Electrodermal Activity Signals Research Articles

EDA-Graph: Graph Signal Processing of Electrodermal Activity for Emotional States Detection.

The continuous detection of emotional states has many applications in mental health, marketing, human-computer interaction, and assistive robotics. Electrodermal activity (EDA), a signal modulated by sympathetic nervous system activity, provides continuous insight into emotional states. However, EDA possesses intricate nonstationary and nonlinear characteristics, making the extraction of emotion-relevant information challenging. We propose a novel graph signal processing (GSP) approach to model EDA signals as graphical networks, termed EDA-graph. The GSP leverages graph theory concepts to capture complex relationships in time-series data. To test the usefulness of EDA-graphs to detect emotions, we processed EDA recordings from the CASE emotion dataset using GSP by quantizing and linking values based on the Euclidean distance between the nearest neighbors. From these EDA-graphs, we computed the features of graph analysis, including total load centrality (TLC), total harmonic centrality (THC), number of cliques (GNC), diameter, and graph radius, and compared those features with features obtained using traditional EDA processing techniques. EDA-graph features encompassing TLC, THC, GNC, diameter, and radius demonstrated significant differences (p<0.05) between five emotional states (Neutral, Amused, Bored, Relaxed, and Scared). Using machine learning models for classifying emotional states evaluated using leave-one-subject-out cross-validation, we achieved a five-class F1 score of up to 0.68.

Zooming into the Complex Dynamics of Electrodermal Activity Recorded during Emotional Stimuli: A Multiscale Approach

Physiological phenomena exhibit complex behaviours arising at multiple time scales. To investigate them, techniques derived from chaos theory were applied to physiological signals, providing promising results in distinguishing between healthy and pathological states. Fractal-like properties of electrodermal activity (EDA), a well-validated tool for monitoring the autonomic nervous system state, have been reported in previous literature. This study proposes the multiscale complexity index of electrodermal activity (MComEDA) to discern different autonomic responses based on EDA signals. This method builds upon our previously proposed algorithm, ComEDA, and it is empowered with a coarse-graining procedure to provide a view at multiple time scales of the EDA response. We tested MComEDA’s performance on the EDA signals of two publicly available datasets, i.e., the Continuously Annotated Signals of Emotion (CASE) dataset and the Affect, Personality and Mood Research on Individuals and Groups (AMIGOS) dataset, both containing physiological data recorded from healthy participants during the view of ultra-short emotional video clips. Our results highlighted that the values of MComEDA were significantly different (p-value &lt; 0.05 after Wilcoxon signed rank test with Bonferroni’s correction) when comparing high- and low-arousal stimuli. Furthermore, MComEDA outperformed the single-scale approach in discriminating among different valence levels of high-arousal stimuli, e.g., showing significantly different values for scary and amusing stimuli (p-value = 0.024). These findings suggest that a multiscale approach to the nonlinear analysis of EDA signals can improve the information gathered on task-specific autonomic response, even when ultra-short time series are considered.

Wearable Technology Insights: Unveiling Physiological Responses During Three Different Socially Anxious Activities

Wearable technology holds promise for monitoring and managing Social Anxiety Disorder (SAD), yet the absence of clear biomarkers specific to SAD hampers its effectiveness. This paper explores this issue by presenting a study investigating variances in heart rate, heart rate variability, and skin conductance between socially anxious and non-anxious individuals. One hundred eleven non-clinical student participants participated in groups of three in three anxiety-provoking activities (i.e., speech, group discussion, and interview) in a controlled lab-based study. During the study, electrocardiogram (ECG) and electrodermal activity (EDA) signals were captured via on-body electrodes. During data analysis, participants were divided into four groups based on their self-reported anxiety level (“None”, “mild”, “moderate”, and “severe”). Between-group analysis shows that discriminating ECG features (i.e., HR and MeanNN) could identify anxious individuals during anxiety-provoking activities, while EDA could not. Moreover, the discriminating ECG features improved the classification accuracy of anxious and non-anxious individuals in different machine-learning techniques. The findings need to be further scrutinized in real-world settings for the generalizability of the results.

