Comparative analysis of emotion estimation methods based on physiological measurements for real-time applications
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Recent research has shown that under certain circumstances multimodal emotion recognition is possible even in real time.
“Recent research has shown that under certain circumstances multimodal emotion recognition is possible even in real time [37].”
From Survey on AI-Based Multimodal Methods for Emotion Detection (2019) · cites this paper as [37]
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918 · see citing works
Data Mining: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,493 · see citing works
Heart Rate Variability: Standards of Measurement, Physiological Interpretation, and Clinical Use
Electrophysiology, T. F. O. T. E. S. O. C. T. N. A. (1996) · Circulation · doi:10.1161/01.cir.93.5.1043 · Cited by 17,159 · see citing works
THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS
Fisher, R. (1936) · Annals of Eugenics · doi:10.1111/j.1469-1809.1936.tb02137.x · Cited by 16,063 · see citing works
Heart rate variability: Standards of measurement, physiological interpretation, and clinical use
Malik, M., Bigger, J. T., Camm, A. J., Kleiger, R. E., Malliani, A., Moss, A. J., & Schwartz, P. J. (1996) · European Heart Journal · doi:10.1093/oxfordjournals.eurheartj.a014868 · Cited by 15,289 · see citing works
Peng, H., Long, F., & Ding, C. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.159 · Cited by 10,614 · see citing works
Witten, I. H. (2008) · Cited by 9,115 · see citing works
Wrappers for feature subset selection
Kohavi, R., & John, G. H. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00043-x · Cited by 8,940 · see citing works
Physiological time-series analysis using approximate entropy and sample entropy
Richman, J. S., & Moorman, J. R. (2000) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.2000.278.6.h2039 · Cited by 7,880 · see citing works
A Real-Time QRS Detection Algorithm
Pan, J., & Tompkins, W. J. (1985) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.1985.325532 · Cited by 7,855 · see citing works
Picard, R. W. (1997) · MIT Press · doi:10.7551/mitpress/1140.001.0001 · Cited by 7,382 · see citing works
(2005) · CTIT technical reports series · Cited by 5,307 · see citing works
Patterns of cognitive appraisal in emotion.
Smith, C. A., & Ellsworth, P. C. (1985) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.48.4.813 · Cited by 4,068 · see citing works
Anxiety, sensitivity, anxiety frequency, and the prediction of fearfulness
Reiss, S., Peterson, R. A., Gursky, D. M., & McNally, R. J. (1986) · Behaviour Research and Therapy · doi:10.1016/0005-7967(86)90143-9 · Cited by 3,169 · see citing works
Floating search methods in feature selection
Pudil, P., Novovičová, J., & Kittler, J. (1994) · Pattern Recognition Letters · doi:10.1016/0167-8655(94)90127-9 · Cited by 3,129 · see citing works
Theoretical and Empirical Analysis of ReliefF and RReliefF
Robnik-Šikonja, M., & Kononenko, I. (2003) · Machine Learning · doi:10.1023/a:1025667309714 · Cited by 3,025 · see citing works
Toward machine emotional intelligence: analysis of affective physiological state
Picard, R. W., Vyzas, E., & Healey, J. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.954607 · Cited by 2,334 · see citing works
Detecting Stress During Real-World Driving Tasks Using Physiological Sensors
Healey, J. A., & Picard, R. W. (2005) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2005.848368 · Cited by 2,207 · see citing works
Introduction to Spectral Analysis
(2005) · Technometrics · doi:10.1198/tech.2005.s841 · Cited by 1,933 · see citing works
Heart rate variability as an index of regulated emotional responding
Appelhans, B. M., & Luecken, L. J. (2006) · Review of General Psychology · doi:10.1037/1089-2680.10.3.229 · Cited by 1,893 · see citing works
The Handbook of emotion elicitation and assessment
(2008) · Choice Reviews Online · doi:10.5860/choice.46-1769 · Cited by 1,127 · see citing works
Emotion recognition based on physiological changes in music listening
Kim, J., & André, E. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.26 · Cited by 1,079 · see citing works
Emotion recognition system using short-term monitoring of physiological signals
Kim, K. H., Bang, S. W., & Kim, S. R. (2004) · Medical & Biological Engineering & Computing · doi:10.1007/bf02344719 · Cited by 944 · see citing works
Emotion Elicitation Using Films
Rottenberg, J. A., Ray, R. D., & Gross, J. J. (2007) · Oxford University Press eBooks · doi:10.1093/oso/9780195169157.003.0002 · Cited by 862 · see citing works
Emotional category data on images from the international affective picture system
Mikels, J. A., Fredrickson, B. L., Samanez-Larkin, G. R., Lindberg, C. M., Maglio, S. J., & Reuter‐Lorenz, P. A. (2005) · Behavior Research Methods · doi:10.3758/bf03192732 · Cited by 675 · see citing works
Relationship between color and emotion: A study of college students.
