Recognition of multiple drivers’ emotional state

Introduction. Disability to recognize emotional and mental states from other people’s facial expressions is acknowledged as one of the major developmental-behavioral problems in autistic children. Therefore, this study aimed to investigate the effect of traditional and computer emotional facial training on the recognition of facial emotional states in children with autism and mother-child relation and parental competence in mothers of autistic children. Material and Methods. The present was clinical trial study. The children in the both groups were trained during 10 sessions. In traditional group children training using 40 images related to four situations as happiness, sadness, anger, and fear. The children in the computer group trained with the Let’s Face It software. These questionnaires were completed one day before and one day and one week after performing the intervention. The data were analyzed using SPSS software. Results. Findings showed that emotional state training has improved the identification of emotional states (p = 0.01), mother-child relation (p = 0.001) and parental competence (p = 0.001) in the both groups. There was also a statistically significant difference between the scores of identifying face emotional states (p = 0.01), mother-child relation (p = 0.001) and parental competence (p = 0.001) in the both groups. But training in traditional group was effectiveness than computer grope. Conclusion. Teaching emotional face to children with autism improved the recognition of emotional states by these children, and subsequently improved the mother-child relationship and motherly sense of competence in the mothers of these children.

Recognition of emotional states using frequency effective connectivity maps through transfer learning approach from electroencephalogram signals

Background: One of the significant problems in human societies that has existed for a long time. Unfortunately, this problem has assumed more serious dimensions in our communities. One of the challenges faced by methamphetamine abusers is a disorder in emotion regulation, social cognition, and recognition of emotional states. Objectives: In this regard, the purpose of this research was to investigate the effect of emotion regulation training on the recognition of emotional states and social cognition in methamphetamine abusers. Patients and Methods: This research is classified as applied in terms of purpose and quasi-experimental in terms of design, using a pre-test and post-test format with a control group. The study sample consisted of 30 methamphetamine abusers who referred to the TC treatment center (TC) of the Baran Institute in Tabriz in 2024. Sampling was done purposefully, and 15 individuals were randomly assigned to the experimental group and 15 to the control group. To collect data, Bakker's Emotional Processing Questionnaire (2007) and Chen and Lin's Social Cognition Questionnaire (2019) were used. A pre-test was administered to both groups, after which the experimental group received an emotion regulation training protocol over 8 sessions. The research hypotheses were tested using the univariate covariance method. Results: The research findings indicated that emotion regulation training had a positive and significant effect on the recognition of emotional states and social cognition in methamphetamine abusers, increasing the levels of these variables (P < 0.05). Conclusions: This study demonstrated that emotion regulation training improved the recognition of emotional states and social cognition in methamphetamine abusers. Therefore, by offering effective strategies in this area, meaningful steps can be taken to reduce the problems faced by drug abusers.

Companion robots play an important role to accompany humans and provide emotional support, such as reducing human social isolation and loneliness. Based on recognizing human partner's mental states, a companion robot is able to dynamically adjust its behaviors, and make human-robot interaction smoother and natural. Human emotion has been recognized by many modalities like facial expression and voice. Neurophysiological signals have shown promising results in emotion recognition, since it is an innate signal of human brain which cannot be faked. In this paper, emotional state recognition using a neurophysiology method is studied to guide and modulate companion-robot navigation to enhance its social capabilities. Electroencephalogram (EEG), a type of neurophysiological signals, is used to recognize human emotional state, and then feed into a navigation path planning algorithm for controlling a companion robot's routes. Simulation results show that mobile robot presents navigation behaviors modulated by dynamic human emotional states.

Emotion recognition is considered as one of the most vital and challenging studies in the domain of computer vision. Due to the ongoing pandemic the people’s emotional and mental state is getting affected due to less physical interaction and increased virtual interaction. The motivation of the work lies in the recognition and monitoring of the emotional state of the human beings in live environment with higher accuracy rate and fast recognition time so as to keep them aware about their emotional state in the pandemic situation. Emotion recognition is achieved using the proposed deep convolution neural network (DCNN) using custom Gabor filter with 85.8% accuracy. Emotion recognition is also being investigated and compared with other deep learning models such as AlexNet, VGG-Net for testing the efficacy. When the emotion of the person will be found sad or angry then an alert sound is played and an email alert is automatically sent.KeywordsEmotion recognitionDeep learningGabor filterPandemicReal-time alertDCNNPreprocessing

