Emotion Analysis Using Electrodermal Signals and Spiking Deep Belief Network.

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.

Aiming at problems such as the difficulty of recognizing emotions in the elderly and the inability of traditional machine-learning models to effectively capture the nonlinear relationship between physiological signal data, a Recursive Map (RM) combined with a Vision Transformer (ViT) is proposed to recognize the emotions of the elderly based on Electroencephalogram (EEG), Electrodermal Activity (EDA), and Heart Rate Variability (HRV) signals. The Dung Beetle Optimizer (DBO) is used to optimize the variational modal decomposition of EEG, EDA, and HRV signals. The optimized decomposed time series signals are converted into two-dimensional images using RM, and then the converted image signals are applied to the ViT for the study of emotion recognition of the elderly. The pre-trained weights of ViT on the ImageNet-22k dataset are loaded into the model and retrained with the two-dimensional image data. The model is validated and compared using the test set. The research results show that the recognition accuracy of the proposed method on EEG, EDA, and HRV signals is 99.35%, 86.96%, and 97.20%, respectively. This indicates that EEG signals can better reflect the emotional problems of the elderly, followed by HRV signals, while EDA signals have poorer effects. Compared with Support Vector Machine (SVM), Naive Bayes (NB), and K-Nearest Neighbors (KNN), the recognition accuracy of the proposed method is increased by at least 9.4%, 11.13%, and 12.61%, respectively. Compared with ResNet34, EfficientNet-B0, and VGG16, it is increased by at least 1.14%, 0.54%, and 3.34%, respectively. This proves the superiority of the proposed method in emotion recognition for the elderly.

There has been growing interest in sleep tracking technologies utilizing ring-shaped wearable devices. This study aimed to develop a method for accurately estimating sleep duration, onset, and offset using such a device. Conventional wrist-worn accelerometer-based devices often show limited accuracy, particularly during periods of low movement. Likewise, algorithms relying solely on electrodermal activity (EDA) signals struggle to detect frequent wake episodes due to their low temporal resolution. To address these limitations, we developed a ring-shaped wearable device and a set of algorithms that integrate both accelerometer and EDA signals. The performance of the proposed algorithms was evaluated through a clinical study involving 25 participants, the majority of whom had sleep disorders. Results showed that combining these complementary signals enabled accurate detection of sleep onset and offset within 10min and maintained high accuracy even in conditions such as minimal movement or frequent wake episodes. These findings suggest that multimodal sensing may offer a promising direction for enhancing the reliability of sleep monitoring in real-world settings.

Emotion is characterized by dimensions of affective valence and arousal, either or both of which may be altered by sleep loss, thereby contributing to impaired regulatory functioning. Controlled laboratory studies of total sleep deprivation (TSD) generally show alterations in physiological arousal and affective state, but the relationship of affect and emotion with physiological arousal during TSD has not been well characterized. Established methods for examining physiological arousal include electrodermal activity (EDA) measures such as non-specific skin conductance responses (NSSCR) and skin conductance level (SCL). These measures are robust physiological markers of sympathetic arousal and have been linked to changes in experienced emotion. To explore the link between physiological arousal and affect during sleep deprivation, we investigated individuals’ EDA under TSD and its relationship to self-reported affect. We also investigated the relationship of EDA to two other measures known to be particularly sensitive to the arousal-decreasing effects of TSD, i.e., self-reported sleepiness and performance on a vigilant attention task. Data were drawn from three previously published laboratory experiments where participants were randomly assigned to either well-rested control (WRC) or 38 h of TSD. In this data set, comprising one of the largest samples ever used in an investigation of TSD and EDA (N = 193 with 74 WRC and 119 TSD), we found the expected impairing effects of TSD on self-reported affect and sleepiness and on vigilant attention. Furthermore, we found that NSSCR, but not SCL, were sensitive to TSD, with significant systematic inter-individual differences. Across individuals, the change in frequency of NSSCR during TSD was not predictive of the effect of TSD on affect, sleepiness, or vigilant attention, nor was it related to these outcomes during the rested baseline. Our findings indicate that while physiological arousal, as measured by EDA, may be useful for assessing TSD-related changes in non-specific arousal at the group level, it is not associated with individuals’ self-reported affect at rest nor their change in affect during TSD. This suggests that an essential aspect of the relationship between physiological arousal and self-reported affect is not well captured by EDA as measured by NSSCR.