Kaya, N., & Epps, H. H. (2004) · College student journal · Cited by 588 · see citing works
Mandryk, R. L., & Atkins, M. S. (2007) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2006.11.011 · Cited by 538 · see citing works
Kring, A. M., Smith, D. A., & Neale, J. M. (1994) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.66.5.934 · Cited by 525 · see citing works
Kring, A. M., Smith, D. A., & Neale, J. M. (1994) · Journal of Personality and Social Psychology · doi:10.1037//0022-3514.66.5.934 · Cited by 524 · see citing works
Basic emotions are associated with distinct patterns of cardiorespiratory activity
Rainville, P., Bechara, A., Naqvi, N. H., & Damásio, A. R. (2006) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2005.10.024 · Cited by 499 · see citing works
Cardiovascular, electrodermal, and respiratory response patterns to fear‐ and sadness‐inducing films
Kreibig, S. D., Wilhelm, F. H., Roth, W. T., & Gross, J. J. (2007) · Psychophysiology · doi:10.1111/j.1469-8986.2007.00550.x · Cited by 495 · see citing works
Verification and Extension of the Principle of Autonomic Response-Stereotypy
Lacey, J. I., & Lacey, B. C. (1958) · The American Journal of Psychology · doi:10.2307/1419197 · Cited by 486 · see citing works
Using Noninvasive Wearable Computers to Recognize Human Emotions from Physiological Signals
Lisetti, C., & Nasoz, F. (2004) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/s1110865704406192 · Cited by 432 · see citing works
Introduction to Spectral Analysis
Percival, D. B., & Walden, A. T. (2020) · Cambridge University Press eBooks · doi:10.1017/9781139235723.002 · Cited by 413 · see citing works
Toward Emotion Recognition in Car-Racing Drivers: A Biosignal Processing Approach
Katsis, C. D., Katertsidis, N. S., Ganiatsas, G., & Fotiadis, D. I. (2008) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/tsmca.2008.918624 · Cited by 332 · see citing works
Feature selection via discretization
Liu, H., & Setiono, R. (1997) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.617056 · Cited by 328 · see citing works
An empirical study of machine learning techniques for affect recognition in human–robot interaction
Rani, P., Liu, C., Sarkar, N., & Vanman, E. (2006) · Pattern Analysis and Applications · doi:10.1007/s10044-006-0025-y · Cited by 313 · see citing works
Multidimensional normative ratings for the International Affective Picture System
Libkuman, T. M., Otani, H., Kern, R. P., Viger, S. G., & Novak, N. (2007) · Behavior Research Methods · doi:10.3758/bf03193164 · Cited by 190 · see citing works
Fletcher, R., Dobson, K., Goodwin, M. S., Eydgahi, H., Wilder-Smith, O., Fernholz, D., Kuboyama, Y., Hedman, E. B., Poh, M., & Picard, R. W. (2010) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2009.2038692 · Cited by 155 · see citing works
Emotion Research by the People, for the People
Picard, R. W. (2010) · Emotion Review · doi:10.1177/1754073910364256 · Cited by 134 · see citing works
A user-independent real-time emotion recognition system for software agents in domestic environments
Leon, E., Clarke, G., Callaghan, V., & Sepulveda, F. (2006) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2006.06.001 · Cited by 124 · see citing works
Kolodyazhniy, V., Kreibig, S. D., Gross, J. J., Roth, W. T., & Wilhelm, F. H. (2011) · Psychophysiology · doi:10.1111/j.1469-8986.2010.01170.x · Cited by 122 · see citing works
