Smart textile-based wearable sweat sensors have recently received a lot of attention due to their potential for use in personal medical monitoring. They have a variety of desirable qualities, including low cost, easy implementation, stretchability, flexibility, and light weight. Wearable sweat sensors are a potential approach for personalized medical devices because of these features. Moreover, real-time textile-based sweat sensors can easily monitor health by analyzing the sweat produced by the human body. We reviewed the most recent advancements in wearable sweat sensors from the fabrication, materials, and disease detection and monitoring perspectives. To integrate real-time biosensors with electronics and introduce advancements to the field of wearable technology, key chemical constituents of sweat, sweat collection technologies, and concerns of textile substrates are elaborated. Perspectives for building wearable biosensing systems based on sweat are reviewed, as well as the methods and difficulties involved in enhancing wearable sweat-sensing performance.

Detection of emotional states plays a prominent role in affective computing, decision-making, and healthcare. Physiological signals are an ideal target for continuous and objective assessment of emotional states. Electrodermal activity (EDA) is considered one of the most effective and widely used markers of sympathetic activation. However, emotional state assessment using EDA is challenging. We hypothesize that this is caused by the impossibility of current EDA analysis tools to capture the non-linear variations of the signal. Selecting an appropriate non-linear signal processing method will allow more effective emotion recognition using EDA signals. In this study, we have compared the performance of four non-linear signal processing methods— Improved symbolic aggregate approximation (isaxEDA), Complexity analysis (comEDA), Topological analysis (topEDA), and Network theory-based analysis (netEDA)—for classifying emotional states using EDA signals. For this, EDA signals were obtained from the publicly available Continuously Annotated Signals of Emotion (CASE) dataset. Features are extracted from each of the non-linear signal processing methods, encompassing aspects related to the dynamics, complexity, structure, and network properties of EDA. Subsequently, the extracted features from each method were employed as input for four machine learning classifiers: Naïve Bayes, Random Forest, k-Nearest Neighbors, and Support Vector Machine, which were validated using leave-one-subject-out cross-validation. The results demonstrate that isaxEDA achieved the highest performance, with an F1-score of 65% using an SVM classifier. The study supports the suitability of non-linear EDA signal processing to improve emotion recognition, which could be used to detect mental health conditions such as anxiety and depression.

Emotions play an essential role in human life as they are linked to well-being and markers of various diseases. Physiological signals can be used to assess emotions objectively and continuously. Electrodermal activity (EDA) is particularly interesting to assess emotions due to its relationship with the sympathetic nervous system. EDA signals are composed of tonic and phasic components that react differently to emotions, and various methods are available to obtain these components. However, the most accurate and effective method used for emotion analysis based on the phasic component of EDA has not been reported so far. This study presents the comparison of various EDA decomposition methods used for emotion detection based on levels of affective dimensions (arousal and valence) levels (low vs. high). In this study, EDA was decomposed using six methods, namely convex optimization-based EDA (cvxEDA), Time-Varying Sympathetic Activity (TVSymp), continuous decomposition analysis (CDA), dynamic causal modeling (DCM), BayesianEDA, and sparse deconvolution approach (Sparse). To test the most usable decomposition method for objective assessment of emotions, EDA signals from the database for emotion analysis using physiological signals (DEAP) were obtained. Statistical, morphological, Hjorth, and non-linear Entropy features were extracted from the phasic component obtained from each decomposition method and fed to the Random Forest and support vector machine classifiers for detection of arousal and valence affective dimension. TVSymp yielded the highest F1 score of 72.79% and 73.49% for classifying Arousal and Valence, respectively.

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.

Recognition of dichotomous emotional states such as happy and sad play important roles in many aspects of human life. Existing literature has recorded diverse attempts in extracting physiological and non-physiological traits to record these emotional states. Selection of the right instrumental approach for measuring these traits plays a critical role in emotion recognition. Moreover, various stimuli have been used to induce emotions. Therefore, there is a current need to perform a comprehensive overview of instrumental approaches and their outcomes for the new generation of researchers. In this direction, this study surveys the instrumental approaches in discriminating happy and sad emotional states that are elicited using audio-visual stimuli. A comprehensive literature review is performed using PubMed, Scopus, and ACM digital library repositories. The reviewed articles are classified with respect to the i) stimulation modality, ii) acquisition protocol, iii) instrumentation approaches, iv) feature extraction, and v) classification methods. In total, 39 research articles were published on the selected topic of instrumental approaches in differentiating dichotomous emotional states using audio-visual stimuli between January 2011 and April 2021. The majority of the papers used physiological traits, namely electrocardiogram, electrodermal activity, heart rate variability, photoplethysmogram, and electroencephalogram based instrumental approaches for recognizing the emotional states. The results show that only a few articles have focused on audio-visual stimuli for the elicitation of happy and sad emotional states. This review is expected to seed research in the areas of standardization of protocols, enhancing the diagnostic relevance of these instruments, and extraction of more reliable biomarkers.