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The present model is based on signal collected from physiological sensors followed by consecutive deployment of unsupervised machine learning model. The proposed model is unsupervised in following aspects: firstly, it introduces\n                    <jats:italic>Expectation Maximization<\/jats:italic>\n                    problem with respect to unknown emotion labels to be derived from the measures. Correlation of physiological signal and individual emotion labels can be identified. This follows a considerable emotion classification method. However, the output of EM model doesn\u2019t ensure the correct identification of emotion class, if any. We introduce Support Vector\n                    <jats:italic>Regression (SVR)<\/jats:italic>\n                    as output module of this model. Hence, we try to forecast the probable classes of emotion after investigating the ranges of values and appropriate standard threshold values of physiological signal with respect to respective emotion class e.g. angry, frustration and joy. This should be noted that, the proposed model doesn\u2019t envisage facial expression analysis. However, after successful implementation of Gaussian behaviors of mixed physiological signal, we can enhance the accuracy of identification. Significant emotional context exists in output with more precise results of emotion identification phases.\n                  <\/jats:p>","DOI":"10.3233\/jifs-179686","type":"journal-article","created":{"date-parts":[[2020,3,13]],"date-time":"2020-03-13T13:04:08Z","timestamp":1584104648000},"page":"5999-6017","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Identifying emotion pattern from physiological sensors through unsupervised\u00a0EMDeep model"],"prefix":"10.1177","volume":"38","author":[{"given":"Viviane","family":"Gal","sequence":"first","affiliation":[{"name":"Centre d\u2019Etudes et De Recherche en Informatique et Communications (CEDRIC) \/ Conservatoire National des Arts et M\u00e9tiers (Cnam), Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Soumya","family":"Banerjee","sequence":"additional","affiliation":[{"name":"Centre d\u2019Etudes et De Recherche en Informatique et Communications (CEDRIC) \/ Conservatoire National des Arts et M\u00e9tiers (Cnam), Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dana V.","family":"Rad","sequence":"additional","affiliation":[{"name":"Faculty of Educational Science, Psychology &amp; Social Sciences, Aurel Vlaicu University of Arad, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2020,3,11]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"YangB. 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