Incongruity-aware multimodal physiology signals fusion for emotion recognition

Статья написана в рамках исследования по проекту КН МОН РК № АРО9259875 «Разработка алгоритмов распознавания негативных эмоциональных состояний военнослужащих ВС РК (Руководитель Мадалиева З.Б.)». Материл статьи носит обзорный характер и посвящен теоретическим и практическим аспектам проблемы распознавания негативных эмоциональных состояний. В частности, представлены краткий обзор теоретических аспектов распознавания эмоциональных состояний, проанализирован практический опыт распознавания эмоциональных состояний, сделан краткий анализ современного программного обеспечения, позволяющего автоматически оценивать эмоциональные состояния человека. Выделены три основные тенденции исследований проблемы распознавания эмоциональных состояний, установлена взаимосвязь проблемы с изучением эмоционального интеллекта и эмоциональной гибкости. Приводится краткий обзор методов распознавания эмоций, основанных на различных входных данных, таких как биометрия, видеоканал или поведенческие данные. На основе ряда статей обосновывается, что процессы регуляции эмоций являются фактором психологического здоровья человека, а успешное их распознавание связано с нормальным развитием индивида. В разделе Дискуссия выделяются и анализируются различные современные направления в реализации проблемы распознавания эмоциональных состояний. В заключении намечаются перспективы данной отрасли, её связь с другими прикладными областями исследований. Ключевые слова: эмоциональные состояния, распознавание эмоций, эмоциональный интеллект, эмоциональная гибкость, регуляция эмоций, эмоциональное реагирование. Мақала ҚР БҒМ ҒК № АРО9259875 «ҚР ҚК әскери қызметшілерінің жағымсыз эмоционалдық жай-күйін тану алгоритмдерін әзірлеу (Жетекшісі Мадалиева З. Б.)» жобасы бойынша зерттеу шеңберінде жазылған. Мақаланың материалы шолу сипатына ие және теріс эмоционалды жағдайларды тану мәселесінің теориялық және практикалық аспектілеріне арналған. Атап айтқанда, эмоционалды жағдайларды танудың теориялық аспектілеріне қысқаша шолу жасалады, эмоционалды жағдайларды танудың практикалық тәжірибесі талданады, адамның эмоционалды жағдайларын автоматты түрде бағалауға мүмкіндік беретін заманауи бағдарламалық жасақтамаға қысқаша талдау жасалады. Эмоционалды жағдайларды тану мәселесін зерттеудің үш негізгі бағыты анықталды, эмоционалды интеллект пен эмоционалды икемділікті зерттеудегі проблеманың байланысы анықталды. Биометрия, бейне арна немесе мінез-құлық деректері сияқты әртүрлі енгізулерге негізделген эмоцияны тану әдістеріне қысқаша шолу жасалады. Бір қатар мақалаларға сүйене отырып, эмоцияларды реттеу процестері адамның психологиялық денсаулығының факторы болып табылады және оларды сәтті тану адамның қалыпты дамуымен байланысты. Пікір талас бөлімінде эмоционалды жағдайларды тану мәселесін жүзеге асырудағы әртүрлі заманауи бағыттар бөлініп, талданады. Қорытындылай келе, осы саланың перспективалары, оның басқа қолданбалы зерттеу салаларымен байланысы көрсетілген. Түйінді сөздер: эмоционалды күйлер, эмоцияны тану, эмоционалды интеллект, эмоционалды икемділік, эмоцияны реттеу, эмоционалды реакция. The article was written as part of a study on the project of the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan № AРO9259875 "Development of algorithms for recognizing negative emotional states of servicemen of the Armed Forces of the Republic of Kazakhstan (The head of Madalieva Z.B)". The material of the article is of an overview nature and is devoted to theoretical and practical aspects of the problem of recognizing negative emotional states. In particular, a brief overview of the theoretical aspects of the recognition of emotional states is presented, the practical experience of the recognition of emotional states is analyzed, and a brief analysis of modern software is made that automatically assesses a person's emotional states. Three main trends of research on the problem of recognition of emotional states are identified; the relationship of the problem with the study of emotional intelligence and emotional flexibility is established. A brief overview of emotion recognition methods based on various input data, such as biometrics, video channel or behavioral data is provided. Based on a number of articles, it is substantiated that the processes of emotion regulation are a factor of human psychological health, and their successful recognition is associated with the normal development of the individual. The Discussion section highlights and analyzes various modern trends in the implementation of the problem of recognition of emotional states. In conclusion, the prospects of this industry and its connection with other applied research areas are outlined. Keywords: emotional states, emotion recognition, emotional intelligence, emotional flexibility, emotion regulation, emotional response.

