Non-Invasive Technology on a Classroom Chair for Detection of Emotions Used for the Personalization of Learning Resources
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.
[1] Nakasone, Arturo, Prendinger, Helmut and Ishizuka, Mitsuru Emotion
Recognition from Electromyography and Skin Conductance.. 2005. The
Fifth International Workshop on Biosignal Interpretation BSI-05.
[2] Zhai, Jing and Barreto, Armando Stress Detection in Computer Users
Based on Digital Signal Processing of Noninvasive Physiological
Variables.. New York City : s.n., 2006. Proceedings of the 28th IEEE
EMBS Annual International Conference.
[3] Picard, Rosalind W. Affective Computing. s.l. : MIT Press, 2000.
[4] Arroyo-Palacios, J. and Romano, D.M. Towards a Standarization in the
Use of Physiological Signals for Affective Recognition Systems. The
Netherlands : s.n., 2008. Proceedings of Measuring Behavior.
[5] Csíkszentmihályi, Mihály. Flow: The Psychology of Optimal
Experience. New York : Harper and Row, 1990.
[6] Steels, L. An Architecture of Flow. [book auth.] M. Tokoro and L.
Steels. A Learning Zone of One's Own. Amsterdam : IOS Press, 2004,
pp. 137-150.
[7] Kort, B., Reilly, R. and Picard, R.W. An Affective Model of Interplay
between Emotions and Learning. Madison, WI : IEEE, 2001.
Conference on Advanced Learning Technologies. pp. 43-46.
[8] Kleine, M., et al. The structure of students' emotions experienced during
a mathematical achievement test. 2005, ZDM.
[9] Pekrun, R., Frenzel, A. and Goetz, T. The Control-Value Theory of
Achievement Emotions: An Integrative Approach to Emotion in
Education. [book auth.] M. Tokoro and L. Steels. Emotion in Education.
San Diego : Academic Press, 2007, pp. 13-36.
[10] Chaffar, S. and Frasson, C. The Emotional Conditions of Learning.
2005, American Association for Artificial Intelligence.
[11] Craig, S. D., et al. Affect and learning: an exploratory look into the role
of affect in learning with AutoTutor. October 2004, Journal of
Educational Media.
[12] D'Mello, S., et al. AutoTutor Detects and Responds to Learners
Affective and Cognitive States. Montreal, Canada : s.n., 2008.
Workshop on Emotional and Cognitive Issues at the International
Conference of Intelligent Tutoring Systems.
[13] Mota, Selene and Picard, Rosalind. Automated Posture Analysis for
Detecting Learner's Interest Level. Computer Vision and Patten
Recognition Workshop, 2003
[14] Boekaerts, M. and Corno, L. Self-Regulation in the Classroom: A
Perspective on Assessment and Intervention. University of Leiden, The
Netherlands : Applied Psychology: An Internatinal Review, 2005.
[15] Lekkala, J., Salpavaara, T. and Kärki, S. EMFI - Vesatile Material for
Monitoring of Human Functions. Rio de Janeiro : s.n., 2006. XVIII
Imeko World Congress.
[16] Alametsä, J., et al. The Potential of EMFi Sensors in Heart Activity
Monitoring. Berlin, Germany : s.n., 2004. 2nd OpenECG Workshop
"Integration of the ECG into the EHR & Interoperability of ECG Device
Systems".
[17] Koivistoinen, T., Junnila, S. and Värri, A. An EMFi-film Sensor based
Ballistocardiographic Chair: Performance and Cycle Extraction Method.
2005. IEEE Workshop on Signal Processing Systems Design and
Implementation. pp. 373-377.
[18] Koivistoinen, T., et al. A New Method for Measuring the
Ballistocardiogram using EMFi Sensors in a normal chair. San
Francisco, CA : s.n., 2004. Proceedings of the 26th Annual International
Conference of the IEEE EMBS.
[19] Kärki, S., Kääriäinen, M. and Lekkala, J. Measurement of heart sounds
with EMFi transducer. Lyon, France : s.n., 2007. Proceedings of the 29th
Annual International Conference of the IEEE EMBS.
[20] Anttonen, J. Using the EMFi chair to measure the user's emotion-related
heart rate responses. Department of Computer Sciences, University of
Tampere. 2005. Master's Thesis.
[21] Vyzas, E. Recognition of Emotional and Cognitive States Using
Pshysiological Data. Department of Mechanical Engineering,
Massachusetts Institute of Technology. Massachusetts : s.n., 1999.
Bachelor Thesis.
[22] Kamiya, K., et al. Sitting Posture Analysis by Pressure Sensors. 2008.
