Heart Rate Monitoring as an Easy Way to Increase Engagement in Human-Agent Interaction

Jérémy Frey


Physiological sensors are gaining the attention of manufacturers and users. As denoted by devices such as smartwatches or the newly released Kinect 2 – which can covertly measure heartbeats – or by the popularity of smartphone apps that track heart rate during fitness activities. Soon, physiological monitoring could become widely accessible and transparent to users. We demonstrate how one could take advantage of this situation to increase users’ engagement and enhance user experience in human-agent interaction. We created an experimental protocol involving embodied agents – “virtual avatars”. Those agents were displayed alongside a beating heart. We compared a condition in which this feedback was simply duplicating the heart rates of users to another condition in which it was set to an average heart rate. Results suggest a superior social presence of agents when they display feedback similar to users’ internal state. This physiological “similarity-attraction” effect may lead, with little effort, to a better acceptance of agents and robots by the general public.


  1. Agelink, M. W., Malessa, R., Baumann, B., Majewski, T., Akila, F., Zeit, T., and Ziegler, D. (2001). Standardized tests of heart rate variability: normal ranges obtained from 309 healthy humans, and effects of age, gender, and heart rate. Clinical Autonomic Research, 11(2):99-108.
  2. Becker, C. and Prendinger, H. (2005). Evaluating affective feedback of the 3D agent max in a competitive cards game. In Affective Computing and Intelligent Interaction, pages 466-473.
  3. Berta, R., Bellotti, F., De Gloria, A., Pranantha, D., and Schatten, C. (2013). Electroencephalogram and Physiological Signal Analysis for Assessing Flow in Games. IEEE Transactions on Computational Intelligence and AI in Games, 5(2):164-175.
  4. Bestgen, Y., Fairon, C., and Kerves, L. (2004). Un barometre affectif effectif: Corpus de référence et méthode pour déterminer la valence affective de phrases. Journées internationales d'analyse statistique des donnés textuelles (JADT).
  5. Fairclough, S. H. (2014). Human Sensors - Perspectives on the Digital Self. Keynote at Sensornet 7814.
  6. Fukushima, H., Terasawa, Y., and Umeda, S. (2011). Association between interoception and empathy: evidence from heartbeat-evoked brain potential. International journal of psychophysiology : official journal of the International Organization of Psychophysiology, 79(2):259-65.
  7. Gerlinghaus, F., Pierce, B., Metzler, T., Jowers, I., Shea, K., and Cheng, G. (2012). Design and emotional expressiveness of Gertie (An open hardware robotic desk lamp). IEEE RO-MAN 7812, pages 1129-1134.
  8. Harrison, C., Horstman, J., Hsieh, G., and Hudson, S. (2012). Unlocking the expressivity of point lights. In CHI 7812, page 1683, New York, New York, USA. ACM Press.
  9. Huppi, B. Q., Stringer, C. J., Bell, J., and Capener, C. J. (2003). United States Patent 6658577: Breathing status LED indicator.
  10. ITU (2003). P. 851, Subjective Quality Evaluation of Telephone Services Based on Spoken Dialogue Systems. International Telecommunication Union, Geneva.
  11. Karlesky, M. and Isbister, K. (2014). Designing for the Physical Margins of Digital Workspaces: Fidget Widgets in Support of Productivity and Creativity. In TEI 7814.
  12. Kranjec, J., Beguš, S., Geršak, G., and Drnovšek, J. (2014). Non-contact heart rate and heart rate variability measurements: A review. Biomedical Signal Processing and Control, 13:102-112.
  13. Le Tallec, M., Antoine, J.-Y., Villaneau, J., and Duhaut, D. (2011). Affective interaction with a companion robot for hospitalized children: a linguistically based model for emotion detection. In 5th Language and Technology Conference (LTC'2011).
