{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T12:10:57Z","timestamp":1770898257811,"version":"3.50.1"},"reference-count":79,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T00:00:00Z","timestamp":1645660800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Extensive possibilities of applications have rendered emotion recognition ineluctable and challenging in the fields of computer science as well as in human-machine interaction and affective computing. Fields that, in turn, are increasingly requiring real-time applications or interactions in everyday life scenarios. However, while extremely desirable, an accurate and automated emotion classification approach remains a challenging issue. To this end, this study presents an automated emotion recognition model based on easily accessible physiological signals and deep learning (DL) approaches. As a DL algorithm, a Feedforward Neural Network was employed in this study. The network outcome was further compared with canonical machine learning algorithms such as random forest (RF). The developed DL model relied on the combined use of wearables and contactless technologies, such as thermal infrared imaging. Such a model is able to classify the emotional state into four classes, derived from the linear combination of valence and arousal (referring to the circumplex model of affect\u2019s four-quadrant structure) with an overall accuracy of 70% outperforming the 66% accuracy reached by the RF model. Considering the ecological and agile nature of the technique used the proposed model could lead to innovative applications in the affective computing field.<\/jats:p>","DOI":"10.3390\/s22051789","type":"journal-article","created":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T21:11:07Z","timestamp":1645737067000},"page":"1789","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Automated Affective Computing Based on Bio-Signals Analysis and Deep Learning Approach"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2282-3537","authenticated-orcid":false,"given":"Chiara","family":"Filippini","sequence":"first","affiliation":[{"name":"Department of Neurosciences, Imaging and Clinical Sciences, University G. D\u2019Annunzio of Chieti-Pescara, 9, 66100 Chieti, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8132-8985","authenticated-orcid":false,"given":"Adolfo","family":"Di Crosta","sequence":"additional","affiliation":[{"name":"Department of Psychological, Health and Territorial Sciences, University G. D\u2019Annunzio of Chieti-Pescara, 9, 66100 Chieti, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2385-5840","authenticated-orcid":false,"given":"Rocco","family":"Palumbo","sequence":"additional","affiliation":[{"name":"Department of Psychological, Health and Territorial Sciences, University G. D\u2019Annunzio of Chieti-Pescara, 9, 66100 Chieti, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1903-0501","authenticated-orcid":false,"given":"David","family":"Perpetuini","sequence":"additional","affiliation":[{"name":"Department of Neurosciences, Imaging and Clinical Sciences, University G. D\u2019Annunzio of Chieti-Pescara, 9, 66100 Chieti, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1506-1995","authenticated-orcid":false,"given":"Daniela","family":"Cardone","sequence":"additional","affiliation":[{"name":"Department of Neurosciences, Imaging and Clinical Sciences, University G. D\u2019Annunzio of Chieti-Pescara, 9, 66100 Chieti, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4819-2120","authenticated-orcid":false,"given":"Irene","family":"Ceccato","sequence":"additional","affiliation":[{"name":"Department of Neurosciences, Imaging and Clinical Sciences, University G. D\u2019Annunzio of Chieti-Pescara, 9, 66100 Chieti, Italy"}]},{"given":"Alberto","family":"Di Domenico","sequence":"additional","affiliation":[{"name":"Department of Psychological, Health and Territorial Sciences, University G. 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The Media Equation: How People Treat Computers, Television, and New Media like Real People and Places, Cambridge University Press. The Media Equation: How People Treat Computers, Television, and New Media Like Real People and Places."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1109\/34.954607","article-title":"Toward Machine Emotional Intelligence: Analysis of Affective Physiological State","volume":"23","author":"Picard","year":"2001","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1517","DOI":"10.1007\/s10803-012-1695-5","article-title":"Recognition of Emotions in Autism: A Formal Meta-Analysis","volume":"43","author":"Uljarevic","year":"2013","journal-title":"J. Autism Dev. Disord"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.1080\/13607863.2020.1856781","article-title":"Age-Related Differences in the Perception of COVID-19 Emergency during the Italian Outbreak","volume":"25","author":"Ceccato","year":"2021","journal-title":"Aging Mental Health"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.schres.2012.03.028","article-title":"Is There an Affective Working Memory Deficit in Patients with Chronic Schizophrenia?","volume":"138","author":"Mammarella","year":"2012","journal-title":"Schizophr. Res."