Human Activity Recognition Algorithm with Physiological and Inertial Signals Fusion: Photoplethysmography, Electrodermal Activity, and Accelerometry.

Inertial signals are the most widely used signals in human activity recognition (HAR) applications, and extensive research has been performed on developing HAR classifiers using accelerometer and gyroscope data. This study aimed to investigate the potential enhancement of HAR models through the fusion of biological signals with inertial signals. The classification of eight common low-, medium-, and high-intensity activities was assessed using machine learning (ML) algorithms, trained on accelerometer (ACC), blood volume pulse (BVP), and electrodermal activity (EDA) data obtained from a wrist-worn sensor. Two types of ML algorithms were employed: a random forest (RF) trained on features; and a pre-trained deep learning (DL) network (ResNet-18) trained on spectrogram images. Evaluation was conducted on both individual activities and more generalized activity groups, based on similar intensity. Results indicated that RF classifiers outperformed corresponding DL classifiers at both individual and grouped levels. However, the fusion of EDA and BVP signals with ACC data improved DL classifier performance compared to a baseline DL model with ACC-only data. The best performance was achieved by a classifier trained on a combination of ACC, EDA, and BVP images, yielding F1-scores of 69 and 87 for individual and grouped activity classifications, respectively. For DL models trained with additional biological signals, almost all individual activity classifications showed improvement (p-value < 0.05). In grouped activity classifications, DL model performance was enhanced for low- and medium-intensity activities. Exploring the classification of two specific activities, ascending/descending stairs and cycling, revealed significantly improved results using a DL model trained on combined ACC, BVP, and EDA spectrogram images (p-value < 0.05).

Design of a Remote Multiparametric Tool to Assess Mental Well-Being and Distress in Young People (mHealth Methods in Mental Health Research Project): Protocol for an Observational Study.

Mental health conditions have become a substantial cause of disability worldwide, resulting in economic burden and strain on the public health system. Incorporating cognitive and physiological biomarkers using noninvasive sensors combined with self-reported questionnaires can provide a more accurate characterization of the individual's well-being. Biomarkers such as heart rate variability or those extracted from the electrodermal activity signal are commonly considered as indices of autonomic nervous system functioning, providing objective indicators of stress response. A model combining a set of these biomarkers can constitute a comprehensive tool to remotely assess mental well-being and distress. This study aims to design and validate a remote multiparametric tool, including physiological and cognitive variables, to objectively assess mental well-being and distress. This ongoing observational study pursues to enroll 60 young participants (aged 18-34 years) in 3 groups, including participants with high mental well-being, participants with mild to moderate psychological distress, and participants diagnosed with depression or anxiety disorder. The inclusion and exclusion criteria are being evaluated through a web-based questionnaire, and for those with a mental health condition, the criteria are identified by psychologists. The assessment consists of collecting mental health self-reported measures and physiological data during a baseline state, the Stroop Color and Word Test as a stress-inducing stage, and a final recovery period. Several variables related to heart rate variability, pulse arrival time, breathing, electrodermal activity, and peripheral temperature are collected using medical and wearable devices. A second assessment is carried out after 1 month. The assessment tool will be developed using self-reported questionnaires assessing well-being (short version of Warwick-Edinburgh Mental Well-being Scale), anxiety (Generalized Anxiety Disorder-7), and depression (Patient Health Questionnaire-9) as the reference. We will perform correlation and principal component analysis to reduce the number of variables, followed by the calculation of multiple regression models. Test-retest reliability, known-group validity, and predictive validity will be assessed. Participant recruitment is being carried out on a university campus and in mental health services. Recruitment commenced in October 2022 and is expected to be completed by June 2024. As of July 2023, we have recruited 41 participants. Most participants correspond to the group with mild to moderate psychological distress (n=20, 49%), followed by the high mental well-being group (n=13, 32%) and those diagnosed with a mental health condition (n=8, 20%). Data preprocessing is currently ongoing, and publication of the first results is expected by September 2024. This study will establish an initial framework for a comprehensive mental health assessment tool, taking measurements from sophisticated devices, with the goal of progressing toward a remotely accessible and objectively measured approach that maintains an acceptable level of accuracy in clinical practice and epidemiological studies. OSF Registries N3GCH; https://doi.org/10.17605/OSF.IO/N3GCH. DERR1-10.2196/51298.

