
Contents
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Description of an Autonomous Closed-Loop Biofeedback System Description of an Autonomous Closed-Loop Biofeedback System
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Biosignals, Biofeedback, and Affect Biosignals, Biofeedback, and Affect
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The Closed-Loop Autonomous Biofeedback Model The Closed-Loop Autonomous Biofeedback Model
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Application of an Autonomous Closed-Loop Biofeedback System: The Affective Music Player Application of an Autonomous Closed-Loop Biofeedback System: The Affective Music Player
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Sensing and Signal Processing Sensing and Signal Processing
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Pattern Recognition, Influencing, and Feedback Pattern Recognition, Influencing, and Feedback
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Validation Validation
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Discussion Discussion
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Acknowledgments Acknowledgments
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Notes Notes
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References References
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35 Autonomous Closed-Loop Biofeedback: An Introduction and a Melodious Application
Get accessEgon L. van den Broek has a background in Artificial Intelligence (AI) (MSc, 2001, Radboud University Nijmegen, RU). He obtained his first PhD on Content-Based Image Retrieval (CBIR) (2005) from the RU and his second PhD on Affective Signal Processing (ASP) (2011) from the University of Twente. Currently, Egon is Chief Science Officer at Information eXperience (IX) B.V., assistant professor at the Utrecht University, and consultant (e.g., for TNO, Philips, and the United Nations). He has developed and lectured several courses and MSc tracks and guided 50+ students. Further, Egon serves as external expert for various agencies (e.g., EU, IWT, and ANR), as program committee member of conferences, on boards of advice, and on editorial boards of an encyclopedia and journals (e.g., Behaviour & Information Technology, Journal of Ambient Intelligence and Smart Environments, and Pattern Recognition Letters). Egon has published 150+ scientific articles and has several patent applications pending. He frequently serves as invited and keynote speaker, chair of conferences and workshops, and has received several awards.
Joris H. Janssen received the BSc and MSc (cum laude) degrees in artificial intelligence from Radboud University, Nijmegen, The Netherlands, in 2007 and 2008, respectively. He is currently working toward the PhD degree in the Human Technology Interaction Group, School of Innovation Sciences, Eindhoven University of Technology, The Netherlands. He is also a visiting scholar in the Department of Communication of Stanford University, California, and a visiting researcher at Philips Research, Eindhoven, The Netherlands. His main research interests are centered around affective computing, emotion, and applied psychophysiology. His current work focuses on wearable technologies that can augment emotion communication and improve intimacy between people. Written output of his work can be found in more than 20 peer-reviewed journal, conference, and book chapter publications as well as three US patent applications.
Joyce H.D.M. Westerink studied physics at Utrecht University, and graduated in 1985. Since then, she joined Philips Research and specialized on topics involving human perception and cognition of consumer products: visual perception of display devices, user-friendliness of home entertainment systems, sensory aspects of personal care products and psychophysiological aspects of user experience. The first 14 years she did this research on detachment at the Tue-based Institute for Perception Research (IPO), and since then she has been member of several research groups at Philips Research dedicated to personal care products, media interaction, and brain and behavior. Her current interest is in psychophysiology and human wellbeing. Written output of her work can be found in some 35 articles in books and international journals and 7 US patents.
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Published:04 August 2014
Cite
Abstract
This chapter is from the forthcoming The Oxford Handbook of Affective Computing edited by Rafael Calvo, Sidney K. D'Mello, Jonathan Gratch, and Arvid Kappas. This chapter defines the core concepts surrounding biofeedback and denotes their relations. Subsequently, a closed-loop human-machine architecture is introduced in which a biofeedback protocol is executed. This architecture is brought from theory to practice via a personalized affective music player (AMP). Regression and kernel density estimation are applied to model the physiological changes elicited by music. The AMP was validated via a real-world evaluation over the course of several weeks. Results show that our autonomous closed-loop biofeedback system can cope with noisy situations and handle large interindividual differences in the music domain. The AMP augments music listening, where its techniques enable autonomous affect guidance. Our approach provides valuable insights for affective computing and autonomous closed-loop biofeedback systems in general.
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