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Prodi, N., Visentin, C., Borella, E., Mammarella, I.C., and Di Domenico, A. (2019). Noise, Age, and Gender Effects on Speech Intelligibility and Sentence Comprehension for 11-to 13-Year-Old Children in Real Classrooms. Front. Psychol., 2166.","DOI":"10.3389\/fpsyg.2019.02166"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1080\/13607863.2013.790929","article-title":"Saying It with a Natural Child\u2019s Voice! When Affective Auditory Manipulations Increase Working Memory in Aging","volume":"17","author":"Mammarella","year":"2013","journal-title":"Aging Ment. Health"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e28","DOI":"10.4081\/jphr.2013.e28","article-title":"Technology and the Future of Healthcare","volume":"2","author":"Thimbleby","year":"2013","journal-title":"J. Public Health Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1007\/s10111-003-0143-x","article-title":"Emotion Recognition from Physiological Signals Using Wireless Sensors for Presence Technologies","volume":"6","author":"Nasoz","year":"2004","journal-title":"Cogn. Technol. Work."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Tivatansakul, S., Ohkura, M., Puangpontip, S., and Achalakul, T. (2014, January 25\u201326). Emotional Healthcare System: Emotion Detection by Facial Expressions Using Japanese Database. Proceedings of the 2014 6th Computer Science and Electronic Engineering Conference (CEEC), Colchester, UK.","DOI":"10.1109\/CEEC.2014.6958552"},{"key":"ref_13","unstructured":"Reisenzein, R. (1992). A Structuralist Reconstruction of Wundt\u2019s Three-Dimensional Theory of Emotion. The Structuralist Program in Psychology: Foundations and Applications, Hogrefe & Huber Publishers."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1177\/1754073911410747","article-title":"Four Models of Basic Emotions: A Review of Ekman and Cordaro, Izard, Levenson, and Panksepp and Watt","volume":"3","author":"Tracy","year":"2011","journal-title":"Emot. Rev."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1037\/0033-295X.99.3.561","article-title":"Basic Emotions, Relations among Emotions, and Emotion-Cognition Relations","volume":"99","author":"Izard","year":"1992","journal-title":"Psychol. Rev."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1037\/0033-295X.97.3.315","article-title":"What\u2019s Basic about Basic Emotions?","volume":"97","author":"Ortony","year":"1990","journal-title":"Psychol. Rev."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1017\/S0954579405050340","article-title":"The Circumplex Model of Affect: An Integrative Approach to Affective Neuroscience, Cognitive Development, and Psychopathology","volume":"17","author":"Posner","year":"2005","journal-title":"Dev. Psychopathol."},{"key":"ref_18","unstructured":"Saarni, C. (1999). The Development of Emotional Competence, Guilford Press."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1080\/026999398379574","article-title":"Discrete Emotions or Dimensions? The Role of Valence Focus and Arousal Focus","volume":"12","author":"Barrett","year":"1998","journal-title":"Cogn. Emot."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1080\/09658210903130764","article-title":"A Comparison of Dimensional Models of Emotion: Evidence from Emotions, Prototypical Events, Autobiographical Memories, and Words","volume":"17","author":"Rubin","year":"2009","journal-title":"Memory"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1037\/h0077714","article-title":"A Circumplex Model of Affect","volume":"39","author":"Russell","year":"1980","journal-title":"J. Personal. Soc. Psychol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2129","DOI":"10.1016\/j.neuropsychologia.2008.02.032","article-title":"An Affective Circumplex Model of Neural Systems Subserving Valence, Arousal, and Cognitive Overlay during the Appraisal of Emotional Faces","volume":"46","author":"Gerber","year":"2008","journal-title":"Neuropsychologia"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1002\/hbm.20553","article-title":"The Neurophysiological Bases of Emotion: An FMRI Study of the Affective Circumplex Using Emotion-Denoting Words","volume":"30","author":"Posner","year":"2009","journal-title":"Hum. Brain Mapp."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1332","DOI":"10.1007\/s10803-013-1993-6","article-title":"Using the Circumplex Model of Affect to Study Valence and Arousal Ratings of Emotional Faces by Children and Adults with Autism Spectrum Disorders","volume":"44","author":"Tseng","year":"2014","journal-title":"J. Autism. Dev. Disord."},{"key":"ref_25","unstructured":"Lang, P.J., Bradley, M.M., and Cuthbert, B.N. (1995). International Affective Picture System (IAPS): Technical Manual and Affective Ratings, NIMH Center for the Study of Emotion and Attention."