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International audience; Pattern of emotion identification is one of the improvised research application regarding facial expression as major concern, in those cases, conventional facial expressions for patterns identification. The present model is based on signal collected from phys-iological sensors followed by consecutive deployment of unsupervised machine learning model. The proposed model is unsu-pervised in following aspects: firstly, it introduces Expectation Maximization 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’t ensure the correct identification of emo-tion class, if any. We introduce Support Vector Regression (SVR) 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’t envisage facial expression analysis. However, after successful implementation of Gaussian behaviors of mixed physio-logical signal, we can enhance the accuracy of identification. Significant emotional context exists in output with more precise results of emotion identification phases.

Living exceptional moments, experiencing thrills, well-being, blooming, are often part of our dreams or aspirations. We choose various ways to get there like games. Whether the player is looking for originality, challenges, discovery, a story, or other goals, emotional states are the purpose of his quest. He remains until the game gives him pleasure, sensations. How bring them there? We are developing a new human environment interaction that takes into account and adapts to emotions. We address video or pervasive games or other applications. Through this goal, players should not be bothered by interfaces, or biosensors invasivness. This work raises two questions:- Can we discover emotional states based on physiological measurements from contact biosensors?- If so, can these sensors be replaced by remote, non-invasive devices and produce the same results?The models we have developed propose solutions based on unsupervised machine learning methods. We also present remote measurements technics and explain our future works in a new field we call affectics., Vivre des moments exceptionnels, connaître des sensations fortes, du bien-être, nous épanouir, font souvent partie de nos rêves ou aspirations. Nous choisissons des moyens divers pour y arriver comme le jeu. Que le joueur recherche l’originalité, les défis, la découverte, une histoire, ou d’autres buts, ce sont des états émotionnels qui sont l’objet de sa quête. Il attend que le jeu lui procure du plaisir, des sensations. Comment les lui apporter ? Mettre au point une nouvelle interaction humain environnement, dans les jeux vidéo ou pervasifs ou autres applications, prenant en compte et s’adaptant aux émotions de chacun, sans être gêné par les interfaces, biocapteurs de contact par exemple, est notre objectif. Cela soulève deux questions : - Peut-on découvrir des états émotionnels à partir de mesures physiologiques issues de biocapteurs de contact ? - Si oui, ces capteurs peuvent-ils être remplacés par des dispositifs distants, donc non invasifs, et produire les mêmes résultats ?Les modèles mis au point proposent des solutions à base de méthodes mathématiques d’apprentissage non supervisées. Nous présentons aussi des moyens de mesures à distance et expliquons les futurs travaux dans le domaine que nous baptisons affectique.