Katsis, C. D., Katertsidis, N. S., & Fotiadis, D. I. (2010) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2010.12.001 · Cited by 110 · see citing works
Affective Man-Machine Interface: Unveiling Human Emotions through Biosignals
van den Broek, E. L., Lisý, V., Janssen, J. H., Westerink, J. H. D. M., Schut, M. H., & Tuinenbreijer, K. (2010) · Communications in computer and information science · doi:10.1007/978-3-642-11721-3_2 · Cited by 92 · see citing works
Heart Rate Variability: Measures and ModelsPreprint
Teich, M. C., Lowen, S. B., Jost, B. M., Vibe‐Rheymer, K., & Heneghan, C. (2012) · arXiv (Cornell University) · doi:10.1002/9780470545379.ch6 · Cited by 71 · see citing works
Barke, A., Stähl, J., & Kröner‐Herwig, B. (2011) · Journal of Behavior Therapy and Experimental Psychiatry · doi:10.1016/j.jbtep.2011.07.006 · Cited by 63 · see citing works
Ćosić, K., Popović, S., Kukolja, D., Horvat, M., & Dropuljić, B. (2010) · Cyberpsychology Behavior and Social Networking · doi:10.1089/cyber.2009.0260 · Cited by 62 · see citing works
MAUI avatars: Mirroring the user's sensed emotions via expressive multi-ethnic facial avatars
Nasoz, F., & Lisetti, C. (2006) · Journal of Visual Languages & Computing · doi:10.1016/j.jvlc.2006.05.001 · Cited by 30 · see citing works
Jurin, T., Jokić-begić, N., & Korajlija, A. L. (2012) · Assessment (Odessa, Fla.) · doi:10.1177/1073191111402459 · Cited by 27 · see citing works
Harley, J. M., Bouchet, F., & Azevedo, R. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-30950-2_5 · Cited by 26 · see citing works
Heart Rate Variability: Measures and Models
(2009) · Institute of Electrical and Electronics Engineers eBooks · doi:10.1109/9780470545379.ch6 · Cited by 8 · see citing works
Physiology-based affect recognition and adaptation in human-machine interaction
Sarkar, N., & Liu, C. (2009) · Cited by 1 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Wearable-Based Affect Recognition—A Review
Schmidt, P., Reiss, A., Dürichen, R., & Laerhoven, K. V. (2019) · Sensors · doi:10.3390/s19194079 · Cited by 267 · see citing works
Electrocardiogram-Based Emotion Recognition Systems and Their Applications in Healthcare—A Review
Hasnul, M. A., Aziz, N. A. A., Alelyani, S., Mohana, M., & Aziz, A. A. (2021) · Sensors · doi:10.3390/s21155015 · Cited by 213 · see citing works
Alslaity, A., & Orji, R. (2022) · Behaviour and Information Technology · doi:10.1080/0144929x.2022.2156387 · Cited by 177 · see citing works
Arza, A., Garzón-Rey, J. M., Lázaro, J., Gil, E., Lopez-Anton, R., de la Camara, C., Laguna, P., Bailon, R., & Aguiló, J. (2019) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-018-1879-z · Cited by 148 · see citing works
Survey on AI-Based Multimodal Methods for Emotion Detection
Marechal, C., Mikołajewski, D., Tyburek, K., Prokopowicz, P., Bougueroua, L., Ancourt, C., & Węgrzyn-Wolska, K. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-16272-6_11 · Cited by 137 · see citing works
Physiological and Behavior Monitoring Systems for Smart Healthcare Environments: A Review
Rodrigues, M. J., Postolache, O. A., & Cercas, F. (2020) · Sensors · doi:10.3390/s20082186 · Cited by 122 · see citing works
Tan, C. N. W., Šarlija, M., & Kasabov, N. K. (2021) · Neurocomputing · doi:10.1016/j.neucom.2020.12.098 · Cited by 111 · see citing works
