Abstract
Many researchers in the HRI and ECA domains try to build robots and agents that exhibit human-like behavior in real-world close encounter situations. One major requirement for comparing such robots and agents is to have an objective quantitative metric for measuring naturalness in various kinds of interactions. Some researchers have already suggested techniques for measuring stress level, awareness etc using physiological signals like GSR and BVP. One problem of available techniques is that they are only tested with extreme situations and cannot according to the analysis provided in this paper distinguish the response of human subjects in natural interaction situations. One other problem of the available techniques is that most of them require calibration and some times ad-hoc adjustment for every subject. This paper explores the usefulness of various kinds of physiological signals and statistics in distinguishing natural and unnatural partner behavior in a close encounter situation. The paper also explores the usefulness of these statistics in various time slots of the interaction. Based on this analysis a regressor was designed to measure naturalness in close encounter situations and was evaluated using human-human and human-robot interactions and shown to achieve 92.5% accuracy in distinguishing natural and unnatural situations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Mohammad, Y., Xu, Y., Matsumura, K., Nishida, T.: The h 3 r explanation corpus:human-human and base human-robot interaction dataset. In: The fourth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2008) (December 2008)
Qazi, Z., Wang, Z., Haq, I.: Human likeness of humanoid robots exploring the uncanny valley. In: International Conference on Emerging Technologies, ICET 2006, pp. 650–656 (2006)
Shi, Y., Choi, E.H.C., Ruiz, N., Chen, F., Taib, R.: Galvanic skin respons (gsr) as an index of cognitive load. In: CHI 2007, April 2007, pp. 2651–2656 (2007)
Mandryk, R.L., Inkpen, K.M.: Physiological indicators for the evaluation of co-located collaborative play. In: CSCW 2004 (November 2004)
Lin, T., Hu, W., Omata, M., Imamiya, A.: Do physiological data relate to traditional usability indexes? In: OZCHI 2005 (November 2005)
Lang, P.J.: The emotion probe: Studies of motivation and attention. American Psychologiest 50(5), 372–385 (1995)
Mower, E., Feil-Seifer, D.J., Mataric, M.J., Narayanan, S.: Investigating implicit cues for user state estimation in human-robot interaction using physiological measurements. In: 16th International Conference on Robot & Human Interactive Communication, August 2007, pp. 1125–1130 (2007)
Bradley, M., Lang, P.J.: Measuring Emotions: Behavior, Feeling and Physiology. In: Cognitive Neurosceince of Emotion. Oxford University Press, New York (2000)
Argyle, M.: Bodily Communication. New Ed edition, Routledge (2001)
Papillo, J.F., Shapiro, D.: The Cardiovascular System. In: Principles of Psychophysiology: Physical, Social, and Inferential Elements. Cambridge University Press, Cambridge (1990)
Rowe, D.W., Sibert, J., Irwin, D.: Heart rate variability: Indicator of user stateas an aid to human-computer interaction. In: Conference on Human Factors in Computing Systems (CHI 1998) (1998)
Kidd, C.D., Breazeal, C.: Human-robot interaction experiments: lessons learned. In: Robot Compaions: hard problems and open challenges in Robot Human Interaction symbosioum of Social Intelligence and Interaciton in Animals Robots and Agents, April 2005, pp. 141–142 (2005)
Rosner, B.: On the detection of many outliers. Technometrics 17(2), 221–227 (1975)
Mohammad, Y., Nishdia, T.: Human adaptation to a miniature robot: Precursors of mutual adaptation. In: IEEE 17th International Symposium on Robot and Human Interactive Communication (IEEE Ro-Man), pp. 124–129 (2008)
Liao, W., Zhang, W., Zhu, Z., Ji, O.: A decision theoretic model for stress recognition and user assisstance. In: AAAI 2005, pp. 529–534 (2005)
Stern, R.M., Ray, W.J., Quigley, K.S.: Psychological Recording, 2nd edn. Oxford University Press, Oxford (2001)
Mohammad, Y., Nishida, T.: Robust singular spectrum transform. In: IEA/AIE (accepted, 2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mohammad, Y., Nishida, T. (2009). Measuring Naturalness during Close Encounters Using Physiological Signal Processing. In: Chien, BC., Hong, TP., Chen, SM., Ali, M. (eds) Next-Generation Applied Intelligence. IEA/AIE 2009. Lecture Notes in Computer Science(), vol 5579. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02568-6_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-02568-6_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02567-9
Online ISBN: 978-3-642-02568-6
eBook Packages: Computer ScienceComputer Science (R0)