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Journal articles on the topic 'Affective state recognition'

Author: Grafiati

Published: 8 June 2024

Last updated: 31 July 2025

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1

Murali Krishna, P., R. Pradeep Reddy, Veena Narayanan, S. Lalitha, and Deepa Gupta. "Affective state recognition using audio cues." Journal of Intelligent & Fuzzy Systems 36, no. 3 (2019): 2147–54. http://dx.doi.org/10.3233/jifs-169926.

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Blanco, M. J., F. Valle-Inclán, and J. Lamas. "Affective state dependence in a recognition task." Revista de Psicología Social 1, no. 1 (1986): 79–82. http://dx.doi.org/10.1080/02134748.1986.10821545.

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Neethirajan, Suresh. "Affective State Recognition in Livestock—Artificial Intelligence Approaches." Animals 12, no. 6 (2022): 759. http://dx.doi.org/10.3390/ani12060759.

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Farm animals, numbering over 70 billion worldwide, are increasingly managed in large-scale, intensive farms. With both public awareness and scientific evidence growing that farm animals experience suffering, as well as affective states such as fear, frustration and distress, there is an urgent need to develop efficient and accurate methods for monitoring their welfare. At present, there are not scientifically validated ‘benchmarks’ for quantifying transient emotional (affective) states in farm animals, and no established measures of good welfare, only indicators of poor welfare, such as injury

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Chen, Zhimin, and David Whitney. "Tracking the affective state of unseen persons." Proceedings of the National Academy of Sciences 116, no. 15 (2019): 7559–64. http://dx.doi.org/10.1073/pnas.1812250116.

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Emotion recognition is an essential human ability critical for social functioning. It is widely assumed that identifying facial expression is the key to this, and models of emotion recognition have mainly focused on facial and bodily features in static, unnatural conditions. We developed a method called affective tracking to reveal and quantify the enormous contribution of visual context to affect (valence and arousal) perception. When characters’ faces and bodies were masked in silent videos, viewers inferred the affect of the invisible characters successfully and in high agreement based sole

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M. M. Al Qudah, Mustafa, Ahmad S. A. Mohamed, and Syaheerah L. Lutfi. "Affective State Recognition Using Thermal-Based Imaging: A Survey." Computer Systems Science and Engineering 37, no. 1 (2021): 47–62. http://dx.doi.org/10.32604/csse.2021.015222.

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Li, Fang, and Dan Zhang. "Transformer-Driven Affective State Recognition from Wearable Physiological Data in Everyday Contexts." Sensors 25, no. 3 (2025): 761. https://doi.org/10.3390/s25030761.

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The rapid advancement in wearable physiological measurement technology in recent years has brought affective computing closer to everyday life scenarios. Recognizing affective states in daily contexts holds significant potential for applications in human–computer interaction and psychiatry. Addressing the challenge of long-term, multi-modal physiological data in everyday settings, this study introduces a Transformer-based algorithm for affective state recognition, designed to fully exploit the temporal characteristics of signals and the interrelationships between different modalities. Utilizin

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Meng, Hongying, and Nadia Bianchi-Berthouze. "Affective State Level Recognition in Naturalistic Facial and Vocal Expressions." IEEE Transactions on Cybernetics 44, no. 3 (2014): 315–28. http://dx.doi.org/10.1109/tcyb.2013.2253768.

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Mavromoustakos Blom, Paris, Sander Bakkes, Chek Tan, et al. "Towards Personalised Gaming via Facial Expression Recognition." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 10, no. 1 (2021): 30–36. http://dx.doi.org/10.1609/aiide.v10i1.12707.

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In this paper we propose an approach for personalising the space in which a game is played (i.e., levels) dependent on classifications of the user's facial expression — to the end of tailoring the affective game experience to the individual user. Our approach is aimed at online game personalisation, i.e., the game experience is personalised during actual play of the game. A key insight of this paper is that game personalisation techniques can leverage novel computer vision-based techniques to unobtrusively infer player experiences automatically based on facial expression analysis. Specifically

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Ab. Aziz, Nor Azlina, Tawsif K., Sharifah Noor Masidayu Sayed Ismail, et al. "Asian Affective and Emotional State (A2ES) Dataset of ECG and PPG for Affective Computing Research." Algorithms 16, no. 3 (2023): 130. http://dx.doi.org/10.3390/a16030130.

