{"references": ["Nakasone, Arturo, Prendinger, Helmut and Ishizuka, Mitsuru Emotion\nRecognition from Electromyography and Skin Conductance.. 2005. The\nFifth International Workshop on Biosignal Interpretation BSI-05.", "Zhai, Jing and Barreto, Armando Stress Detection in Computer Users\nBased on Digital Signal Processing of Noninvasive Physiological\nVariables.. New York City : s.n., 2006. Proceedings of the 28th IEEE\nEMBS Annual International Conference.", "Picard, Rosalind W. Affective Computing. s.l. : MIT Press, 2000.", "Arroyo-Palacios, J. and Romano, D.M. Towards a Standarization in the\nUse of Physiological Signals for Affective Recognition Systems. The\nNetherlands : s.n., 2008. Proceedings of Measuring Behavior.", "Cs\u251c\u00a1kszentmih\u251c\u00edlyi, Mih\u251c\u00edly. Flow: The Psychology of Optimal\nExperience. New York : Harper and Row, 1990.", "Steels, L. An Architecture of Flow. [book auth.] M. Tokoro and L.\nSteels. A Learning Zone of One's Own. Amsterdam : IOS Press, 2004,\npp. 137-150.", "Kort, B., Reilly, R. and Picard, R.W. An Affective Model of Interplay\nbetween Emotions and Learning. Madison, WI : IEEE, 2001.\nConference on Advanced Learning Technologies. pp. 43-46.", "Kleine, M., et al. The structure of students' emotions experienced during\na mathematical achievement test. 2005, ZDM.", "Pekrun, R., Frenzel, A. and Goetz, T. The Control-Value Theory of\nAchievement Emotions: An Integrative Approach to Emotion in\nEducation. [book auth.] M. Tokoro and L. Steels. Emotion in Education.\nSan Diego : Academic Press, 2007, pp. 13-36.\n[10] Chaffar, S. and Frasson, C. The Emotional Conditions of Learning.\n2005, American Association for Artificial Intelligence.\n[11] Craig, S. D., et al. Affect and learning: an exploratory look into the role\nof affect in learning with AutoTutor. October 2004, Journal of\nEducational Media.\n[12] D'Mello, S., et al. AutoTutor Detects and Responds to Learners\nAffective and Cognitive States. Montreal, Canada : s.n., 2008.\nWorkshop on Emotional and Cognitive Issues at the International\nConference of Intelligent Tutoring Systems.\n[13] Mota, Selene and Picard, Rosalind. Automated Posture Analysis for\nDetecting Learner's Interest Level. Computer Vision and Patten\nRecognition Workshop, 2003\n[14] Boekaerts, M. and Corno, L. Self-Regulation in the Classroom: A\nPerspective on Assessment and Intervention. University of Leiden, The\nNetherlands : Applied Psychology: An Internatinal Review, 2005.\n[15] Lekkala, J., Salpavaara, T. and K\u251c\u00f1rki, S. EMFI - Vesatile Material for\nMonitoring of Human Functions. Rio de Janeiro : s.n., 2006. XVIII\nImeko World Congress.\n[16] Alamets\u251c\u00f1, J., et al. The Potential of EMFi Sensors in Heart Activity\nMonitoring. Berlin, Germany : s.n., 2004. 2nd OpenECG Workshop\n\"Integration of the ECG into the EHR & Interoperability of ECG Device\nSystems\".\n[17] Koivistoinen, T., Junnila, S. and V\u251c\u00f1rri, A. An EMFi-film Sensor based\nBallistocardiographic Chair: Performance and Cycle Extraction Method.\n2005. IEEE Workshop on Signal Processing Systems Design and\nImplementation. pp. 373-377.\n[18] Koivistoinen, T., et al. A New Method for Measuring the\nBallistocardiogram using EMFi Sensors in a normal chair. San\nFrancisco, CA : s.n., 2004. Proceedings of the 26th Annual International\nConference of the IEEE EMBS.\n[19] K\u00e4rki, S., K\u00e4\u00e4ri\u00e4inen, M. and Lekkala, J. Measurement of heart sounds\nwith EMFi transducer. Lyon, France : s.n., 2007. Proceedings of the 29th\nAnnual International Conference of the IEEE EMBS.\n[20] Anttonen, J. Using the EMFi chair to measure the user's emotion-related\nheart rate responses. Department of Computer Sciences, University of\nTampere. 2005. Master's Thesis.\n[21] Vyzas, E. Recognition of Emotional and Cognitive States Using\nPshysiological Data. Department of Mechanical Engineering,\nMassachusetts Institute of Technology. Massachusetts : s.n., 1999.\nBachelor Thesis.\n[22] Kamiya, K., et al. Sitting Posture Analysis by Pressure Sensors. 2008.\nICPR08. pp. 1-4.\n[23] Graesser, A. C., et al. Detection of Emotions During Learning with\nAutoTutor. Vancouver, Canada : s.n., 2006. Proceedings of the 28th\nAnnual Meeting of the Cognitive Science Society. pp. 285-290.\n[24] Paquette, G. An Ontology and a Software Framework for Competency\nModeling and Management Competency in an Instructional Engineering\nMethod ( MISA ). 2007. Educational Technology & Society, 10, pp. 1-\n21.\n[25] Anderson, J. R., Bothell, D., Byrne, M. D., Douglass, S., Lebiere C.,\nQin, Y., et al. (2004). An Integrated Theory of the Mind. Psychological\nReview, 111, (4), 1036-1060. Retrieved from http://actr.\npsy.cmu.edu/publications/pubinfo.php?id=526\n[26] Newell, A. Unified Theories of Cognition. MA: Harvard University\nPress 1994.\n[27] Ramirez, C., & Cooley, R. . A Theory of the Acquisition of Episodic\nMemory. In Proceeding of the ECML-97: Case-Based Reasoning\nWorkshop. Prague,. Czech Republic.: Springer-Verlag. 1997a, p48-55\n[28] Wang, Y. The Theoretical Framework and Cognitive Process of\nLearning. In D. Zhang, Y. Wang, & W. Kinsner, Proc. 6th IEEE Int.\nConf. on Cognitive Informatics (ICCI'07) 2007 pp. 470-479.\n[29] Ramirez, C., Valdes, B. \"A general knowledge representation model for\nthe acquisition of skills and concepts,\" coginf, pp.412-417, 2009 8th\nIEEE International Conference on Cognitive Informatics, 2009\n[30] Torres, J., Dodero, J. M., Ramirez, C., Valdes, B., & Lugo, A.. Adaptive\nLearning Scenarios Based on Student Profile. In U. A. de Yucat\u251c\u00edn,\nRecursos Digitales para el Aprendizaje. 2009 pp. 348-349.\n[31] Torres, J., Ju\u251c\u00edrez, E., Dodero, J. M., & Aedo, I. Advanced Transactional\nModels for Complex Learning Processes. In Recursos Digitales para el\nAprendizaje. Merida: U A. de Yucatan. . 2009"]}

Emotions are related with learning processes and physiological signals can be used to detect them for the personalization of learning resources and to control the pace of instruction. A model of relevant emotions has been developed, where specific combinations of emotions and cognition processes are connected and integrated with the concept of 'flow', in order to improve learning. The cardiac pulse is a reliable signal that carries useful information about the subject-s emotional condition; it is detected using a classroom chair adapted with non invasive EMFi sensor and an acquisition system that generates a ballistocardiogram (BCG), the signal is processed by an algorithm to obtain characteristics that match a specific emotional condition. The complete chair system is presented in this work, along with a framework for the personalization of learning resources.