Optimal Electrodermal Activity Segment for Enhanced Emotion Recognition Using Spectrogram-Based Feature Extraction and Machine Learning.

In clinical and scientific research on emotion recognition using physiological signals, selecting the appropriate segment is of utmost importance for enhanced results. In our study, we optimized the electrodermal activity (EDA) segment for an emotion recognition system. Initially, we obtained EDA signals from two publicly available datasets: the Continuously annotated signals of emotion (CASE) and Wearable stress and affect detection (WESAD) for 4-class dimensional and three-class categorical emotional classification, respectively. These signals were pre-processed, and decomposed into phasic signals using the 'convex optimization to EDA' method. Further, the phasic signals were segmented into two equal parts, each subsequently segmented into five nonoverlapping windows. Spectrograms were then generated using short-time Fourier transform and Mel-frequency cepstrum for each window, from which we extracted 85 features. We built four machine learning models for the first part, second part, and whole phasic signals to investigate their performance in emotion recognition. In the CASE dataset, we achieved the highest multi-class accuracy of 62.54% using the whole phasic and 61.75% with the second part phasic signals. Conversely, the WESAD dataset demonstrated superior performance in three-class emotions classification, attaining an accuracy of 96.44% for both whole phasic and second part phasic segments. As a result, the second part of EDA is strongly recommended for optimal outcomes.

Exploring the impact of computer-mediated emotional interactions on human facial and physiological responses

Remote communication has become pervasive, yet its impact on human emotions, empathy, and physiological responses remains unclear. This study addresses this gap by investigating how emotional video-mediated conversations differ from face-to-face interactions, focusing on behavioral and physiological responses. We create a multimodal dataset of Electrodermal Activity (EDA) signals, Photoplethysmography (PPG) signals, and facial videos from two people conversing about emotional topics in face-to-face and remote video-mediated conditions. We use a series of repeated measures ANOVA with aligned rank transform to compare face-to-face to remote conversation in terms of heart rate activity, electrodermal activity, and facial action units. We also explore how subjective empathy between people varies in these two conditions. Our findings reveal significant differences in physiological responses between face-to-face and remote conversation and variations in perceived empathy based on interaction setting, highlighting the nuanced influence of communication channels. We also show that we can recognize emotions more accurately when we pre-train a random forest classifier with one condition’s data (an increase of 20% to 45% for various modalities). Finally, we discuss the research findings and limitations and offer insights for optimizing human–computer interaction and understanding human emotional responses in an increasingly tech-mediated world.

Machine learning-based multimodal fusion recognition of passenger ship seafarers’ workload: A case study of a real navigation experiment

Passenger ships have complex transportation systems and seafarers face high workloads, making them susceptible to serious injuries and fatalities in the event of accidents. Existing unimodal workload recognition for seafarers mainly focuses on fixed load induction in bridge simulators, whereas a multimodal approach using multi-sensor data fusion can overcome the reliability and sensitivity limitations of a single sensor. To accurately identify the workload of seafarers, we propose a machine learning-based multimodal fusion method at the feature layer and utilise the Gini index to determine the feature weight of the multimodal data. Through a real ship navigation experiment, the subjective workload assessment technique (SWAT) was employed to collect the continuous workload scores of 24 seafarers in daily tasks. Further, the Dempster–Shafer evidence theory was used to integrate these scores with the unsafe behavior probability of seafarers to obtain a calibrated workload. Electroencephalogram (EEG), electrocardiogram (ECG), and electrodermal activity (EDA) signals were collected in real time, and a high-dimensional feature matrix was extracted to construct the workload recognition model. Random forest, XGBoost, and back-propagation neural networks were used to establish multimodal fusion workload recognition models at the feature-fusion stage, and the model performances were compared. The results showed that the multimodal fusion based on EEG, ECG, and EDA had an excellent recognition accuracy. The XGBoost algorithm has better performance with an accuracy of 85.72%, which is an increment of 9.49% compared to that of the unimodal algorithm, and this improvement passed the statistical significance test. Important features suitable for multimodal fusion recognition were also analysed. The findings of this study have profound significance for enhancing risk control measures pertaining to human factors and ensuring navigation safety.

