Implementing psychophysiological measures is a worthwhile approach for understanding human reaction to robot presence in terms of individual emotional state. This paper reviews the suitability of using psychophysiological assessment in human-robot interaction (HRI) research. A review of most common psychophysiological parameters used in a controlled laboratory setting is provided and advantages and challenges of their utilization in HRI experiments are described. Exemplar studies focused on the implementation of psychophysiological measures for the evaluation of the emotional responses of the participants to the robots’ presence are described. Based on the reviewed literature, the paper also describes the results of our own research experience to make the most of the emerged recommendations. We planned and performed a study aimed at implementing psychophysiological measurements for assessing the human response of two groups of older adults (Healthy vs. Mild Cognitive Impairment subjects) towards a telepresence robot. Finally, the paper provides a summary of lessons learned across the field in using psychophysiological measures in HRI studies.
References
[1]
Breazeal, C. Towards sociable robots. Robot. Auton. Syst. 2003, 42, 167–175, doi:10.1016/S0921-8890(02)00373-1.
[2]
Forlizzi, J.; di Salvo, C. Service Robots in the Domestic Environment: A Study of the Roomba Vacuum in the Home. In Proceedings of the 1st Annual Conference on Human-Robot Interaction, Salt Lake City, UT, USA, March 2006; pp. 258–265.
[3]
Reeves, B.; Nass, C. The Media Equation; CSLI Publications: Cambridge, UK, 1996.
[4]
Sung, J.Y.; Guo, L.; Grinter, R.E.; Christensen, H.I. My roomba is rambo: Intimate home appliances. Ubiquitous Comp. 2007, 4717, 145–162.
[5]
Broekens, J.; Heerink, M.; Rosendal, H. Assistive social robots in elderly care: A review. Gerontechnology 2009, 8, 94–103.
[6]
Feil-Seifer, D.; Mataric, M.J. Defining Socially Assistive Robotics. In Proceedings of the IEEE International Conference on Rehabilitation Robotics, Chicago, IL, USA, 28 June–1 July 2005; pp. 465–468.
[7]
Gross, H.-M.; Schr?ter, C.H.; Müller, S.; Volkhardt, M.; Einhorn, E.; Bley, A.; Langner, T.; Merten, M.; Huijnen, C.; van den Heuvel, H.; et al. Further Progress towards a Home Robot Companion for People with Mild Cognitive Impairment. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (IEEE-SMC 2012), Seoul, Korea, 14–17 October 2012; pp. 637–644.
[8]
Bethel, C.L.; Salomon, K.; Murphy, R.R.; Burke, J.L. Survey of Psychophysiology Measurements Applied to Human-Robot Interaction. In Proceedings of the 16th IEEE International Symposium on Robot & Human Interactive Communication, Jeju Island, Korea, 26–29 August 2007.
[9]
Dautenhahn, K. Socially intelligent robots: Dimensions of human-robot interaction. Philos. Trans. Roy. Soc. Biol. Sci. 2007, 362, 679–704, doi:10.1098/rstb.2006.2004.
[10]
Fong, T.; Nourbakhsh, I.; Dautenhahn, K. A survey of socially interactive robots. Rob. Auton. Syst. 2003, 42, 143–166, doi:10.1016/S0921-8890(02)00372-X.
[11]
Cacioppo, J.T.; Berntson, G.G.; Klein, D.J.; Poehlmann, K.M. The psychophysiology of emotion across the lifespan. Annu. Rev. Gerontol. Geriatr. 1997, 17, 27–74.
[12]
Hagemann, D.; Waldstein, S.; Thayer, J.F. Central and autonomic nervous system integration in emotion. Brain Cogn. 2003, 52, 79–87, doi:10.1016/S0278-2626(03)00011-3.
Swangnetr, M. Analysis of Patient-Robot Interaction Using Statistical and Signal Processing Methods. Ph.D. Thesis, North Carolina State University, Raleigh, NC, USA, 2010.
[15]
Russell, J.A. A circumplex model of affect. J. Pers. Soc. Psychol. 1980, 39, 1151–1178.