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Affective computing focuses on instilling emotion awareness in machines. This area has attracted many researchers globally. However, the lack of an affective database based on physiological signals from the Asian continent has been reported. This is an important issue for ensuring inclusiveness and avoiding bias in this field. This paper introduces an emotion recognition database, the Asian Affective and Emotional State (A2ES) dataset, for affective computing research. The database comprises electrocardiogram (ECG) and photoplethysmography (PPG) recordings from 47 Asian participants of various

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Erle, Thorsten M., and Friederike Funk. "Visuospatial and Affective Perspective-Taking." Social Psychology 53, no. 5 (2022): 315–26. http://dx.doi.org/10.1027/1864-9335/a000504.

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Abstract. Perspective-taking is the ability to intuit another person’s mental state. Historically, cognitive and affective perspective-taking are distinguished from visuospatial perspective-taking because the content these processes operate on is too dissimilar. However, all three share functional similarities. Following recent research showing relations between cognitive and visuospatial perspective-taking, this article explores links between visuospatial and affective perspective-taking. Data of three preregistered experiments suggest that visuospatial perspective-taking does not improve emo

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Schmidt, Philip, Attila Reiss, Robert Dürichen, and Kristof Van Laerhoven. "Wearable-Based Affect Recognition—A Review." Sensors 19, no. 19 (2019): 4079. http://dx.doi.org/10.3390/s19194079.

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Affect recognition is an interdisciplinary research field bringing together researchers from natural and social sciences. Affect recognition research aims to detect the affective state of a person based on observables, with the goal to, for example, provide reasoning for the person’s decision making or to support mental wellbeing (e.g., stress monitoring). Recently, beside of approaches based on audio, visual or text information, solutions relying on wearable sensors as observables, recording mainly physiological and inertial parameters, have received increasing attention. Wearable systems ena

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Samyuktha S and Sarwath Unnisa. "Emotional Speech Recognition using CNN model." International Journal of Information Technology, Research and Applications 4, no. 1 (2025): 30–38. https://doi.org/10.59461/ijitra.v4i1.164.

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Speech Emotion Recognition (SER) is a new area of artificial intelligence that deals with recognizing human emotions from speech signals. Emotions are an important aspect of communication, affecting social interactions and decision-making processes. This paper introduces a complete SER system that uses state-of-the-art deep learning methods to recognize emotions like Happy, Sad, Angry, Neutral, Surprise, Calm, Fear, and Disgust. The suggested model uses Mel-Spectrograms, MFCCs, and Chroma features for efficient feature extraction. Convolutional layers are utilized to capture complex patterns i

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Verma, Bindu, and Ayesha Choudhary. "Affective state recognition from hand gestures and facial expressions using Grassmann manifolds." Multimedia Tools and Applications 80, no. 9 (2021): 14019–40. http://dx.doi.org/10.1007/s11042-020-10341-6.

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Beyers, Christiaan. "Moral Subjectivity and Affective Deficit in the Transitional State." Social Analysis 59, no. 4 (2015): 66–82. http://dx.doi.org/10.3167/sa.2015.590405.

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In the context of transitional justice, how does the reinvented state come to be assumed as a social fact? South African land restitution interpellates victims of apartheid- and colonial-era forced removals as claimants, moral and legal subjects of a virtuous 'new' state. In the emotional narratives of loss and suffering called forth in land claim forms, the state is addressed as a subject capable of moral engagement. Claim forms also 'capture' affects related to the event of forced removals as an unstable political resource. However, within an ultimately legal and bureaucratic process, the de

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Zhang, Yunxia, Xin Li, Changming Zhao, et al. "Affective EEG-Based Person Identification Using Channel Attention Convolutional Neural Dense Connection Network." Security and Communication Networks 2021 (November 22, 2021): 1–10. http://dx.doi.org/10.1155/2021/7568460.