Machine learning-based stress classification system using wearable sensor devices

&lt;p&gt;University students often become victims of high-stress levels due to the highly competitive work environment. Unmonitored stress levels in students can inflict severe physiological health problems. This work aims to build a stress classification framework using wearable sensor devices to predict mental stress levels for undergraduate engineering students. It comprises a study to collect a data set of 23 university students using wearable devices for four physiological signals, i.e., electroencephalogram (EEG), electrodermal activity (EDA), skin temperature (SKT), and heart rate (HR), when the students perform the montreal imaging stress task (MIST) for the mental workload. The machine learning models proposed in this work help classify stress into three levels: rest, moderate, and high. The models achieve a classification accuracy of 99.98% using the EEG signals’ time-frequency domain features and an accuracy of 99.51% using the EDA, HR, and SKT signals. The proposed models achieve better scores than all the previous studies on stress classification, using EEG signals and EDA, HR, and SKT signals. This study is novel since it also demonstrates the applicability and proficiency of wearable sensor devices in developing accurate stress classification models to help build real-time stress monitoring systems.&lt;/p&gt;

Measurements of Electrodermal Activity, Tissue Oxygen Saturation, and Visual Analog Scale for Different Cuff Pressures.

The quantification of comfort in binding parts, essential human-machine interfaces (HMI) for the functioning of rehabilitation robots, is necessary to reduce physical strain on the user despite great achievements in their structure and control. This study aims to investigate the physiological impacts of binding parts by measuring electrodermal activity (EDA) and tissue oxygen saturation (StO2). In Experiment 1, EDA was measured from 13 healthy subjects under three different pressure conditions (10, 20, and 30 kPa) for 1 min using a pneumatic cuff on the right thigh. In Experiment 2, EDA and StO2 were measured from 10 healthy subjects for 5 min. To analyze the correlation between EDA parameters and the decrease in StO2, a survey using the visual analog scale (VAS) was conducted to assess the level of discomfort at each pressure. The EDA signal was decomposed into phasic and tonic components, and the EDA parameters were extracted from these two components. RM ANOVA and a post hoc paired t-test were used to determine significant differences in parameters as the pressure increased. The results showed that EDA parameters and the decrease in StO2 significantly increased with the pressure increase. Among the extracted parameters, the decrease in StO2 and the mean SCL proved to be effective indicators. Such analysis outcomes would be highly beneficial for studies focusing on the comfort assessment of the binding parts of rehabilitation robots.

Tracking motion sickness in dynamic VR environments with EDA signals

Virtual reality (VR) has emerged as a mainstream commercial technology with multiple sensory channels, including advanced user interfaces (human-computer). However, the number of users complaining of VR motion sickness (MS) has been rapidly increasing. In this study, VR MS was investigated using subjective questionnaires: simulator sickness questionnaire (SSQ), virtual reality sickness questionnaire (VRSQ), and fast motion sickness scale (FMS) and objective physiological methods: electrodermal activity (EDA). To track VR MS based on symptoms, a model was constructed by applying a polynomial regression method. In addition, this study attempted to determine whether there are differences in MS according to the degree of immersion in a VR environment. Twenty participants (10 males and 10 females) were exposed to a dynamic VR environment through a simple operation method using a head-mounted display (HMD). The coefficients of determination of the objective measurement components were high before and after the rest. In particular, both the SSQ and VRSQ were highest for disorientation, that is, dizziness disorder and difficulty concentrating. This suggests that the timing of the EDA signal data extraction with respect to the rest may be critical. In addition, subjects placed in an immersive VR environment performed the experiment in an unstable and dynamic posture; accordingly, the VR MS score was higher for subjects who participated in the experiment in a more dynamic position. Therefore, we confirmed the possibility of a significant association between VR sickness and postural stability. This study demonstrates the possibility of tracking MS in a dynamic VR environment according to intervals and symptoms using EDA signals, and the stability of posture should be considered in immersed VR environments.Relevance to industry: The findings here provide insights into the model of tracking motion sickness using the EDA signal to prevent or reduce VR motion sickness. The EDA signal model can benefit the design or development of an immersive VR environment.