[16]
Morris, J.D. SAM: The self-assessment manikin. An efficient cross-cultural measurement of emotional response. J. Advert. Res. 1995, 35, 63–68.
[17]
Cacioppo, J.T.; Tassinary, L.G. Psychophysiology and psychophysiological inference. In Principles of Psychophysiology; Cacioppo, J.T., Tassinary, L.G., Eds.; Cambridge University Press: Cambridge, UK, 1990; pp. 3–33.
[18]
Dirican, A.C.; G?ktürk, M. Psychophysiological measures of human cognitive states applied in human computer interaction. Procedia Comput. Sci. 2011, 3, 1361–1367, doi:10.1016/j.procs.2011.01.016.
[19]
Kuli?, D.; Croft, E. Affective state estimation for human-robot interaction. IEEE Trans. Robot. 2007, 23, 991–1000, doi:10.1109/TRO.2007.904899.
[20]
Gevins, A.; Smith, M.E. Neurophysiological measures of cognitive workload during human-computer interaction. Theor. Issues Ergon. Sci. 2003, 4, 113–131, doi:10.1080/14639220210159717.
[21]
Van Reekum, C.M.; Johnstone, T.; Banse, R.; Etter, A.; Wehrle, T.; Scherer, K.R. Psychophysiological responses to appraisal dimensions in a computer game. Cogn. Emotion 2004, 18, 663–668, doi:10.1080/02699930341000167.
[22]
Mandryk, R.L.; Atkins, M.S. A fuzzy physiological approach for continuously modeling emotion during interaction with play technologies. Int. J. Hum. Comp. Stud. 2007, 65, 329–347, doi:10.1016/j.ijhcs.2006.11.011.
[23]
Wilson, G.M.; Sasse, M.A. Investigating the Impact of Audio Degradations on Users: Subjective vs. Objective Assessment Methods. In Proceedings of OZCHI 2000: Interfacing Reality in the New Millennium, Sydney, Australia, 4–8 December 2000; pp. 135–142.
[24]
Scheirer, J.; Fernandez, R.; Klein, J.; Picard, R. Frustrating the user on purpose: A step toward building an affective computer. Interact. Comp. 2002, 14, 93–118, doi:10.1016/S0953-5438(01)00059-5.
[25]
Barlett, C.P.; Anderson, C.A.; Swing, E.L. Video game effects—Confirmed, suspected, and speculativ. A review of the evidence. Simul. Gam. 2009, 40, 377–403, doi:10.1177/1046878108327539.
[26]
Hebert, S.; Beland, R.; Dionne-Fournelle, O.; Crete, M.; Lupien, S.J. Physiological stress response to video-game playing: The contribution of built-in music. Life Sci. 2005, 76, 2371–2380, doi:10.1016/j.lfs.2004.11.011.
[27]
Ravaja, N.; Saari, T.; Salminen, M.; Laarni, J.; Holopainen, J.; J?rvinen, A. Emotional Response Patterns and Sense of Presence during Video Games: Potential Criterion Variables for Game Design. In Proceedings of the NordiCHI 2004, Tampere, Finland, 26–27 October 2004; pp. 339–347.
[28]
Mandryk, R.L.; Atkins, M.S.; Inkpen, K.M. A Continuous and Objective Evaluation of Emotional Experience with Interactive Play Environments. In Proceedings of the Conference on Human Factors in Computing Systems (CHI 2006), Montreal, QC, Canada, 24–27 April 2006; pp. 1027–1036.
[29]
Kivikangas, M.; Chanel, G.; Cowley, B.; Ekman, I.; Salminen, M.; J?rvel?, S.; Ravaja, N. A review of the use of psychophysiological methods in game research. J. Gam. Virt. Worlds 2011, 3, 181–199.
[30]
Kidd, C.D.; Breazeal, C. Human-Robot Interaction Experiments: Lessons Learned. In Proceedings of the AISB’05 Symposium Robot Companions: Hard Problems and Open Challenges in Robot-Human Interaction, Hertfordshire, UK, 14–15 April 2005; pp. 141–142.