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In the biometric recognition mode, the use of electroencephalogram (EEG) for biometric recognition has many advantages such as anticounterfeiting and nonsteal ability. Compared with traditional biometrics, EEG biometric recognition is safer and more concealed. Generally, EEG-based biometric recognition is to perform person identification (PI) through EEG signals collected by performing motor imagination and visual evoked tasks. The aim of this paper is to improve the performance of different affective EEG-based PI using a channel attention mechanism of convolutional neural dense connection net

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Sapiński, Tomasz, Dorota Kamińska, Adam Pelikant, and Gholamreza Anbarjafari. "Emotion Recognition from Skeletal Movements." Entropy 21, no. 7 (2019): 646. http://dx.doi.org/10.3390/e21070646.

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Automatic emotion recognition has become an important trend in many artificial intelligence (AI) based applications and has been widely explored in recent years. Most research in the area of automated emotion recognition is based on facial expressions or speech signals. Although the influence of the emotional state on body movements is undeniable, this source of expression is still underestimated in automatic analysis. In this paper, we propose a novel method to recognise seven basic emotional states—namely, happy, sad, surprise, fear, anger, disgust and neutral—utilising body movement. We ana

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Rani, Pramila, Nilanjan Sarkar, Craig A. Smith, and Leslie D. Kirby. "Anxiety detecting robotic system – towards implicit human-robot collaboration." Robotica 22, no. 1 (2004): 85–95. http://dx.doi.org/10.1017/s0263574703005319.

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A novel affect-sensitive human-robot cooperative framework is presented in this paper. Peripheral physiological indices are measured through wearable biofeedback sensors to detect the affective state of the human. Affect recognition is performed through both quantitative and qualitative analyses. A subsumption control architecture sensitive to the affective state of the human is proposed for a mobile robot. Human-robot cooperation experiments are performed where the robot senses the affective state of the human and responds appropriately. The results presented here validate the proposed framew

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Varun, Chand H., Chrisanthus Ashley, Thampi Athul, S. Dayal, and S. Dhanup. "Emotion Recognition Techniques using Facial Images: A Review." Advancement in Image Processing and Pattern Recognition 7, no. 1 (2023): 7–17. https://doi.org/10.5281/zenodo.10393888.

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<em>Emotion recognition has emerged as a critical area of research, with applications in fields such as human- computer interaction, affective computing, and mental health assessment. This paper presents a comprehensive review of state-of-the-art facial emotion recognition technologies. In addition to reviewing unimodal facial emotion recognition approaches, this paper explores the integration of EEG signals with facial expressions to improve the accuracy and robustness of emotion recognition systems. The aim of this study is to analyze and compare different approaches employed for facial emot

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Brown, Terry M., Robert N. Golden, and Dwight L. Evans. "Organic Affective Psychosis Associated with the Routine Use of Non-prescription Cold Preparations." British Journal of Psychiatry 156, no. 4 (1990): 572–75. http://dx.doi.org/10.1192/bjp.156.4.572.

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A patient experienced an organic affective psychosis on three separate occasions after taking recommended doses of non-prescription cold/sinus preparations. The possible underlying pharmacological mechanisms of this clinical reaction lend support to the cholinergic-adrenergic balance hypothesis of affective disorders. Recognition of this acute drug-induced state can lead to appropriate short-term pharmacotherapy and can prevent misdiagnosis of a major affective disorder or schizophrenia.

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Yu, Dong Mei. "Affective-Cognitive Reward Model Based on Emotional Interactions." Applied Mechanics and Materials 333-335 (July 2013): 1357–60. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.1357.