AI-based emotion recognition has emerged as a critical component of affect-aware educational technologies, particularly in online, large-scale, and technology-mediated learning environments where direct observation of learners’ emotional states is limited. By leveraging facial expressions, speech, physiological signals, and interaction data, emotion-aware systems aim to support adaptive instruction, learner engagement, and instructional decision-making. Despite rapid technical progress, the integration of emotion recognition into authentic educational practice remains constrained by dataset limitations, cultural bias, weak pedagogical alignment, and unresolved ethical concerns. This article presents a critical and theory-informed review of recent advances in AI-based emotion recognition in education, with particular emphasis on deep learning architectures and multimodal fusion approaches, while highlighting persistent challenges and future research directions.Purpose:This study aims to critically examine the current state of AI-based emotion recognition in educational contexts by analyzing dominant modeling paradigms, sensing modalities, application domains, and ethical implications, in order to identify key gaps between technical development and pedagogical practice.Methods/Study design/approach:The study adopts a narrative literature review approach grounded in educational and affective computing theories. Representative and influential studies were analyzed across facial, speech, physiological, and multimodal emotion recognition systems, with emphasis on deep learning, attention-based, and transformer architectures. The review synthesizes findings related to system performance, educational applications, dataset characteristics, and ethical considerations.Result/Findings:The findings indicate that multimodal emotion recognition systems generally outperform unimodal approaches in detecting learning-relevant affective states such as engagement, frustration, and confusion. Advances in deep learning, particularly attention-based and transformer models, improve temporal modeling and robustness. However, most systems rely on non-educational datasets, demonstrate limited cultural generalizability, and prioritize recognition accuracy over pedagogical interpretability and long-term educational impact. Ethical challenges related to privacy, consent, bias, and emotional surveillance remain insufficiently addressed.Novelty/Originality/Value:This study provides an integrative and pedagogically grounded synthesis that bridges affective computing research with educational theory and governance perspectives. By reframing AI-based emotion recognition as a decision-support mechanism rather than an automated control system, the article offers a conceptual foundation for responsible, culturally inclusive, and ethically aligned deployment of emotion-aware AI in education, serving as a reference for future interdisciplinary research and policy development.

Emotion recognition from single-channel EEG signals using a two-stage correlation and instantaneous frequency-based filtering method

The purpose of this paper is the design of a mobile bio-feedback device that collects bio-signals from people and it assigns them to anxiety levels. Student emotional state recognition might be approved a significant support providing self-regulation besides teaching and coaching quality. The physiological reactions of the body affect the conductivity of the skin, skin temperature and heart rate. Based on this information the body produces bio-signals that can be processed to recognize basic human emotional states. Usually stressful situations result in increased skin conductivity. Another physiological reaction to increased human anxiety is decreased skin temperature. Also people under stressful conditions usually have increased heart rate values. Emotional state recognition and regulation may be an important factor in achieving better performance. Furthermore we could assert that there is often a skeptical attitude toward human contact with sensors which sometimes are annoying and may prove inconvenient especially when attached to fingers. In this work we focus on improving interaction with the device by designing a mobile version that gives users freedom of movement and mobility. There is a widespread adoption of mobile smartphones and tablets today in our everyday lives. So we can assume that everyone has access to one and developing an interface that links the bio-feedback device to them will broaden the scenarios where it could be used. This paper will also examine an activity where a mobile version of the device could be beneficial for the user, suggest a prototype design for a mobile adaptation of the user interface and lastly describe some new use cases that highlight the positive effect a mobile version of the device can have.