ICPR08. pp. 1-4.
[23] Graesser, A. C., et al. Detection of Emotions During Learning with
AutoTutor. Vancouver, Canada : s.n., 2006. Proceedings of the 28th
Annual Meeting of the Cognitive Science Society. pp. 285-290.
[24] Paquette, G. An Ontology and a Software Framework for Competency
Modeling and Management Competency in an Instructional Engineering
Method ( MISA ). 2007. Educational Technology & Society, 10, pp. 1-
21.
[25] Anderson, J. R., Bothell, D., Byrne, M. D., Douglass, S., Lebiere C.,
Qin, Y., et al. (2004). An Integrated Theory of the Mind. Psychological
Review, 111, (4), 1036-1060. Retrieved from http://actr.
psy.cmu.edu/publications/pubinfo.php?id=526
[26] Newell, A. Unified Theories of Cognition. MA: Harvard University
Press 1994.
[27] Ramirez, C., & Cooley, R. . A Theory of the Acquisition of Episodic
Memory. In Proceeding of the ECML-97: Case-Based Reasoning
Workshop. Prague,. Czech Republic.: Springer-Verlag. 1997a, p48-55
[28] Wang, Y. The Theoretical Framework and Cognitive Process of
Learning. In D. Zhang, Y. Wang, & W. Kinsner, Proc. 6th IEEE Int.
Conf. on Cognitive Informatics (ICCI'07) 2007 pp. 470-479.
[29] Ramirez, C., Valdes, B. "A general knowledge representation model for
the acquisition of skills and concepts," coginf, pp.412-417, 2009 8th
IEEE International Conference on Cognitive Informatics, 2009
[30] Torres, J., Dodero, J. M., Ramirez, C., Valdes, B., & Lugo, A.. Adaptive
Learning Scenarios Based on Student Profile. In U. A. de Yucatán,
Recursos Digitales para el Aprendizaje. 2009 pp. 348-349.
[31] Torres, J., Juárez, E., Dodero, J. M., & Aedo, I. Advanced Transactional
Models for Complex Learning Processes. In Recursos Digitales para el
Aprendizaje. Merida: U A. de Yucatan. . 2009
[1] Nakasone, Arturo, Prendinger, Helmut and Ishizuka, Mitsuru Emotion
Recognition from Electromyography and Skin Conductance.. 2005. The
Fifth International Workshop on Biosignal Interpretation BSI-05.
[2] Zhai, Jing and Barreto, Armando Stress Detection in Computer Users
Based on Digital Signal Processing of Noninvasive Physiological
Variables.. New York City : s.n., 2006. Proceedings of the 28th IEEE
EMBS Annual International Conference.
[3] Picard, Rosalind W. Affective Computing. s.l. : MIT Press, 2000.
[4] Arroyo-Palacios, J. and Romano, D.M. Towards a Standarization in the
Use of Physiological Signals for Affective Recognition Systems. The
Netherlands : s.n., 2008. Proceedings of Measuring Behavior.
[5] Csíkszentmihályi, Mihály. Flow: The Psychology of Optimal
Experience. New York : Harper and Row, 1990.
[6] Steels, L. An Architecture of Flow. [book auth.] M. Tokoro and L.
Steels. A Learning Zone of One's Own. Amsterdam : IOS Press, 2004,
pp. 137-150.
[7] Kort, B., Reilly, R. and Picard, R.W. An Affective Model of Interplay
between Emotions and Learning. Madison, WI : IEEE, 2001.
Conference on Advanced Learning Technologies. pp. 43-46.
[8] Kleine, M., et al. The structure of students' emotions experienced during
a mathematical achievement test. 2005, ZDM.
[9] Pekrun, R., Frenzel, A. and Goetz, T. The Control-Value Theory of
Achievement Emotions: An Integrative Approach to Emotion in
Education. [book auth.] M. Tokoro and L. Steels. Emotion in Education.
San Diego : Academic Press, 2007, pp. 13-36.
[10] Chaffar, S. and Frasson, C. The Emotional Conditions of Learning.
2005, American Association for Artificial Intelligence.
[11] Craig, S. D., et al. Affect and learning: an exploratory look into the role
of affect in learning with AutoTutor. October 2004, Journal of
Educational Media.
[12] D'Mello, S., et al. AutoTutor Detects and Responds to Learners
Affective and Cognitive States. Montreal, Canada : s.n., 2008.
Workshop on Emotional and Cognitive Issues at the International
Conference of Intelligent Tutoring Systems.