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In this paper, author presents a new computational model where both the results of emotional interactions from the intrinsic emotion and recognition and the extrinsic environmental stimulation (another agent), the two parts play an important role in everyday life. We take a kind of six basic emotion states (happiness, surprise, anger, fear, sadness, disgust), and updates its state depending on its current emotional and cognitive state and meeting with another one in random environment. At last, we also design some experiments to verify the effects of affective cognitive algorithm. Those experi

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Djara, Tahirou, Abdoul Matine Ousmane, and Antoine Vianou. "Emotional State Recognition Using Facial Expression, Voice, and Physiological Signal." International Journal of Robotics Applications and Technologies 6, no. 1 (2018): 1–20. http://dx.doi.org/10.4018/ijrat.2018010101.

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Emotion recognition is an important aspect of affective computing, one of whose aims is the study and development of behavioral and emotional interaction between human and machine. In this context, another important point concerns acquisition devices and signal processing tools which lead to an estimation of the emotional state of the user. This article presents a survey about concepts around emotion, multimodality in recognition, physiological activities and emotional induction, methods and tools for acquisition and signal processing with a focus on processing algorithm and their degree of re

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Al Qudah, Mustafa, Ahmad Mohamed, and Syaheerah Lutfi. "Analysis of Facial Occlusion Challenge in Thermal Images for Human Affective State Recognition." Sensors 23, no. 7 (2023): 3513. http://dx.doi.org/10.3390/s23073513.

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Several studies have been conducted using both visual and thermal facial images to identify human affective states. Despite the advantages of thermal facial images in recognizing spontaneous human affects, few studies have focused on facial occlusion challenges in thermal images, particularly eyeglasses and facial hair occlusion. As a result, three classification models are proposed in this paper to address the problem of thermal occlusion in facial images, with six basic spontaneous emotions being classified. The first proposed model in this paper is based on six main facial regions, includin

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Dvoynikova, Anastasia, Maxim Markitantov, Elena Ryumina, et al. "Analysis of infoware and software for human affective states recognition." Informatics and Automation 21, no. 6 (2022): 1097–144. http://dx.doi.org/10.15622/ia.21.6.2.

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The article presents an analytical review of research in the affective computing field. This research direction is a component of artificial intelligence, and it studies methods, algorithms and systems for analyzing human affective states during interactions with other people, computer systems or robots. In the field of data mining, the definition of affect means the manifestation of psychological reactions to an exciting event, which can occur both in the short and long term, and also have different intensity. The affects in this field are divided into 4 types: affective emotions, basic emoti

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Hudlicka, Eva, and Jonathan Pfautz. "Once More with Feeling: Augmenting Recognition Primed Decision Making with Affective Factors." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 46, no. 3 (2002): 303–7. http://dx.doi.org/10.1177/154193120204600319.

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Although quintessentially human, emotions have, until recently, been largely ignored in the human factors cognitive engineering / decision-making area. This is surprising, as extensive empirical evidence indicates that emotions, and personality traits, influence human perception and decision-making. This is particularly the case in crisis situations, when extreme affective states may arise (e.g., anxiety). The development of more complete and realistic theories of human perception and decision-making, and associated computational models, will require the inclusion of personality and affective

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Zhao, Yanhaotian. "State of the Art in the Application of Multimodal Affective Methods for Comparative Analysis of Modal Deficits." Applied and Computational Engineering 174, no. 1 (2025): 1–9. https://doi.org/10.54254/2755-2721/2025.po24716.

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The advent of multimedia technology has precipitated a paradigm shift in the realm of human-computer interaction and affective computing, thus rendering multimodal emotion recognition a pivotal domain. However, the issue of modal absence, resulting from equipment failure or environmental interference in practical applications, significantly impacts the accuracy of emotion recognition. The objective of this paper is to analyse multimodal emotion recognition methods oriented to modal absence. The focus is on comparing and analysing the advantages and disadvantages of techniques such as generativ

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Oikonomou, Vangelis P., Kostas Georgiadis, Fotis Kalaganis, Spiros Nikolopoulos, and Ioannis Kompatsiaris. "A Sparse Representation Classification Scheme for the Recognition of Affective and Cognitive Brain Processes in Neuromarketing." Sensors 23, no. 5 (2023): 2480. http://dx.doi.org/10.3390/s23052480.