DETECTION OF PHOBIAS FROM ELECTRODERMAL ACTIVITY SIGNAL

This article is focused on the preliminary study of detecting different types of phobias from the electrodermal activity signal. Electrodermal activity is independent of parasympathetic activity; therefore, it is an ideal indicator of phobias. An automatic algorithm was created for the detection of phobias, which evaluates the presence of a stress reaction to a stimulus in the form of an image sequence representing the given phobia. The results obtained using the proposed algorithm were confronted with the data provided by individual respondents in the questionnaire. By comparison, it was found that the proposed algorithm detected a greater number of phobias than reported by the respondents. According to the achieved results, we can state that electrodermal activity can serve as a means of objectifying the presence of phobias in individuals.

Adaptive thresholding increases sensitivity to detect changes in the rate of skin conductance responses to psychologically arousing stimuli in both laboratory and ambulatory settings

Psychophysiologists recording electrodermal activity (EDA) often derive measures of slow, tonic activity—skin conductance level (SCL)—and faster, more punctate changes—skin conductance responses (SCRs). A SCR is conventionally considered to have occurred when the local amplitude of the EDA signal exceeds a researcher-determined threshold (e.g., 0.05 μS), typically fixed across study participants and conditions. However, fixed SCR thresholds can preferentially exclude data from individuals with low SCL because their SCRs are smaller on average, thereby reducing statistical power for group-level analyses. Thus, we developed a fixed plus adaptive (FA) thresholding method that adjusts identification of SCRs based on an individual's SC at the onset of the SCR to increase statistical power and include data from more participants. We assess the utility of applying FA thresholding across two independent samples and explore age and race-related associations with EDA outcomes. Study 1 uses wired EDA measurements from 254 healthy adults responding to evocative images and sounds in a laboratory setting. Study 2 uses wireless EDA measurements from 20 children with autism in a clinical environment while they completed behavioral tasks. Compared to a 0.01, 0.03, and 0.05 μS fixed threshold, FA thresholding at 1.9% modestly increases statistical power to detect a difference in SCR rate between tasks with higher vs. lower subjective arousal and reduces exclusion of participants by up to 5% across both samples. This novel method expands the EDA analytical toolbox and may be useful in populations with highly variable basal SCL or when comparing groups with different basal SCL. Future research should test for reproducibility and generalizability in other tasks, samples, and contexts. Impact statementsThis article is important because it introduces a novel method to enhance sensitivity and statistical power in analyses of skin conductance responses from electrodermal data.

Video-based sympathetic arousal assessment via peripheral blood flow estimation.

Electrodermal activity (EDA) is considered a standard marker of sympathetic activity. However, traditional EDA measurement requires electrodes in steady contact with the skin. Can sympathetic arousal be measured using only an optical sensor, such as an RGB camera? This paper presents a novel approach to infer sympathetic arousal by measuring the peripheral blood flow on the face or hand optically. We contribute a self-recorded dataset of 21 participants, comprising synchronized videos of participants' faces and palms and gold-standard EDA and photoplethysmography (PPG) signals. Our results show that we can measure peripheral sympathetic responses that closely correlate with the ground truth EDA. We obtain median correlations of 0.57 to 0.63 between our inferred signals and the ground truth EDA using only videos of the participants' palms or foreheads or PPG signals from the foreheads or fingers. We also show that sympathetic arousal is best inferred from the forehead, finger, or palm.