Emotion management is a critical psychological skill for athletes, deserving significant attention during the foundational training phase. Early intervention in athletes’ emotional states allows coaches to devise effective training plans, ultimately enhancing competition performance. Emotions can be evaluated through facial expressions and physiological signals, providing a basis for detecting athletes’ training emotions. This study explores the biomechanical characteristics of facial expressions to recognize emotions. First, the relationship between facial expressions and facial Action Units (AUs) is analyzed, identifying AU combinations that effectively represent facial expressions. Considering the temporal and spatial dynamics of facial expressions, AUs are used as feature inputs for an SVM model to classify athletes’ training emotions. Subsequently, emotional expressions are mapped to corresponding emotional states, introducing an emotion index to quantify athletes’ training emotions. Finally, quantitative emotion recognition is achieved based on transient facial expressions observed during training. This research provides a theoretical framework for coaches and athletes to enhance emotional management through biomechanically informed training approaches.

This study proposes a system that can recognize human emotional state from bio-signal. The technology is provided to improve the interaction between humans and computers to achieve an effective human–machine that is capable for intelligent interaction. The proposed method is able to recognize six emotional states, such as joy, happiness, fear, anger, despair, and sadness. These set of emotional states are widely used for emotion recognition purposes. The result shows that the proposed method can distinguish one emotion compared to all other possible emotional states. The method is composed of two steps: 1) multi-modal bio-signal evaluation and 2) emotion recognition using artificial neural network. In the first step, we present a method to analyze and fix human sensitivity using physiological signals, such as electroencephalogram, electrocardiogram, photoplethysmogram, respiration, and galvanic skin response. The experimental analysis shows that the proposed method has good accuracy performance and could be applied on many human–computer interaction devices for emotion detection.

Emotion recognition using physiological signals has received a great deal of attention in recent years. Local features of signals have been more commonly used in this field of research. Brain connectivity measures have also been applied to emotion recognition in last few years. Considering the extensive neural interactions between the brain and heart and the intrinsic nervous system of the heart, and bearing in mind the different autonomic nervous system activity in various emotional states, we expect that the connectivity between the heart and brain can be measured as a discriminative feature for the purpose of emotion recognition. In the present paper, we investigate the possibility of recognizing four music-induced emotions on the basis of functional connectivity between the heart and brain. One of the discriminating features in multichannel/multisource signal processing is Mutual Information. Herein, mutual information is estimated between the power of three channels of forehead biosignals in six sub-bands and the normalized high frequency component of HRV. Subject-dependent and subject-independent classification studies were carried out using SVM classifier, which resulted in an average accuracy of 95.7% and 86.9%, respectively. The promising results suggest that the amount of information which our brain and heart provide about each other is different in each emotional state and may be used to distinguish between them.

Emotional inference by recognizing facial expressions in an image or video has been actively conducted for several years, for example, visual emotion recognition and emotional state recognition. The limitations of illumination conditions, variations of head poses, and partial occlusion are the most challenging problems in image processing. Furthermore, recognizing and inferring insignificant objects in the background is complex. These factors degrade performance. We collected evidence from human sensing through a psychological approach rather than a numerical approach, and converted it into a language through fuzzy theory. This can not only be intuitively explained, but it is also possible to evaluate and analyze numerically. The predicted inference accuracy increased by approximately 9.7% compared with the simple average method.

REICHENBACH, LISA, and MASTERS, JOHN C. Children's Use of Expressive and Contextual Cues in Judgments ofEmotion. CHILD DEVELOPMENT, 1983, 54, 993-1004. In 2 experiments, preschool and third-grade children judged the happy, sad, angry, or neutral emotional states of other children on the basis of expressive cues alone (slides of children's facial expressions), contextual cues alone (vignettes describing the stimulus children's emotion-inducing experiences), or both expressive and contextual cues. Older children were more accurate than younger only when given multiple cues. For children of both ages, contextual cues led to greater accuracy in the recognition of emotional states than did expressive cues. When multiple cues were inconsistent with one another, younger children relied more on expressive cues, while older children preferred contextual ones. Children from disrupted families were less accurate in judging emotion in peers, and their misjudgments involved fewer judgments of happiness and more judgments of anger. Results are discussed in terms of cognitive-developmental and social learning processes influencing children's judgment of emotion in others.