A review

Thermal comfort is defined as the condition of mind that expresses satisfaction with the thermal environment and it is assessed by subjective evaluation. Achieving comfortable environments is essential for human health, productivity, learning performance and energy efficiency. The measurement of thermal comfort requires an indirect process involving several domains: thermal environment, physiology and psychology. Four physiological signals were reviewed, considering their relevance in the context of measuring indoor thermal comfort. The measurement approaches for investigating electroencephalography, electrocardiograph, skin temperature and galvanic skin response signals in the field of thermal comfort are presented. This paper introduces an overview on the application of wearable sensors for recording physiological parameters and extracting features potentially correlated with thermal comfort, together with a discussion about their reliability. The review shows the state of the art, the identification of existing knowledge gaps in this area and the corresponding needs for future research and dedicated methodological efforts.

Recognition of Stress Activation by Unobtrusive Multi Sensing Setup

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.

Application of fractional Fourier transform in feature extraction from ELECTROCARDIOGRAM and GALVANIC SKIN RESPONSE for emotion recognition

Emotion recognition from physiological signals plays an essential role in human–computer interaction and affective computing. This paper aims to study the effectiveness of Fractional Fourier Transform (FrFT) as a novel feature extraction method in improving the accuracy of emotion recognition from physiological signals. Emotion detection is performed in two dimensions, of arousal and valence, using Electrocardiogram (ECG) and galvanic skin response (GSR) signals recorded on the ASCERTAIN database. Features extracted in the FrFT, time, and frequency domains are classified using two binary SVMs. The results suggest the usefulness of the phase information of the FrFT coefficients in arousal and valence detection and above-chance emotion recognition is achieved with both ECG and GSR signals. Comparison of the time domain features, frequency domain features, and their combination shows that FrFT are more distinct in emotion detection. The best recognition accuracy in both valence and arousal is achieved from the phase information of the FrFT coefficients using the ECG signal, which is equal to 78.32% and 76.83%, respectively.

ALTERNATIVE METHOD FOR PAIN ASSESSMENT USING EMG AND GSR

Most of the existing pain estimation techniques depend on the response of the subject through verbal or nonverbal communication which does not suit infants, senseless and injured persons, and subjects with cognitive impairment. To bridge this gap, researchers have explored the potential of facial video- and image-based pain recognition methods. However, it provides limited classification performance with complex computation and costly frameworks including a large storage capacity. The dependence of autonomic nervous system (ANS) activities on stimulus like pain provides an alternative pathway to assess pain subjected to external stimuli through ANS-related biosignals. In this article, processing and analysis technique of electromyogram and galvanic skin response signals for assessment of pain for noncooperative subjects are presented and validated against BioVid heat pain database. Different intensities of pain are considered and characterized with the statistical features extracted from the said biosignals. It is noticed that the accuracy level of pain estimation increases with the rise in pain intensity. For highest pain level, 80% detection accuracy is achieved which outperforms the performances of facial expression-based pain assessment techniques.

Pain Assessment Tool With Electrodermal Activity for Postoperative Patients: Method Validation Study.

BackgroundAccurate, objective pain assessment is required in the health care domain and clinical settings for appropriate pain management. Automated, objective pain detection from physiological data in patients provides valuable information to hospital staff and caregivers to better manage pain, particularly for patients who are unable to self-report. Galvanic skin response (GSR) is one of the physiologic signals that refers to the changes in sweat gland activity, which can identify features of emotional states and anxiety induced by varying pain levels. This study used different statistical features extracted from GSR data collected from postoperative patients to detect their pain intensity. To the best of our knowledge, this is the first work building pain models using postoperative adult patients instead of healthy subjects.ObjectiveThe goal of this study was to present an automatic pain assessment tool using GSR signals to predict different pain intensities in noncommunicative, postoperative patients.MethodsThe study was designed to collect biomedical data from postoperative patients reporting moderate to high pain levels. We recruited 25 participants aged 23-89 years. First, a transcutaneous electrical nerve stimulation (TENS) unit was employed to obtain patients' baseline data. In the second part, the Empatica E4 wristband was worn by patients while they were performing low-intensity activities. Patient self-report based on the numeric rating scale (NRS) was used to record pain intensities that were correlated with objectively measured data. The labels were down-sampled from 11 pain levels to 5 different pain intensities, including the baseline. We used 2 different machine learning algorithms to construct the models. The mean decrease impurity method was used to find the top important features for pain prediction and improve the accuracy. We compared our results with a previously published research study to estimate the true performance of our models.ResultsFour different binary classification models were constructed using each machine learning algorithm to classify the baseline and other pain intensities (Baseline [BL] vs Pain Level [PL] 1, BL vs PL2, BL vs PL3, and BL vs PL4). Our models achieved higher accuracy for the first 3 pain models than the BioVid paper approach despite the challenges in analyzing real patient data. For BL vs PL1, BL vs PL2, and BL vs PL4, the highest prediction accuracies were achieved when using a random forest classifier (86.0, 70.0, and 61.5, respectively). For BL vs PL3, we achieved an accuracy of 72.1 using a k-nearest-neighbor classifier.ConclusionsWe are the first to propose and validate a pain assessment tool to predict different pain levels in real postoperative adult patients using GSR signals. We also exploited feature selection algorithms to find the top important features related to different pain intensities.International Registered Report Identifier (IRRID)RR2-10.2196/17783

Human stress classification during public speaking using physiological signals

Public speaking is a common type of social evaluative situation and a significant amount of the population feel uneasy with it. It is of utmost importance to detect public speaking stress so that appropriate action can be taken to minimize its impacts on human health. In this study, a multimodal human stress classification scheme in response to real-life public speaking activity is proposed. Electroencephalography (EEG), galvanic skin response (GSR), and photoplethysmography (PPG) signals of forty participants are acquired in rest-state and during public speaking activity to divide data into a stressed and non-stressed group. Frequency domain features from EEG and time-domain features from GSR and PPG signals are extracted. The selected set of features from all modalities are fused to classify the stress into two classes. Classification is performed via a leave-one-out cross-validation scheme by using five different classifiers. The highest accuracy of 96.25% is achieved using a support vector machine classifier with radial basis function. Statistical analysis is performed to examine the significance of EEG, GSR, and PPG signals between the two phases of the experiment. Statistical significance is found in certain EEG frequency bands as well as GSR and PPG data recorded before and after public speaking supporting the fact that brain activity, skin conductance, and blood volumetric flow are credible measures of human stress during public speaking activity.