  14. Lee, K. M. and Nass, C. (2003). Designing social presence of social actors in human computer interaction. In Proceedings of the conference on Human factors in computing systems - CHI 7803, number 5, page 289, New York, New York, USA. ACM Press.
  15. Lee, M., Kim, K., Rho, H., and Kim, S. J. (2014). Empa talk. In CHI EA 7814, pages 1897-1902, New York, New York, USA. ACM Press.
  16. Lisetti, C. L. t. and Nasoz, F. (2004). Using Noninvasive Wearable Computers to Recognize Human Emotions from Physiological Signals. EURASIP J ADV SIG PR, 2004(11):1672-1687.
  17. MacDorman, K. (2005). Androids as an experimental apparatus: Why is there an uncanny valley and can we exploit it. CogSci-2005 workshop: toward social mechanisms of android science, 3.
  18. Mandryk, R., Inkpen, K., and Calvert, T. (2006). Using psychophysiological techniques to measure user experience with entertainment technologies. Behaviour & Information Technology.
  19. Matthews, G., Campbell, S. E., Falconer, S., Joyner, L. a., Huggins, J., Gilliland, K., Grier, R., and Warm, J. S. (2002). Fundamental dimensions of subjective state in performance settings: Task engagement, distress, and worry. Emotion, 2(4):315-340.
  20. Möller, S., Smeele, P., Boland, H., and Krebber, J. (2007). Evaluating spoken dialogue systems according to defacto standards: A case study. Computer Speech & Language, 21(1):26-53.
  21. Picard, R. W. (1995). Affective computing. Technical Report 321, MIT Media Laboratory.
  22. Prendinger, H., Dohi, H., and Wang, H. (2004). Empathic embodied interfaces: Addressing users' affective state. In Affective Dialogue Systems, pages 53-64.
  23. Reidsma, D., Nijholt, A., Tschacher, W., and Ramseyer, F. (2010). Measuring Multimodal Synchrony for Human-Computer Interaction. In 2010 International Conference on Cyberworlds, pages 67-71. IEEE.
  24. Renard, Y., Lotte, F., Gibert, G., Congedo, M., Maby, E., Delannoy, V., Bertrand, O., and Lécuyer, A. (2010). OpenViBE: An Open-Source Software Platform to Design, Test, and Use Brain-Computer Interfaces in Real and Virtual Environments. Presence: Teleoperators and Virtual Environments, 19(1):35-53.
  25. Slovák, P., Janssen, J., and Fitzpatrick, G. (2012). Understanding heart rate sharing: towards unpacking physiosocial space. CHI 7812, pages 859-868.
  26. Walmink, W., Wilde, D., and Mueller, F. F. (2014). Displaying Heart Rate Data on a Bicycle Helmet to Support Social Exertion Experiences. In TEI 7814.
  27. Winton, W. M., Putnam, L. E., and Krauss, R. M. (1984). Facial and autonomic manifestations of the dimensional structure of emotion. Journal of Experimental Social Psychology, 20(3):195-216.
  28. Wright, P. and McCarthy, J. (2008). Empathy and experience in HCI. CHI 7808.

Paper Citation

in Harvard Style

Frey J. (2015). Heart Rate Monitoring as an Easy Way to Increase Engagement in Human-Agent Interaction . In Proceedings of the 2nd International Conference on Physiological Computing Systems - Volume 1: PhyCS, ISBN 978-989-758-085-7, pages 129-136. DOI: 10.5220/0005226101290136

in Bibtex Style

author={Jérémy Frey},
title={Heart Rate Monitoring as an Easy Way to Increase Engagement in Human-Agent Interaction},
booktitle={Proceedings of the 2nd International Conference on Physiological Computing Systems - Volume 1: PhyCS,},

in EndNote Style

JO - Proceedings of the 2nd International Conference on Physiological Computing Systems - Volume 1: PhyCS,
TI - Heart Rate Monitoring as an Easy Way to Increase Engagement in Human-Agent Interaction
SN - 978-989-758-085-7
AU - Frey J.
PY - 2015
SP - 129
EP - 136
DO - 10.5220/0005226101290136