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"887","DOI":"10.3758\/s13428-013-0405-3","article-title":"The Adaptation of the Affective Norms for English Words (ANEW) for Italian","volume":"46","author":"Montefinese","year":"2014","journal-title":"Behav. Res. Methods"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1168","DOI":"10.3758\/s13428-012-0310-1","article-title":"Affective Auditory Stimuli: Adaptation of the International Affective Digitized Sounds (IADS-2) for European Portuguese","volume":"45","author":"Soares","year":"2013","journal-title":"Behav. Res. Methods"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1038\/s41597-020-0366-1","article-title":"The Chieti Affective Action Videos Database, a Resource for the Study of Emotions in Psychology","volume":"7","author":"Manna","year":"2020","journal-title":"Sci. Data"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1038\/s41597-021-01053-z","article-title":"Updating the Chieti Affective Action Videos Database with Older Adults","volume":"8","author":"Ceccato","year":"2021","journal-title":"Sci. Data"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.trc.2020.01.006","article-title":"Examining the Effects of Emotional Valence and Arousal on Takeover Performance in Conditionally Automated Driving","volume":"112","author":"Du","year":"2020","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"113571","DOI":"10.1016\/j.eswa.2020.113571","article-title":"Convolutional Neural Network Based Emotion Classification Using Electrodermal Activity Signals and Time-Frequency Features","volume":"159","author":"Ganapathy","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Filippini, C., Perpetuini, D., Cardone, D., and Merla, A. (2021). Improving Human\u2013Robot Interaction by Enhancing NAO Robot Awareness of Human Facial Expression. Sensors, 21.","DOI":"10.3390\/s21196438"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.patrec.2017.06.004","article-title":"Reinforcement Online Learning for Emotion Prediction by Using Physiological Signals","volume":"107","author":"Liu","year":"2018","journal-title":"Pattern Recognit. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"429","DOI":"10.2217\/pme-2018-0044","article-title":"Wearables and the Medical Revolution","volume":"15","author":"Dunn","year":"2018","journal-title":"Pers. Med."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Shu, L., Xie, J., Yang, M., Li, Z., Li, Z., Liao, D., Xu, X., and Yang, X. (2018). A Review of Emotion Recognition Using Physiological Signals. Sensors, 18.","DOI":"10.3390\/s18072074"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Guo, H.-W., Huang, Y.-S., Lin, C.-H., Chien, J.-C., Haraikawa, K., and Shieh, J.-S. (November, January 31). Heart Rate Variability Signal Features for Emotion Recognition by Using Principal Component Analysis and Support Vectors Machine. Proceedings of the 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE), Taichung, Taiwan.","DOI":"10.1109\/BIBE.2016.40"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lee, M.S., Lee, Y.K., Pae, D.S., Lim, M.T., Kim, D.W., and Kang, T.K. (2019). Fast Emotion Recognition Based on Single Pulse PPG Signal with Convolutional Neural Network. Appl. Sci., 9.","DOI":"10.3390\/app9163355"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.compind.2017.04.005","article-title":"Respiration-Based Emotion Recognition with Deep Learning","volume":"92\u201393","author":"Zhang","year":"2017","journal-title":"Comput. Ind."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Filippini, C., Perpetuini, D., Cardone, D., Chiarelli, A.M., and Merla, A. (2020). Thermal Infrared Imaging-Based Affective Computing and Its Application to Facilitate Human Robot Interaction: A Review. Appl. Sci., 10.","DOI":"10.3390\/app10082924"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1007\/s12369-020-00661-w","article-title":"Facilitating the Child\u2013Robot Interaction by Endowing the Robot with the Capability of Understanding the Child Engagement: The Case of Mio Amico Robot","volume":"13","author":"Filippini","year":"2021","journal-title":"Int. J. Soc. Robot."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.infrared.2017.01.002","article-title":"Human Emotions Detection Based on a Smart-Thermal System of Thermographic Images","volume":"81","year":"2017","journal-title":"Infrared Phys. Technol."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Perpetuini, D., Cardone, D., Filippini, C., Chiarelli, A.M., and Merla, A. (2019). Modelling Impulse Response Function of Functional Infrared Imaging for General Linear Model Analysis of Autonomic Activity. Sensors, 19.","DOI":"10.3390\/s19040849"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1023\/A:1007442505281","article-title":"Guest Editors\u2019 Introduction: On Applied Research in Machine Learning","volume":"30","author":"Provost","year":"1998","journal-title":"Mach. Learn."