{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T23:02:11Z","timestamp":1778886131761,"version":"3.51.4"},"reference-count":11,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,1,7]],"date-time":"2021-01-07T00:00:00Z","timestamp":1609977600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,1,7]],"date-time":"2021-01-07T00:00:00Z","timestamp":1609977600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100003093","name":"Ministry of Higher Education, Malaysia","doi-asserted-by":"crossref","award":["FRGS0512-1\/2019"],"award-info":[{"award-number":["FRGS0512-1\/2019"]}],"id":[{"id":"10.13039\/501100003093","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Emotion prediction is a method that recognizes the human emotion derived from the subject\u2019s psychological data. The problem in question is the limited use of heart rate (HR) as the prediction feature through the use of common classifiers such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest (RF) in emotion prediction. This paper aims to investigate whether HR signals can be utilized to classify four-class emotions using the emotion model from Russell\u2019s in a virtual reality (VR) environment using machine learning.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Method<\/jats:title>\n                <jats:p>An experiment was conducted using the Empatica E4 wristband to acquire the participant\u2019s HR, a VR headset as the display device for participants to view the 360\u00b0 emotional videos, and the Empatica E4 real-time application was used during the experiment to extract and process the participant's recorded heart rate.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Findings<\/jats:title>\n                <jats:p>For intra-subject classification, all three classifiers SVM, KNN, and RF achieved 100% as the highest accuracy while inter-subject classification achieved 46.7% for SVM, 42.9% for KNN and 43.3% for RF.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>The results demonstrate the potential of SVM, KNN and RF classifiers to classify HR as a feature to be used in emotion prediction in four distinct emotion classes in a virtual reality environment. The potential applications include interactive gaming, affective entertainment, and VR health rehabilitation.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s40537-020-00401-x","type":"journal-article","created":{"date-parts":[[2021,1,7]],"date-time":"2021-01-07T16:03:46Z","timestamp":1610035426000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Multiclass emotion prediction using heart rate and virtual reality stimuli"],"prefix":"10.1186","volume":"8","author":[{"given":"Aaron Frederick","family":"Bulagang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James","family":"Mountstephens","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2415-5915","authenticated-orcid":false,"given":"Jason","family":"Teo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,1,7]]},"reference":[{"issue":"3","key":"401_CR1","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1109\/TAFFC.2017.2714671","volume":"10","author":"SM Alarcao","year":"2017","unstructured":"Alarcao SM, Fonseca MJ. Emotions recognition using EEG signals: a survey. IEEE Trans Affect Comput. 2017;10(3):374\u201393. https:\/\/doi.org\/10.1109\/TAFFC.2017.2714671.","journal-title":"IEEE Trans Affect Comput"},{"key":"401_CR2","doi-asserted-by":"crossref","unstructured":"Ali M, Machot AH, Al F, Kyamakya K. emotion recognition involving physiological and speech signals: a comprehensive review. In: Studies in systems, decision and control. vol. 109. Springer International Publishing; p. 287\u2013302; 2018.","DOI":"10.1007\/978-3-319-58996-1_13"},{"issue":"2","key":"401_CR3","doi-asserted-by":"publisher","first-page":"37","DOI":"10.3390\/mti3020037","volume":"3","author":"MZ Baig","year":"2019","unstructured":"Baig MZ, Kavakli M. A survey on psycho-physiological analysis & measurement methods in multimodal systems. 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A machine learning approach to classify emotions using GSR. 2015;2(12):72\u201376."},{"key":"401_CR7","doi-asserted-by":"publisher","unstructured":"M\u00e9nard M, Richard P, Hamdi H, Dauc\u00e9 B, Yamaguchi T. Emotion recognition based on heart rate and skin conductance. In: PhyCS 2015\u20142nd international conference on physiological computing systems, proceedings, 26\u201332. 2015. https:\/\/doi.org\/10.5220\/0005241100260032","DOI":"10.5220\/0005241100260032"},{"key":"401_CR8","unstructured":"Minhad, KN., Ali, S. HMD., and Reaz, MBI. (2017) \u201cA Design Framework for Human Emotion Recognition Using Electrocardiogram and Skin Conductance Response Signals,\u201d J. Eng. Sci. Technol., vol. 12, no. 11, pp. 3102\u20133119, 2017."},{"key":"401_CR9","doi-asserted-by":"crossref","unstructured":"Nguyen NT, Nguyen NV, My Huynh T, Tran, Nguyen Binh T. A potential approach for emotion prediction using heart rate signals. In: 9th international conference on knowledge and systems engineering (KSE), Ho Chi Minh city, Vietnam. 2017.","DOI":"10.1109\/KSE.2017.8119462"},{"key":"401_CR10","doi-asserted-by":"publisher","DOI":"10.11591\/ijece.v8i5.pp4004-4014","author":"DB Setyohadi","year":"2018","unstructured":"Setyohadi DB, Kusrohmaniah S, Gunawan SB. Galvanic skin response data classification for emotion detection. Int J Electr Comput Eng. 2018. https:\/\/doi.org\/10.11591\/ijece.v8i5.pp4004-4014.","journal-title":"Int J Electr Comput Eng."},{"key":"401_CR11","doi-asserted-by":"publisher","first-page":"718","DOI":"10.3390\/s20030718","volume":"20","author":"L Shu","year":"2020","unstructured":"Shu L, Yu Y, Chen W, Hua H, Li Q, Jin J, Xu X. Wearable emotion recognition using heart rate data from a smart bracelet. 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