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In this work, we propose a novel framework to recognize the cognitive and affective processes of the brain during neuromarketing-based stimuli using EEG signals. The most crucial component of our approach is the proposed classification algorithm that is based on a sparse representation classification scheme. The basic assumption of our approach is that EEG features from a cognitive or affective process lie on a linear subspace. Hence, a test brain signal can be represented as a linear (or weighted) combination of brain signals from all classes in the training set. The class membership of the b

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Barabanschikov, V. A., and E. V. Suvorova. "Human Emotional State Assessment Based on a Video Portrayal." Experimental Psychology (Russia) 13, no. 4 (2020): 4–24. http://dx.doi.org/10.17759/exppsy.2020130401.

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The article is devoted to the results of approbation of the Geneva Emotion Recognition Test (GERT), a Swiss method for assessing dynamic emotional states, on Russian sample. Identification accuracy and the categorical fields’ structure of emotional expressions of a “living” face are analysed. Similarities and differences in the perception of affective groups of dynamic emotions in the Russian and Swiss samples are considered. A number of patterns of recognition of multi-modal expressions with changes in valence and arousal of emotions are described. Differences in the perception of dynamics an

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Machado, José Alberto Oliveira de Paula, and Sofia Lorena Maia da Silva. "A PATERNIDADE SOCIOAFETIVA E SEUS REFLEXOS NO DIREITO SUCESSÓRIO BRASILEIRO: UMA ANÁLISE JURISPRUDENCIAL." Revista ft 29, no. 145 (2025): 53–54. https://doi.org/10.69849/revistaft/ni10202504261653.

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This study analyzes the impacts of socio-affective paternity on Brazilian inheritance law, considering the evolution of the concept of filiation and its appreciation in the legal system. With the overcoming of the exclusively biological model, affection has become an essential criterion in the definition of parenthood. The research addresses the consolidation of this recognition in case law and the challenges faced in hereditary succession, especially in the equalization of rights between biological and socio-affective children. Through a case law analysis, decisions of higher and state courts

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Ramya, Mrs T., Mr Y. Anurag, Ms K. S. S. Pranathi, Mr M. Samba Raju, and Mr P. Rohith. "FACIAL EMOTION RECOGNITION." YMER Digital 21, no. 04 (2022): 543–57. http://dx.doi.org/10.37896/ymer21.04/55.

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Facial Emotion is the noticeable indication of the full of affective state, cognitive activity, intension, personality, and psychopathology of an individual and plays a communicative role in interpersonal relations. Automatic recognition of facial emotion can be a significant part of normal human-machine interfaces; it might likewise be utilized in conduct science and in clinical practice. An automatic Facial Emotion Recognition framework requirement to perform identification and area of countenances in a jumbled scene, facial component extraction, and look characterization. The facial emotion

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Rossi, Daniela. "Lexical reduplication and affective contents." Cognitive and Empirical Pragmatics 25 (December 5, 2011): 148–75. http://dx.doi.org/10.1075/bjl.25.07ros.

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The purpose of this paper is to investigate how the use of a linguistic form (lexical reduplication) can communicate affective contents. Lexical reduplication, understood as the intentional repetition of a word, is defined as a pattern XX used to convey, on the one hand, a content which differs from the “basic” meaning of X by involving, for instance, intensification, narrowing, or expansion, and, on the other hand, an affective content that results from the evaluation of the state of affairs at hand. To test reduplication as well as the derivation of affective contents linked to its use, I ha

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Yang, Zhuqing, Liya Zhou, and Zhengjun Jing. "A Novel Affective Analysis System Modeling Method Integrating Affective Cognitive Model and Bi-LSTM Neural Network." Computational Intelligence and Neuroscience 2022 (October 7, 2022): 1–11. http://dx.doi.org/10.1155/2022/1856496.