Advanced Analysis of Electrodermal Activity Measures to Detect the Onset of ON State in Parkinson’s Disease

Background: Electrodermal activity (EDA) serves as a prominent biosignal for assessing sympathetic activation across various scenarios. Prior research has suggested a connection between EDA and fluctuations in Parkinson’s disease (PD), but its precise utility in reliably detecting these fluctuations has remained unexplored. This study aims to evaluate the efficacy of both basic and advanced analyses of EDA changes in identifying the transition to the ON state following dopaminergic medication administration in individuals with PD. Methods: In this observational study, 19 individuals with PD were enrolled. EDA was continuously recorded using the Empatica E4 device, worn on the wrist, during the transition from the OFF state to the ON state following levodopa intake. The raw EDA signal underwent preprocessing and evaluation through three distinct approaches. A logistic regression model was constructed to assess the significance of variables predicting the ON/OFF state, and support vector machine (SVM) models along with various Neural Network (NN) configurations were developed for accurate state prediction. Results: Differences were identified between the ON and OFF states in both the time and frequency domains, as well as through the utilization of convex optimization techniques. SVM and NN models demonstrated highly promising results in effectively distinguishing between the OFF and ON states. Conclusions: Evaluating sympathetic activation changes via EDA measures holds substantial promise for detecting non-motor fluctuations in PD. The SVM algorithm, in particular, yields precise outcomes for predicting these non-motor fluctuation states.

The linguistic structure of an emotional text influences the sympathetic activity and the speech prosody of the reader

In this study, we present an analysis of the relationship between the linguistic profile of a text and the physiological and acoustic characteristics of the reader to improve the emotion recognition systems. To this aim, we recorded the speech and electrodermal activity (EDA) signals from 33 healthy volunteers reading neutral and affective texts aloud. We used the BioVoice toolbox and cvxEDA algorithm to estimate some of the main speech and EDA features, respectively. The selected texts were analyzed to quantify their lexical, morpho-syntactic, and syntactic properties. Correlation and Support Vector Regression analyses between linguistic and speech and EDA features have shown a significant bidirectional association between the morpho-syntactic structure of the text and both sympathetic markers and voice acoustic properties. Specifically, significant relationships were observed between linguistic properties and certain EDA and speech features commonly used to evaluate human emotional state (e.g., edaSymp, mean tonic, F0). These findings suggest that lexical, morpho-syntactic, and syntactic properties may have a significant impact on an individual’s emotional dynamics.

Automatic detection of artifacts and improved classification models for emotional activity detection from multimodal physiological data

This manuscript proposes an automated artifacts detection and multimodal classification system for human emotion analysis from human physiological signals. First, multimodal physiological data, including the Electrodermal Activity (EDA), electrocardiogram (ECG), Blood Volume Pulse (BVP) and respiration rate signals are collected. Second, a Modified Compressed Sensing-based Decomposition (MCSD) is used to extract the informative Skin Conductance Response (SCR) events of the EDA signal. Third, raw features (edge and sharp variations), statistical and wavelet coefficient features of EDA, ECG, BVP, respiration and SCR signals are obtained. Fourth, the extracted raw features, statistical and wavelet coefficient features from all physiological signals are fed into the parallel Deep Convolutional Neural Network (DCNN) to reduce the dimensionality of feature space by removing artifacts. Fifth, the fused artifact-free feature vector is obtained for neutral, stress and pleasure emotion classes. Sixth, an artifact-free feature vector is used to train the Random Forest Deep Neural Network (RFDNN) classifier. Then, a trained RFDNN classifier is applied to classify the test signals into different emotion classes. Thus, leveraging the strengths of both RF and DNN algorithms, more comprehensive feature learning using multimodal psychological data is achieved, resulting in robust and accurate classification of human emotional activities. Finally, an extensive experiment using the Wearable Stress and Affect Detection (WESAD) dataset shows that the proposed system outperforms other existing human emotion classification systems using physiological data.