},{"key":"ref_44","unstructured":"Bishop, C.M. (2006). Pattern Recognition. Mach. Learn., 128."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"562","DOI":"10.17743\/jaes.2017.0022","article-title":"Supervised Vocal-Based Emotion Recognition Using Multiclass Support Vector Machine, Random Forests, and Adaboost","volume":"65","author":"Noroozi","year":"2017","journal-title":"J. Audio Eng. Soc."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Gonzalez, J., and Prevost, L. (2021, January 23\u201327). Personalizing Emotion Recognition Using Incremental Random Forests. Proceedings of the 2021 29th European Signal Processing Conference (EUSIPCO), Dublin, Ireland.","DOI":"10.23919\/EUSIPCO54536.2021.9616296"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/S0010-9452(58)80003-9","article-title":"Problems in the Assessment of Hand Preference","volume":"21","author":"Salmaso","year":"1985","journal-title":"Cortex"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1002\/(SICI)1099-0992(199607)26:4<557::AID-EJSP769>3.0.CO;2-4","article-title":"Relative Effectiveness and Validity of Mood Induction Procedures: A Meta-Analysis","volume":"26","author":"Westermann","year":"1996","journal-title":"Eur. J. Soc. Psychol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1080\/02699939508408966","article-title":"Emotion Elicitation Using Films","volume":"9","author":"Gross","year":"1995","journal-title":"Cogn. Emot."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/0005-7916(94)90063-9","article-title":"Measuring Emotion: The Self-Assessment Manikin and the Semantic Differential","volume":"25","author":"Bradley","year":"1994","journal-title":"J. Behav. Ther. Exp. Psychiatry"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1016\/j.ejmp.2012.09.003","article-title":"Infrared Camera Assessment of Skin Surface Temperature\u2013Effect of Emissivity","volume":"29","author":"Bernard","year":"2013","journal-title":"Phys. Med."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Diakides, M., Bronzino, J.D., and Peterson, D.R. (2012). Medical Infrared Imaging: Principles and Practices, CRC Press.","DOI":"10.1201\/b12938"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Filippini, C., Cardone, D., Perpetuini, D., Chiarelli, A.M., Gualdi, G., Amerio, P., and Merla, A. (2021). Convolutional Neural Networks for Differential Diagnosis of Raynaud\u2019s Phenomenon Based on Hands Thermal Patterns. Appl. Sci., 11.","DOI":"10.3390\/app11083614"},{"key":"ref_54","first-page":"130","article-title":"The Influence of Angles and Distance on Assessing Inner-Canthi of the Eye Skin Temperature","volume":"27","author":"Vardasca","year":"2017","journal-title":"Thermol. Int."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1037\/1089-2680.10.3.229","article-title":"Heart Rate Variability as an Index of Regulated Emotional Responding","volume":"10","author":"Appelhans","year":"2006","journal-title":"Rev. Gen. Psychol."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Shaffer, F., and Ginsberg, J.P. (2017). An Overview of Heart Rate Variability Metrics and Norms. Front. Public Health, 5.","DOI":"10.3389\/fpubh.2017.00258"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Chen, C.-L., and Chuang, C.-T. (2017). A QRS Detection and R Point Recognition Method for Wearable Single-Lead ECG Devices. Sensors, 17.","DOI":"10.3390\/s17091969"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Gao, R., Islam, A., Gedeon, T., and Hossain, M.Z. (2020, January 18\u201322). Identifying Real and Posed Smiles from Observers\u2019 Galvanic Skin Response and Blood Volume Pulse. Proceedings of the International Conference on Neural Information Processing, Bangkok, Thailand.","DOI":"10.36227\/techrxiv.13180544"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"e10448","DOI":"10.7717\/peerj.10448","article-title":"Prediction of State Anxiety by Machine Learning Applied to Photoplethysmography Data","volume":"9","author":"Perpetuini","year":"2021","journal-title":"PeerJ"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1113\/expphysiol.2008.042424","article-title":"Breathing Rhythms and Emotions","volume":"93","author":"Homma","year":"2008","journal-title":"Exp. Physiol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"084001","DOI":"10.1088\/1361-6579\/ab310a","article-title":"Mutual Information between Heart Rate Variability and Respiration for Emotion Characterization","volume":"40","author":"Valderas","year":"2019","journal-title":"Physiol. Meas."