Acute Stress State Classification Based on Electrodermal Activity Modeling
Greco, A., Valenza, G., Lázaro, J. M., Rey, J. M. G., Aguiló, J., de la Cámara, C., Bailón, R., & Scilingo, E. P. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3055294 · Cited by 96 · see citing works
Stress Detection Using Physiological Sensors
Sioni, R., & Chittaro, L. (2015) · Computer · doi:10.1109/mc.2015.316 · Cited by 83 · see citing works
Human stress classification during public speaking using physiological signals
Arsalan, A., & Majid, M. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104377 · Cited by 77 · see citing works
CorrNet: Fine-Grained Emotion Recognition for Video Watching Using Wearable Physiological Sensors
Zhang, T., Ali, A. E., Wang, C., Hanjalić, A., & Cesar, P. S. (2020) · Sensors · doi:10.3390/s21010052 · Cited by 76 · see citing works
Techniques and applications of emotion recognition in speech
Lugović, S., Đunđer, I., & Horvat, M. (2016) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.1109/mipro.2016.7522336 · Cited by 67 · see citing works
Self-citationA Review on the Role of Color and Light in Affective Computing
Sokolova, M. V., & Fernández‐Caballero, A. (2015) · Applied Sciences · doi:10.3390/app5030275 · Cited by 67 · see citing works
Affective Recognition in Dynamic and Interactive Virtual Environments
Moghimi, M., Stone, R. J., & Rotshtein, P. (2017) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2764896 · Cited by 49 · see citing works
Horvat, M., Dobrinic, M., Novosel, M., & Jercic, P. (2018) · 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) · doi:10.23919/mipro.2018.8400184 · Cited by 49 · see citing works
Self-citationLi, F., Yang, L., Shi, H., & Liu, C. (2017) · Artery Research · doi:10.1016/j.artres.2017.02.003 · Cited by 38 · see citing works
Sánchez-Reolid, R., de la Rosa, F. L., Sánchez-Reolid, D., López, M. T., & Fernández‐Caballero, A. (2022) · Sensors · doi:10.3390/s22228886 · Cited by 39 · see citing works
Psychophysiology of positive and negative emotions, dataset of 1157 cases and 8 biosignals
Behnke, M., Buchwald, M., Bykowski, A., Kupiński, S., & Kaczmarek, L. D. (2022) · Scientific Data · doi:10.1038/s41597-021-01117-0 · Cited by 37 · see citing works
High-Performance Modelling and Simulation for Big Data Applications
Kołodziej, J., & González–Vélez, H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-16272-6 · Cited by 37 · see citing works
Assessing users’ emotion at interaction time: a multimodal approach with multiple sensors
Gonçalves, V. P., Giancristofaro, G. T., Filho, G. P. R., Johnson, T. M., Carvalho, V., Pessin, G., Néris, V. P. D. A., & Ueyama, J. (2016) · Soft Computing · doi:10.1007/s00500-016-2115-0 · Cited by 34 · see citing works
Affective state estimation based on Russell’s model and physiological measurements
Cittadini, R., Tamantini, C., di Luzio, F. S., Lauretti, C., Zollo, L., & Cordella, F. (2023) · Scientific Reports · doi:10.1038/s41598-023-36915-6 · Cited by 34 · see citing works
Weakly-Supervised Learning for Fine-Grained Emotion Recognition Using Physiological Signals
Zhang, T., Ali, A. E., Wang, C., Hanjalić, A., & Cesar, P. S. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3158234 · Cited by 34 · see citing works
Stress Resilience Assessment Based on Physiological Features in Selection of Air Traffic Controllers