[13] Mota, Selene and Picard, Rosalind. Automated Posture Analysis for
Detecting Learner's Interest Level. Computer Vision and Patten
Recognition Workshop, 2003
[14] Boekaerts, M. and Corno, L. Self-Regulation in the Classroom: A
Perspective on Assessment and Intervention. University of Leiden, The
Netherlands : Applied Psychology: An Internatinal Review, 2005.
[15] Lekkala, J., Salpavaara, T. and Kärki, S. EMFI - Vesatile Material for
Monitoring of Human Functions. Rio de Janeiro : s.n., 2006. XVIII
Imeko World Congress.
[16] Alametsä, J., et al. The Potential of EMFi Sensors in Heart Activity
Monitoring. Berlin, Germany : s.n., 2004. 2nd OpenECG Workshop
"Integration of the ECG into the EHR & Interoperability of ECG Device
Systems".
[17] Koivistoinen, T., Junnila, S. and Värri, A. An EMFi-film Sensor based
Ballistocardiographic Chair: Performance and Cycle Extraction Method.
2005. IEEE Workshop on Signal Processing Systems Design and
Implementation. pp. 373-377.
[18] Koivistoinen, T., et al. A New Method for Measuring the
Ballistocardiogram using EMFi Sensors in a normal chair. San
Francisco, CA : s.n., 2004. Proceedings of the 26th Annual International
Conference of the IEEE EMBS.
[19] Kärki, S., Kääriäinen, M. and Lekkala, J. Measurement of heart sounds
with EMFi transducer. Lyon, France : s.n., 2007. Proceedings of the 29th
Annual International Conference of the IEEE EMBS.
[20] Anttonen, J. Using the EMFi chair to measure the user's emotion-related
heart rate responses. Department of Computer Sciences, University of
Tampere. 2005. Master's Thesis.
[21] Vyzas, E. Recognition of Emotional and Cognitive States Using
Pshysiological Data. Department of Mechanical Engineering,
Massachusetts Institute of Technology. Massachusetts : s.n., 1999.
Bachelor Thesis.
[22] Kamiya, K., et al. Sitting Posture Analysis by Pressure Sensors. 2008.
ICPR08. pp. 1-4.
[23] Graesser, A. C., et al. Detection of Emotions During Learning with
AutoTutor. Vancouver, Canada : s.n., 2006. Proceedings of the 28th
Annual Meeting of the Cognitive Science Society. pp. 285-290.
[24] Paquette, G. An Ontology and a Software Framework for Competency
Modeling and Management Competency in an Instructional Engineering
Method ( MISA ). 2007. Educational Technology & Society, 10, pp. 1-
21.
[25] Anderson, J. R., Bothell, D., Byrne, M. D., Douglass, S., Lebiere C.,
Qin, Y., et al. (2004). An Integrated Theory of the Mind. Psychological
Review, 111, (4), 1036-1060. Retrieved from http://actr.
psy.cmu.edu/publications/pubinfo.php?id=526
[26] Newell, A. Unified Theories of Cognition. MA: Harvard University
Press 1994.
[27] Ramirez, C., & Cooley, R. . A Theory of the Acquisition of Episodic
Memory. In Proceeding of the ECML-97: Case-Based Reasoning
Workshop. Prague,. Czech Republic.: Springer-Verlag. 1997a, p48-55
[28] Wang, Y. The Theoretical Framework and Cognitive Process of
Learning. In D. Zhang, Y. Wang, & W. Kinsner, Proc. 6th IEEE Int.
Conf. on Cognitive Informatics (ICCI'07) 2007 pp. 470-479.
[29] Ramirez, C., Valdes, B. "A general knowledge representation model for
the acquisition of skills and concepts," coginf, pp.412-417, 2009 8th
IEEE International Conference on Cognitive Informatics, 2009
[30] Torres, J., Dodero, J. M., Ramirez, C., Valdes, B., & Lugo, A.. Adaptive
Learning Scenarios Based on Student Profile. In U. A. de Yucatán,
Recursos Digitales para el Aprendizaje. 2009 pp. 348-349.
[31] Torres, J., Juárez, E., Dodero, J. M., & Aedo, I. Advanced Transactional
Models for Complex Learning Processes. In Recursos Digitales para el
Aprendizaje. Merida: U A. de Yucatan. . 2009
@article{"International Journal of Business, Human and Social Sciences:61781", author = "Carlos Ramirez and Carlos Concha and Benjamin Valdes", title = "Non-Invasive Technology on a Classroom Chair for Detection of Emotions Used for the Personalization of Learning Resources", abstract = "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.", keywords = "Ballistocardiogram, emotions in learning, noninvasive sensors, personalization of learning resources.", volume = "4", number = "6", pages = "1348-7", }