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The severity of mental health issues among college students has increased over the past few years, having a significant negative impact on not only their academic performance but also on their families and even society as a whole. Therefore, one of the pressing issues facing college administrators right now is finding a method that is both scientific and useful for determining the mental health of college students. In pace with the advancement of Internet technology, the Internet has become an important communication channel for contemporary college students. As one of the main forces in the h

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R., Sujith Kumar, Soundar Sriram J., Sridhar C., and Fathima G. "Facial Emotion Recognition Based Prediction of Affective State Of Children with Autism Using Ml." International Journal of Computer Sciences and Engineering 11, no. 4 (2023): 19–25. https://doi.org/10.26438/ijcse/v11i4.1925.

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Pasupuleti, Murali Krishna. "Facial Emotion Recognition with Cross-Dataset Generalization Capabilities." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 05 (2025): 528–38. https://doi.org/10.62311/nesx/rphcr14.

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Abstract: Facial emotion recognition (FER) plays a vital role in human-computer interaction, mental health monitoring, and affective computing. Despite recent advancements, achieving generalization across datasets remains a significant challenge due to variations in pose, illumination, ethnicity, and annotation inconsistencies. This study investigates the cross-dataset generalization capabilities of deep learning-based FER models trained on AffectNet, RAF-DB, and FER2013, and evaluated on previously unseen datasets. The performance of convolutional neural networks (CNNs), vision transformers (

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Ali, Kamran, Sachin Shah, and Charles E. Hughes. "In-the-Wild Affect Analysis of Children with ASD Using Heart Rate." Sensors 23, no. 14 (2023): 6572. http://dx.doi.org/10.3390/s23146572.

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Recognizing the affective state of children with autism spectrum disorder (ASD) in real-world settings poses challenges due to the varying head poses, illumination levels, occlusion and a lack of datasets annotated with emotions in in-the-wild scenarios. Understanding the emotional state of children with ASD is crucial for providing personalized interventions and support. Existing methods often rely on controlled lab environments, limiting their applicability to real-world scenarios. Hence, a framework that enables the recognition of affective states in children with ASD in uncontrolled settin

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Hazer-Rau, Dilana, Lin Zhang, and Harald C. Traue. "A Workflow for Affective Computing and Stress Recognition from Biosignals." Engineering Proceedings 2, no. 1 (2020): 85. http://dx.doi.org/10.3390/ecsa-7-08227.

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Affective computing and stress recognition from biosignals have a high potential in various medical applications such as early intervention, stress management and risk prevention, as well as monitoring individuals’ mental health. This paper presents an automated processing workflow for the psychophysiological recognition of emotion and stress states. Our proposed workflow allows the processing of biosignals in their raw state as obtained from wearable sensors. It consists of five stages: (1) Biosignal Preprocessing—raw data conversion and physiological data triggering, relevant information sel

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Lee, Wonhyong, and Dong Hwan Jin. "Intuitive Recognition of a Virtual Agent’s Learning State Through Facial Expressions in VR." Electronics 14, no. 13 (2025): 2666. https://doi.org/10.3390/electronics14132666.

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As artificial intelligence agents become integral to immersive virtual reality environments, their inherent opacity presents a significant challenge to transparent human–agent communication. This study aims to determine if a virtual agent can effectively communicate its learning state to a user through facial expressions, and to empirically validate a set of designed expressions for this purpose. We designed three animated facial expression sequences for a stylized three-dimensional avatar, each corresponding to a distinct learning outcome: clear success (Case A), mixed performance (Case B), a

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Pólya, Tibor, and István Csertő. "Emotion Recognition Based on the Structure of Narratives." Electronics 12, no. 4 (2023): 919. http://dx.doi.org/10.3390/electronics12040919.

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One important application of natural language processing (NLP) is the recognition of emotions in text. Most current emotion analyzers use a set of linguistic features such as emotion lexicons, n-grams, word embeddings, and emoticons. This study proposes a new strategy to perform emotion recognition, which is based on the homologous structure of emotions and narratives. It is argued that emotions and narratives share both a goal-based structure and an evaluation structure. The new strategy was tested in an empirical study with 117 participants who recounted two narratives about their past emoti

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Santana, Maíra Araújo de, Clarisse Lins de Lima, Arianne Sarmento Torcate, Flávio Secco Fonseca, and Wellington Pinheiro dos Santos. "Affective computing in the context of music therapy: a systematic review." Research, Society and Development 10, no. 15 (2021): e392101522844. http://dx.doi.org/10.33448/rsd-v10i15.22844.