Ćosić, K., Šarlija, M., Ivković, V., Zhang, Q., Strangman, G. E., & Popović, S. (2019) · IEEE Access · doi:10.1109/access.2019.2907479 · Cited by 34 · see citing works
Self-citationWearable affect and stress recognition: A reviewPreprint
Schmidt, P., Reiss, A., Duerichen, R., & Laerhoven, K. V. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.08854 · Cited by 31 · see citing works
Cosic, K., Popovic, S., Sarlija, M., Mijic, I., Kokot, M., Kesedzic, I., Strangman, G., Ivkovic, V., & Zhang, Q. (2019) · IEEE Access · doi:10.1109/access.2019.2957357 · Cited by 29 · see citing works
Self-citationThe facial expression of schizophrenic patients applied with infrared thermal facial image sequence
Jian, B., Chen, C., Chu, W.-L., & Huang, M. (2017) · BMC Psychiatry · doi:10.1186/s12888-017-1387-y · Cited by 27 · see citing works
Game-Calibrated and User-Tailored Remote Detection of Stress and Boredom in Games
Bevilacqua, F., Engström, H., & Backlund, P. (2019) · Sensors · doi:10.3390/s19132877 · Cited by 28 · see citing works
Ćosić, K., Popović, S., & Wiederhold, B. K. (2024) · Cyberpsychology Behavior and Social Networking · doi:10.1089/cyber.2023.0737 · Cited by 30 · see citing works
Self-citationEmotion-Specific Facial Activation Maps Based on Infrared Thermal Image Sequences
Jian, B., Chen, C.-L., Huang, M.-W., & Yau, H. (2019) · IEEE Access · doi:10.1109/access.2019.2908819 · Cited by 26 · see citing works
Machine Learning for Stress Detection from Electrodermal Activity: A Scoping ReviewPreprint
Sánchez-Reolid, R., López, M. T., & Fernández‐Caballero, A. (2020) · Preprints.org · doi:10.20944/preprints202011.0043.v1 · Cited by 26 · see citing works
Unimodal approaches for emotion recognition: A systematic review
Tomar, P. S., Mathur, K., & Suman, U. (2022) · Cognitive Systems Research · doi:10.1016/j.cogsys.2022.10.012 · Cited by 25 · see citing works
Enhancing intelligence in multimodal emotion assessments
Gonçalves, V. P., Costa, E. P., Valejo, A., Filho, G. P. R., Johnson, T. M., Pessin, G., & Ueyama, J. (2016) · Applied Intelligence · doi:10.1007/s10489-016-0842-7 · Cited by 22 · see citing works
Multimodal analysis of startle type responses
Ćosić, K., Popović, S., Kukolja, D., Dropuljić, B., Ivanec, D., & Tonković, M. (2016) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2016.01.002 · Cited by 21 · see citing works
Self-citationZanetti, M., Faes, L., Cecco, M. D., Fornaser, A., Valente, M., Guandalini, G. M. A., & Nollo, G. (2019) · Lecture notes in electrical engineering · doi:10.1007/978-3-030-05921-7_20 · Cited by 21 · see citing works
Changes in heart rate and facial actions during a gaming session with provoked boredom and stress
Bevilacqua, F., Engström, H., & Backlund, P. (2017) · Entertainment Computing · doi:10.1016/j.entcom.2017.10.004 · Cited by 20 · see citing works
A Framework to Model and Control the State of Presence in Virtual Reality Systems
Hernández-Melgarejo, G., Luviano-Juárez, A., & Fuentes-Aguilar, R. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3195697 · Cited by 11 · see citing works
Evaluation of consistency of HRV indices change among different emotions
Zhao, L., Yang, L., Shi, H., Xia, Y., Li, F., & Liu, C. (2017) · Chinese Automation Congress · doi:10.1109/cac.2017.8243625 · Cited by 13 · see citing works