Automatic artifact recognition and correction for electrodermal activity based on LSTM-CNN models

Tracking motion sickness in dynamic VR environments with EDA signals

Stress can lead to harmful conditions in the body, such as anxiety disorders and depression. One of the promising noninvasive methods, which has been widely used in detecting stress and emotion, is electrodermal activity (EDA). EDA has a tonic and phasic component called skin conductance level and skin conductance response (SCR). However, the components of the EDA cannot be directly extracted and need to be deconvolved to obtain it. The EDA signals were collected from 18 healthy subjects that underwent three sessions – Stroop test with increasing stress levels. The EDA signals were then deconvoluted by using continuous deconvolution analysis (CDA) and convex optimization approach to electrodermal activity (cvxEDA). Four features from the result of the deconvolution process were collected, namely sample average, standard deviation, first absolute difference, and normalized first absolute difference. Those features were used as the input of the classification process using the extreme learning machine (ELM). The output of classification was the stress level; mild, moderate, and severe. The visual of the phasic component using cvxEDA is more precise or smoother than the CDA's result. However, both methods could separate SCR from the original skin conductivity raw and indicate the small peaks from the SCR. The classification process results showed that both CDA and cvxEDA methods with 50 hidden layers in ELM had a high accuracy in classifying the stress level, which was 95.56% and 94.45%, respectively. This study developed a stress level classification method using ELM and the statistical features of SCR. The result showed that EDA could classify the stress level with over 94% accuracy. This system could help people monitor their mental health during overworking, leading to anxiety and depression because of untreated stress.

The system for measuring the electrodermal activity (EDA) signal occurring at the sweet glands in the human body was implemented in this study. The EDA measurement system (EDAMS) consisted of an algometer and the bio-potential measurement system (BPMS). Three experiments were performed using EDAMS. First, the linearity of the output voltage corresponding to the pressure being applied to an algometer was evaluated. The linearity of output voltage according to the pressure was 0.956. Second, the amplitude and the latency of the EDA signal at the left palm was obtained while applying the pressure stimuli to the left and right scapula. The latency of EDA signal was shorter whereas the amplitude of EDA signal was higher when the pressure applied was applied to the left scapula. Third, the amplitude and latency of the EDA was measured at left and right palm while increasing the pressure stimuli to the left scapula. The latency of EDA signal at left and right palm was decreased according to the intensity of pressure stimulus applied to the left scapula. However, the latency of the EDA signals did not show the linearity with respect to the pressure stimuli.

Machine learning (ML) has revolutionized healthcare by enhancing diagnostic capabilities because of its ability to analyze large datasets and detect minor patterns often overlooked by humans. This is beneficial, especially in pain recognition, where patient communication may be limited. However, ML models often face challenges such as memorization and sensitivity to adversarial examples. Regularization techniques like mixup, which trains models on convex combinations of data pairs, address these issues by enhancing model generalization. While mixup has proven effective in image, speech, and text datasets, its application to time-series signals like electrodermal activity (EDA) is less explored. This research uses ML for pain recognition with EDA signals from the BioVid Heat Pain Database to distinguish pain by applying mixup regularization to manually extracted EDA features and using a support vector machine (SVM) for classification. The results show that this approach achieves an average accuracy of 75.87% using leave-one-subject-out cross-validation (LOSOCV) compared to 74.61% without mixup. This demonstrates mixup’s efficacy in improving ML model accuracy for pain recognition from EDA signals. This study highlights the potential of mixup in ML as a promising approach to enhance pain assessment in healthcare.

This paper explores a novel method for anxiety detection in older adults using simple wristband sensors such as electrodermal activity (EDA) and photoplethysmogram (PPG) and a context-based feature. The proposed method for anxiety detection combines features from a single physiological signal with an experimental context-based feature to improve the performance of the anxiety detection model. The experimental data for this work are obtained from a year-long experiment on 41 healthy older adults (26 females and 15 males) in the age range 60–80 with mean age \(73.36 \pm 5.25\) during a Trier Social Stress Test (TSST) protocol. The anxiety level ground truth was obtained from State–Trait Anxiety Inventory (STAI), which is regarded as the gold standard to measure perceived anxiety. EDA and blood volume pulse (BVP) signals were recorded using a wrist-worn EDA and PPG sensor, respectively. 47 features were computed from EDA and BVP signal, out of which a final set of 24 significantly correlated features were selected for analysis. The phases of the experimental study are encoded as unique integers to generate the context feature vector. A combination of features from a single sensor with the context feature vector is used for training a machine learning model to distinguish between anxious and not-anxious states. Results and analysis showed that the EDA and BVP machine learning models that combined the context feature along with the physiological features achieved 3.37% and 6.41% higher accuracy, respectively, than the models that used only physiological features. Further, end-to-end processing of EDA and BVP signals was simulated for real-time anxiety level detection. This work demonstrates the practicality of the proposed anxiety detection method in facilitating long-term monitoring of anxiety in older adults using low-cost consumer devices.

The exponential increase of wearable health-tracking technologies offers new possibilities but also poses new challenges in signal processing to enable fitness monitoring through multimodal physiological recordings. Although there are several software tools used for post-processing in physiological computing applications, limitations in the analysis, incorporating signals from multiple sources, integrating contextual information and providing information visualization tools prevent a widespread use of this technology. To address these issues, we introduce PhysioLab, a multimodal processing Matlab tool for the data analysis of Electromyography (EMG), Electrocardiography (ECG) and Electrodermal Activity (EDA). The software is intended to facilitate the processing and comprehension of multimodal physiological data with the aim of assessing fitness in several domains. A unique feature of PhysioLab is that is informed by normative data grouped by age and sex, allowing contextualization of data based on users’ demographics. Besides signal processing, PhysioLab includes a novel approach to multivariable data visualization with the aim of simplifying interpretation by non-experts users. The system computes a set of ECG features based on heart rate variability analysis, EMG parameters to quantify force and fatigue levels, and galvanic skin level/responses from EDA signals. Furthermore, PhysioLab provides compatibility with data from multiple low-cost wearable sensors. We conducted an experiment with 17 community-dwelling older adults (64.5 ± 6.4) to assess the feasibility of the tool in characterizing cardiorespiratory profiles during physical activity. Correlation analyses and regression models showed significant interactions between physiology and fitness evaluations. Our results suggest novel ways that physiological parameters could be effectively used to complement traditional fitness assessment.