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Cardone, D., Perpetuini, D., Filippini, C., Spadolini, E., Mancini, L., Chiarelli, A.M., and Merla, A. (2020). Driver Stress State Evaluation by Means of Thermal Imaging: A Supervised Machine Learning Approach Based on ECG Signal. Appl. Sci., 10.","DOI":"10.3390\/app10165673"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Abd Latif, M.H., Yusof, H.M., Sidek, S.N., and Rusli, N. (2015, January 18\u201320). Thermal Imaging Based Affective State Recognition. Proceedings of the 2015 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS), Langkawi, Malaysia.","DOI":"10.1109\/IRIS.2015.7451614"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"103595","DOI":"10.1016\/j.infrared.2020.103595","article-title":"Automated Warping Procedure for Facial Thermal Imaging Based on Features Identification in the Visible Domain","volume":"112","author":"Cardone","year":"2021","journal-title":"Infrared Phys. Technol."},{"key":"ref_65","first-page":"20","article-title":"OpenFace: A General-Purpose Face Recognition Library with Mobile Applications","volume":"6","author":"Amos","year":"2016","journal-title":"CMU Sch. Comput. Sci."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Ayata, D., Yaslan, Y., and Kama\u015fak, M. (2016, January 27\u201329). Emotion Recognition via Random Forest and Galvanic Skin Response: Comparison of Time Based Feature Sets, Window Sizes and Wavelet Approaches. Proceedings of the 2016 Medical Technologies National Congress (TIPTEKNO), Antalya, Turkey.","DOI":"10.1109\/TIPTEKNO.2016.7863130"},{"key":"ref_67","unstructured":"Agarap, A.F. (2018). Deep Learning Using Rectified Linear Units (Relu). arXiv."},{"key":"ref_68","unstructured":"Murugan, P., and Durairaj, S. (2017). Regularization and Optimization Strategies in Deep Convolutional Neural Network. arXiv."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1007\/s40846-017-0238-0","article-title":"Differences of Heart Rate Variability between Happiness and Sadness Emotion States: A Pilot Study","volume":"37","author":"Shi","year":"2017","journal-title":"J. Med. Biol. Eng."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Wan, P., Wen, J., and Wu, C. (2015, January 25\u201328). A Discriminating Method of Driving Anger Based on Sample Entropy of EEG and BVP. Proceedings of the 2015 International Conference on Transportation Information and Safety (ICTIS), Wuhan, China.","DOI":"10.1109\/ICTIS.2015.7232093"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Pan, L., Yin, Z., She, S., and Song, A. (2020). Emotional State Recognition from Peripheral Physiological Signals Using Fused Nonlinear Features and Team-Collaboration Identification Strategy. Entropy, 22.","DOI":"10.3390\/e22050511"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Kosonogov, V., Zorzi, L.D., Honor\u00e9, J., Mart\u00ednez-Vel\u00e1zquez, E.S., Nandrino, J.-L., Martinez-Selva, J.M., and Sequeira, H. (2017). Facial Thermal Variations: A New Marker of Emotional Arousal. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0183592"},{"key":"ref_73","first-page":"1","article-title":"Monitoring the Variation in Driver Respiration Rate from Wakefulness to Drowsiness: A Non-Intrusive Method for Drowsiness Detection Using Thermal Imaging","volume":"3","author":"Kiashari","year":"2018","journal-title":"J. Sleep Sci."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1505","DOI":"10.1007\/s12239-021-0130-3","article-title":"Driver-Condition Detection Using a Thermal Imaging Camera and Neural Networks","volume":"22","author":"Kajiwara","year":"2021","journal-title":"Int. J. Automot. Technol."},{"key":"ref_75","unstructured":"Reshma, R. (2021). Emotional and Physical Stress Detection and Classification Using Thermal Imaging Technique. Ann. Rom. Soc. Cell Biol., 8364\u20138374."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/s10973-020-09665-0","article-title":"Thermal Imaging for Detecting Temperature Changes within the Rheumatoid Foot","volume":"145","author":"Rutkowski","year":"2021","journal-title":"J. Therm. Anal. Calorim."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"040902","DOI":"10.1117\/1.JBO.26.4.040902","article-title":"Smartphone-Based Imaging Systems for Medical Applications: A Critical Review","volume":"26","author":"Hunt","year":"2021","journal-title":"J. Biomed. Opt."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Goulart, C., Valad\u00e3o, C., Delisle-Rodriguez, D., Tavares, D., Caldeira, E., and Bastos-Filho, T. (2018, January 21\u201325). Emotional State Analysis through Infrared Thermal Imaging. Proceedings of the XXVI Brazilian Congress on Biomedical Engineering, Arma\u00e7\u00e3o de Buzios, RJ, Brazil.","DOI":"10.1007\/978-981-13-2517-5_31"},{"key":"ref_79","unstructured":"Sarath, S. (2020, January 28\u201330). Human Emotions Recognition from Thermal Images Using Yolo Algorithm. Proceedings of the 2020 International Conference on Communication and Signal Processing (ICCSP), Chennai, India."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/5\/1789\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:26:45Z","timestamp":1760135205000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/5\/1789"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,24]]},"references-count":79,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["s22051789"],"URL":"https:\/\/doi.org\/10.3390\/s22051789","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,24]]}}}