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Music therapy is an effective tool to slow down the progress of dementia since interaction with music may evoke emotions that stimulates brain areas responsible for memory. This therapy is most successful when therapists provide adequate and personalized stimuli for each patient. This personalization is often hard. Thus, Artificial Intelligence (AI) methods may help in this task. This paper brings a systematic review of the literature in the field of affective computing in the context of music therapy. We particularly aim to assess AI methods to perform automatic emotion recognition applied to

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Chowanda, Andry. "Emowars: Interactive Game Input Menggunakan Ekspresi Wajah." ComTech: Computer, Mathematics and Engineering Applications 4, no. 2 (2013): 1009. http://dx.doi.org/10.21512/comtech.v4i2.2542.

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Research in the affective game has received attention from the research communities over this lustrum. As a crucial aspect of a game, emotions play an important role in user experience as well as to emphasize the user’s emotions state on game design. This will improve the user’s interactivity while they playing the game. This research aims to discuss and analyze whether emotions can replace traditional user game inputs (keyboard, mouse, and others). The methodology used in this research is divided into two main phases: game design and facial expression recognition. The results of this research

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Ryumina, Elena, Maxim Markitantov, and Alexey Karpov. "Multi-Corpus Learning for Audio–Visual Emotions and Sentiment Recognition." Mathematics 11, no. 16 (2023): 3519. http://dx.doi.org/10.3390/math11163519.

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Recognition of emotions and sentiment (affective states) from human audio–visual information is widely used in healthcare, education, entertainment, and other fields; therefore, it has become a highly active research area. The large variety of corpora with heterogeneous data available for the development of single-corpus approaches for recognition of affective states may lead to approaches trained on one corpus being less effective on another. In this article, we propose a multi-corpus learned audio–visual approach for emotion and sentiment recognition. It is based on the extraction of mid-lev

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Harmer, C. J., M. Charles, S. McTavish, E. Favaron, and P. J. Cowen. "Negative ion treatment increases positive emotional processing in seasonal affective disorder." Psychological Medicine 42, no. 8 (2011): 1605–12. http://dx.doi.org/10.1017/s0033291711002820.

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BackgroundAntidepressant drug treatments increase the processing of positive compared to negative affective information early in treatment. Such effects have been hypothesized to play a key role in the development of later therapeutic responses to treatment. However, it is unknown whether these effects are a common mechanism of action for different treatment modalities. High-density negative ion (HDNI) treatment is an environmental manipulation that has efficacy in randomized clinical trials in seasonal affective disorder (SAD).MethodThe current study investigated whether a single session of H

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Guo, Yuanyuan, Yifan Xia, Jing Wang, Hui Yu, and Rung-Ching Chen. "Real-Time Facial Affective Computing on Mobile Devices." Sensors 20, no. 3 (2020): 870. http://dx.doi.org/10.3390/s20030870.

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Convolutional Neural Networks (CNNs) have become one of the state-of-the-art methods for various computer vision and pattern recognition tasks including facial affective computing. Although impressive results have been obtained in facial affective computing using CNNs, the computational complexity of CNNs has also increased significantly. This means high performance hardware is typically indispensable. Most existing CNNs are thus not generalizable enough for mobile devices, where the storage, memory and computational power are limited. In this paper, we focus on the design and implementation o

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Stassen, H. H. "Affective State and Voice: The Specific Properties of Overtone Distributions." Methods of Information in Medicine 30, no. 01 (1991): 44–52. http://dx.doi.org/10.1055/s-0038-1634812.