Simulation of ambulatory electrodermal activity and the handling of low-quality segments

Background and objectivesMonitoring electrodermal activity (EDA) in daily life requires effective handling of low-quality segments, which are common in ambulatory EDA data. Although several low-quality handling methods have been implemented, systematic comparison of these methods, which requires a large annotated dataset, is lacking. MethodsTherefore, we proposed the simulation of realistic ambulatory EDA data starting from high-quality EDA signals, which were subsequently contaminated with varying concentrations of artifacts. Subsequently, three approaches for handling low-quality data were evaluated regarding the preservation of several EDA-derived features: removing all artifacts, interpolating over removed artifacts, and retaining all artifacts. Specifically, multiple EDA features were assessed, derived from response detection (evaluated using F1, precision, recall) as well as EDA, phasic, and tonic features (assessed using absolute error), by comparing the simulated EDA data with and without the inserted artifacts, using the latter as ground truth. ResultsFor response detection, retaining artifacts resulted in the highest F1-scores, while interpolating over removed artifacts achieved the highest F1-scores for the phasic signal. The approaches did significantly differ in the mean error for the phasic but not for the tonic component and raw EDA. ConclusionThis work generated ambulatory EDA datasets of 200 h, containing 0.125 to 3 artifacts per minute, and showed that interpolation over removed artifacts was an effective approach to reconstruct phasic-derived features up to 2 artifacts per minute. The proposed simulation and evaluation methodology, which are easily customizable, offer opportunities for future research to develop and systematically compare signal quality indicators, decomposition methods, and response detectors for processing ambulatory EDA.

Harnessing Wearable Devices for Emotional Intelligence: Therapeutic Applications in Digital Health.

Emotional intelligence strives to bridge the gap between human and machine interactions. The application of such systems varies and is becoming more prominent as healthcare services seek to provide more efficient care by utilizing smart digital health apps. One application in digital health is the incorporation of emotion recognition systems as a tool for therapeutic interventions. To this end, a system is designed to collect and analyze physiological signal data, such as electrodermal activity (EDA) and electrocardiogram (ECG), from smart wearable devices. The data are collected from different subjects of varying ages taking part in a study on emotion induction methods. The obtained signals are processed to identify stimulus trigger instances and classify the different reaction stages, as well as arousal strength, using signal processing and machine learning techniques. The reaction stages are identified using a support vector machine algorithm, while the arousal strength is classified using the ResNet50 network architecture. The findings indicate that the EDA signal effectively identifies the emotional trigger, registering a root mean squared error (RMSE) of 0.9871. The features collected from the ECG signal show efficient emotion detection with 94.19% accuracy. However, arousal strength classification is only able to reach 60.37% accuracy on the given dataset. The proposed system effectively detects emotional reactions and can categorize their arousal strength in response to specific stimuli. Such a system could be integrated into therapeutic settings to monitor patients' emotional responses during therapy sessions. This real-time feedback can guide therapists in adjusting their strategies or interventions.

Analysis of EDA and Heart Rate Signals for Emotional Stimuli Responses

Abstract Emotion intelligence is a popular research topic that stems to bridge the gap between human-machine interaction. The use of such systems varies, however, the goal remains the same, to develop a robust and accurate model for detecting and identifying emotions. Physiological signals, such as electrodermal activity (EDA) and heart rate (HR), when coupled with facial expression analyses can bolster the efficacy of the recognition system. To this end, the aim of this study is to analyse EDA and HR signals to identify reactions to emotional stimuli. The data used in this work was collected from different subjects taking part in a separate study on emotion induction methods. The obtained physiological signals were processed to identify the stimulus trigger instances and detect trends between the different administered emotional stimuli. The results showed that the EDA signal was able to pinpoint the emotional trigger with a root mean squared error (RMSE) of 0.9871. The HR signal showed inconsistencies, however, a clear trend was observed between the emotion reaction and relaxation phase. This preliminary study assessment highlights the possibility of implementing data fusion in emotion recognition systems.