The Influence of Stress Noise and Music Stimulation on the Autonomous Nervous System
Rodrigues, M. J., Postolache, O. A., & Cercas, F. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3279881 · Cited by 13 · see citing works
Chen, Z., & Takatori, C. (2025) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2025.105392 · Cited by 12 · see citing works
Al-Galal, S. A. Y., Alshaikhli, I. F. T., & Wahab, A. (2016) · International Conference on Information and Communication Technology for the Muslim World (ICT4M) · doi:10.1109/ict4m.2016.062 · Cited by 11 · see citing works
Measuring States and Traits in Motivation and Emotion
Roe, R. A., & Inceoglu, I. (2016) · Oxford University Press eBooks · doi:10.1093/med:psych/9780199356942.003.0006 · Cited by 10 · see citing works
A Distributed Architecture for Multimodal Emotion Identification
Sokolova, M. V., Fernández‐Caballero, A., López, M. T., Martínez‐Rodrigo, A., Zangróniz, R., & Pastor, J. M. (2015) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-19629-9_14 · Cited by 9 · see citing works
Wei, W., Fu, X., Zhu, Y., Lu, N., & Ma, S. (2023) · IEEE Access · doi:10.1109/access.2023.3300721 · Cited by 8 · see citing works
Obst, M. A., Al-Zubaidi, A., Heldmann, M., Nolde, J. M., Blümel, N., Kannenberg, S., & Münte, T. F. (2021) · Brain Imaging and Behavior · doi:10.1007/s11682-021-00572-y · Cited by 8 · see citing works
Variations of Facial Actions While Playing Games with Inducing Boredom and Stress
Bevilacqua, F., Backlund, P., & Engström, H. (2016) · International Conference on Games and Virtual Worlds for Serious Applications · doi:10.1109/vs-games.2016.7590374 · Cited by 8 · see citing works
A Hybrid Macro and Micro Method for Consumer Emotion and Behavior Research
Mo, X., Zhihao, X., & Ding-Bang, L. (2023) · IEEE Access · doi:10.1109/access.2023.3303311 · Cited by 7 · see citing works
Respiration signal based two layer stress recognition across non-verbal and verbal situations
Lee, M., Moon, J., Cheon, D., Lee, J., & Lee, K. (2020) · ACM Symposium on Applied Computing · doi:10.1145/3341105.3373853 · Cited by 7 · see citing works
StimSeqOnt: An ontology for formal description of multimedia stimuli sequences
Horvat, M. (2020) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.23919/mipro48935.2020.9245268 · Cited by 7 · see citing works
Self-citationA machine learning approach to identifying objective biomarkers of anxiety and stressPreprint
Ramakrishnan, A., Pardes, A., Lynch, W., Molaro, C., & Platt, M. L. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/745315 · Cited by 6 · see citing works
Pellegrin, G., Ricka, N., Fompeyrine, D. A., Rohaly, T., Enders, L. R., & Roy, H. (2025) · Scientific Reports · doi:10.1038/s41598-025-91916-x · Cited by 5 · see citing works
Kiruba, K., & Sharmila, D. (2016) · Biomedical Research-tokyo · Cited by 5 · see citing works
Ganie, I. A., Gupta, A., & Oberoi, A. (2023) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2023.49221 · Cited by 4 · see citing works
Emotions and Mood States: Modeling, Elicitation, and Recognition
Greco, A., Valenza, G., & Scilingo, E. P. (2016) · Springer eBooks · doi:10.1007/978-3-319-46705-4_4 · Cited by 4 · see citing works
Constructivist Approaches for Computational Emotions: A Systematic Survey
Viola, A., Pavlović, V., & Yoon, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-21671-8_3 · Cited by 3 · see citing works