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Motivated by psychiatric interests and as part of our investigations into the basic properties of human speech, we carried out a normative study with 192 healthy subjects - stratified according to sex, age and education - in order to derive reference values of the general population and to learn to distinguish between normal fluctuations and significant changes over time. In the present investigation, our interest focused on the individual sound characteristics of speakers (“timbre”) rather than on speech behavior. Accordingly, we determined the optimum parameter setting for a problem-specific

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Abdullahi, Bashir Eseyin, Emeka Ogbuju, Taiwo Abiodun, and Francisca Oladipo. "Techniques for facial affective computing: A review." Ukrainian Journal of Educational Studies and Information Technology 11, no. 3 (2023): 211–26. http://dx.doi.org/10.32919/uesit.2023.03.05.

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Facial affective computing has gained popularity and become a progressive research area, as it plays a key role in human-computer interaction. However, many researchers lack the right technique to carry out a reliable facial affective computing effectively. To address this issue, we presented a review of the state-of-the-art artificial intelligence techniques that are being used for facial affective computing. Three research questions were answered by studying and analysing related papers collected from some well-established scientific databases based on some exclusion and inclusion criteria.

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SCHIPOR, O. A., S. G. PENTIUC, and M. D. SCHIPOR. "Toward Automatic Recognition of Children's Affective State Using Physiological Parameters and Fuzzy Model of Emotions." Advances in Electrical and Computer Engineering 12, no. 2 (2012): 47–50. http://dx.doi.org/10.4316/aece.2012.02008.

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Neethirajan, Suresh. "Correction: Neethirajan, S. Affective State Recognition in Livestock—Artificial Intelligence Approaches. Animals 2022, 12, 759." Animals 12, no. 14 (2022): 1856. http://dx.doi.org/10.3390/ani12141856.

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Hadjidimitriou, Stelios, Vasileios Charisis, and Leontios Hadjileontiadis. "Towards a Practical Subject-Independent Affective State Recognition Based on Time-Domain EEG Feature Extraction." International Journal of Heritage in the Digital Era 4, no. 2 (2015): 165–77. http://dx.doi.org/10.1260/2047-4970.4.2.165.

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Udahemuka, Gustave, Karim Djouani, and Anish M. Kurien. "Multimodal Emotion Recognition Using Visual, Vocal and Physiological Signals: A Review." Applied Sciences 14, no. 17 (2024): 8071. http://dx.doi.org/10.3390/app14178071.

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The dynamic expressions of emotion convey both the emotional and functional states of an individual’s interactions. Recognizing the emotional states helps us understand human feelings and thoughts. Systems and frameworks designed to recognize human emotional states automatically can use various affective signals as inputs, such as visual, vocal and physiological signals. However, emotion recognition via a single modality can be affected by various sources of noise that are specific to that modality and the fact that different emotion states may be indistinguishable. This review examines the cu

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Reddy, Dr K. Shirisha. "Emotion Recognition through Text, Speech and Image." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (2023): 1493–506. http://dx.doi.org/10.22214/ijraset.2023.57652.

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Abstract: Emotion recognition plays a vital role in various applications, such as human-computer interaction, affective computing, and psychological assessment. This comparative study investigates the effectiveness of emotion recognition through text, image, and speech modalities. Our research aims to analyze and compare state-of-art approaches in each modality and identify their strengths and limitations. Conducting an exhaustive literature review enabled us to understand existing methodologies, datasets, and evaluation metrics. The research methodology and implementation include data collect

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Ríos, Ulises, Marcelo Arancibia, Juan Pablo Jiménez, and Felix Bermpohl. "The forgotten affective route of social cognition in patients with bipolar disorders." Journal of Experimental Psychopathology 13, no. 4 (2022): 204380872211354. http://dx.doi.org/10.1177/20438087221135422.

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Social cognition (SC) research in bipolar disorders (BD) has provided evidence about deficits in different phases of the illness. Most of the studies have focused on two aspects of SC: theory of mind and emotion recognition. However, according to influential models of social neuroscience, two aspects of understanding others need to be distinguished: the cognitive (theory of mind and emotion recognition) and the affective route (empathy and compassion) of SC. We aimed to determine whether individuals with BD significantly differ from healthy controls on measures of the affective route of SC acc

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