Fatigue driving is a common issue that often leads to traffic accidents, which has motivated numerous automatic driving fatigue detection methods based on various sources, especially reliable physiological signals. However, it still faces the challenges of accuracy, robustness and practicality, especially for the cross-subject detection. The fusion of multi-modality data can improve the effective estimation of driving fatigue. In this work, we take the advantages of user-friendly and multi-modality signals to build a Multi-Modality Attention Network (MMA-Net) for driver fatigue detection with frontal electroencephalography (EEG), electrodermal activity (EDA) and photoplethysmography (PPG) signals for a hybrid. Specifically, a signal adaptive coding module (SAC-M) has been constructed to fully excavate spatial-temporal information of signals, combining with an attention-based feature dissimilation module (AFD-M) to further obtain key comprehensive features. In addition, the performances of baseline models and state-of-the-art methods on signal sources with different window lengths are also compared. The cross-subject experiment is performed on two groups of 14 participants in the driving simulation experiment. The experimental results prove the superiority of our proposed method. It is possible to use the MMA-Net for driver fatigue detection with user-friendly multi-modality signals, such as our selected frontal EEG, EDA and PPG in real-world applications.

This article introduces a systematic review on arousal classification based on electrodermal activity (EDA) and machine learning (ML). From a first set of 284 articles searched for in six scientific databases, fifty-nine were finally selected according to various criteria established. The systematic review has made it possible to analyse all the steps to which the EDA signals are subjected: acquisition, pre-processing, processing and feature extraction. Finally, all ML techniques applied to the features of these signals for arousal classification have been studied. It has been found that support vector machines and artificial neural networks stand out within the supervised learning methods given their high-performance values. In contrast, it has been shown that unsupervised learning is not present in the detection of arousal through EDA. This systematic review concludes that the use of EDA for the detection of arousal is widely spread, with particularly good results in classification with the ML methods found.

In this paper, the authors have investigated the possibility of evaluating the well-being of the user, in relation to external stimuli, by means of continuous monitoring of physiological quantities. This preliminary analysis has interested the extraction of features from Electro-Dermal Activity (EDA) signal and their correlation with different emotional states (i.e., Arousal). A low-cost system for continuous monitoring of EDA has been described, together with the experimental setup and the processing techniques applied. A unique indicator, which combines the features extracted from the raw waveform, has been discussed in the paper and applied in post-processing. The implementation of the processing algorithms and the computation of the novel indicator allow to discriminate, with a statistical significance, the user perception, in case of high emotion events (i.e., from low level Arousal 6). More investigation is needed to improve the processing technique and validate the preliminary results obtained.

Pain is a multifaceted phenomenon that significantly affects a large portion of the global population. Objective pain assessment is essential for developing effective management strategies, which in turn contribute to more efficient and responsive healthcare systems. However, accurately evaluating pain remains a complex challenge due to subtle physiological and behavioural indicators, individual-specific pain responses, and the need for continuous patient monitoring. Automatic pain assessment systems offer promising, technology-driven solutions to support and enhance various aspects of the pain evaluation process. Physiological indicators offer valuable insights into pain-related states and are generally less influenced by individual variability compared to behavioural modalities, such as facial expressions. Skin conductance, regulated by sweat gland activity, and the heart's electrical signals are both influenced by changes in the sympathetic nervous system. Biosignals, such as electrodermal activity (EDA) and electrocardiogram (ECG), can, therefore, objectively capture the body's physiological responses to painful stimuli. This paper proposes a novel multi-modal ensemble deep learning framework that combines electrodermal activity and electrocardiogram signals for automatic pain recognition. The proposed framework includes a uni-modal approach (FCN-ALSTM-Transformer) comprising a Fully Convolutional Network, Attention-based LSTM, and a Transformer block to integrate features extracted by these models. Additionally, a multi-modal approach (CrossMod-Transformer) is introduced, featuring a dedicated Transformer architecture that fuses electrodermal activity and electrocardiogram signals. Experimental evaluations were primarily conducted on the BioVid dataset, with further cross-dataset validation using the AI4PAIN 2025 dataset to assess the generalisability of the proposed method. Notably, the CrossMod-Transformer achieved an accuracy of 87.52% on Biovid and 75.83% on AI4PAIN, demonstrating strong performance across independent datasets and outperforming several state-of-the-art uni-modal and multi-modal methods. These results highlight the potential of the proposed framework to improve the reliability of automatic multi-modal pain recognition and support the development of more objective and inclusive clinical assessment tools.