Guo, Y., & Chen, H. (2023) · Energy Reports · doi:10.1016/j.egyr.2023.04.297 · Cited by 3 · see citing works
Aue, T., Hoeppli, M., Scharnowski, F., & Steyrl, D. (2021) · Journal of Affective Disorders · doi:10.1016/j.jad.2021.07.040 · Cited by 2 · see citing works
Horvat, M., Doljanin, D., & Jagušt, T. (2022) · AIP conference proceedings · doi:10.1063/5.0100044 · Cited by 2 · see citing works
Self-citationClassifying Cognitive Workload Using Machine Learning Techniques and Non-Intrusive Wearable Devices
Liu, Y., Grimaldi, N. S., Basnet, N., Wozniak, D., Chen, E., Zahabi, M., KABER, D. B., & Ruiz, J. (2024) · IEEE International Conference on Human-Machine Systems (ICHMS) · doi:10.1109/ichms59971.2024.10555690 · Cited by 2 · see citing works
Classificação de Gamers por Padrões em suas Expressões Faciais
Jofilsan, N. C., de Lima, S. M. L., Alencar, R. C., Silva, S. H. M. T., de Albuquerque, J. V. O., Filho, O. H. S., Maciel, A. M. A., & dos Santos, W. P. (2021) · Simpósio Brasileiro de Jogos e Entretenimento Digital (SBGames) · doi:10.5753/sbgames_estendido.2021.21516 · Cited by 1 · see citing works
Beyond tumor biology: nursing interventions for psychological and immune health in cancer patients
Gou, Y. R., Gu, H., & Wang, L. (2026) · Supportive Care in Cancer · doi:10.1007/s00520-026-10574-5 · Cited by 1 · see citing works
Recognizing the Micro-Emotions of 2D Game Users with Extreme Learning Machines
Jofilsan, N. C., de Lima, S. M. L., dos Santos, W. P., & Bezerra, B. L. D. (2026) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-032-23311-0_29 · Cited by 1 · see citing works
Bevilacqua, F., Engström, H., & Backlund, P. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-76270-8_35 · Cited by 1 · see citing works
Predicting Task Difficulty Through Psychophysiology
Lukan, J., & Geršak, G. (2021) · Springer eBooks · doi:10.1007/978-3-030-71535-9_3
BEET: Behavior Engagement Emotion Trigger – A AI-based model to maximize audience engagement
Badawy, W. M. (2021) · Menoufia Journal of Electronic Engineering Research · doi:10.21608/mjeer.2021.193089
Soft skills in emergency virtual reality training for first responders: a review (Preprint)Preprint
Bouchoucha, S., Tisserand, Y., Sander, D., & Michela, A. (2024) · doi:10.2196/preprints.68416
- Authors
- Kukolja, D., Popović, S., Horvat, M., Kovač, B., & Ćosić, K.
- Affiliations
- University of Zagreb (HR)
- Corresponding author
- Kukolja, Davor
- Venue
- International Journal of Human-Computer Studies
- Publisher
- Elsevier BV
- Published
- 2014-10
- Volume
- 72
- Issue
- 10-11
- Pages
- 717-727
- ISSN
- 1071-5819 (ISSN-L)
- OpenAlex
- W1963552713
- Cited by
- 66
- References
- 53
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 3 · 2023: 6 · 2022: 8 · 2021: 9 · 2020: 5 · 2019: 9 · 2018: 4 · 2017: 5 · 2016: 10 · 2015: 3
- FWCI
- 8.35
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Human-Computer Studies (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Keywords
- emotion recognition, physiological signals, feature reduction, multilayer perceptron, sequential floating forward selection, minimum redundancy maximum relevance, k-nearest neighbors, ECG, respiration, skin conductance, skin temperature, International Affective Picture System
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review