[31]
Steinfeld, A.; Fong, T.; Kaber, D.; Lewis, M.; Scholtz, J.; Schultz, A.; Goodrich, M. Common Metrics for Human-Robot Interaction. In Proceedings of the 1st ACM SIGCHI/SIGART Conference on Human-Robot Interaction, Salt Lake City, UT, USA, 2–3 March 2006.
[32]
Bethel, C.L.; Salomon, K.; Burke, J.L.; Murphy, R.R. Psychophysiological Experimental Design for Use in Human-Robot Interaction Studies. In Proceedings of the 2007 International Symposium on Collaborative Technologies and Systems (CTS 2007), Orlando, FL, USA, 25–26 May 2007.
[33]
Borod, J.C.; Bloom, R.L.; Brickman, A.M.; Nakhutina, L.; Curko, E.A. Emotional processing deficits in individuals with unilateral brain damage. Emotional processing deficits in individuals with unilateral brain damage. Appl. Neuropsychol. Adult 2002, 9, 23–36, doi:10.1207/S15324826AN0901_4.
[34]
Demaree, H.A.; Everhart, D.E.; Youngstrom, E.A.; Harrison, D.W. Brain lateralization of emotional processing: Histrical roots and a future incorporating dominance. Behav. Cognit. Neurosci. Rev. 2005, 4, 3–20, doi:10.1177/1534582305276837.
[35]
Cacioppo, J.T.; Berntson, G.G.; Larsen, J.T.; Poehlmann, K.M.; Ito, T.A. The psychophysiology of emotion. In The Handbook of Emotion, 2nd; Lewis, R., Haviland-Jones, J.M., Eds.; Guilford Press: New York, NY, USA, 2000; pp. 173–191.
[36]
Ruediger, H.; Seibt, R.; Scheuch, K.; Krause, M.; Alam, S.S. Sympathetic and parasympathetic activation in heart rate variability in male hypertensive patients under mental stress. J. Hum. Hypert. 2004, 18, 307–315, doi:10.1038/sj.jhh.1001671.
[37]
Thayer, J.F.; Ahs, F.; Fredrickson, M.; Sollers, J.J.; Wager, T.D. A meta-analysis of heart rate variability and neuroimaging studies: Implications for heart rate variability as a marker of stress and health. Neurosci. Biobehav. Rev. 2012, 36, 747–756, doi:10.1016/j.neubiorev.2011.11.009.
[38]
Joyner, J.M.; Charkoudian, N.; Wallin, B.G. A sympathetic view of the sympathetic nervous system and human blood pressure regulation. Exp. Physiol. 2008, 93, 715–724, doi:10.1113/expphysiol.2007.039545.
Rainville, P.; Bechara, A.; Naqvi, N.; Damasio, A.R. Basic emotions are associated with distinct patterns of cardiorespiratory activity. Int. J. Psychophysiol. 2006, 61, 5–18, doi:10.1016/j.ijpsycho.2005.10.024.
[41]
Thérèse, J.M.O.; van de Boxtel, A.; Westerink, D.M. Respiratory sinus arrhythmia responses to induced emotional states: Effects of RSA indices, emotion induction method, age, and sex. Biol. Psychol. 2012, 91, 128–141, doi:10.1016/j.biopsycho.2012.05.011.
[42]
Park, B. Psychophysiology as a Tool for HCI Research: Promises and Pitfalls. In Proceedings of the 13th International Conference on Human-Comput er Interaction Part I: New Trends, San Diego, CA, USA, 19–24 July 2009; pp. 141–148.
[43]
Wilson, G. Operator functional state assessment for adaptive automation implementation. In Biomonitoring for Physiological and Cognitive Performance during Military Operations; Caldwell, J.A., Wesensten, N.J., Eds.; SPIE: Orlando, FL, USA; pp. 100–104.
[44]
Blechert, J.; Lajtman, M.; Michael, T.; Margraf, J.; Wilhelm, F.H. Identifying anxiety states using broad sampling and advanced processing of peripheral physiological information. Biomed. Sci. Instrum. 2006, 42, 136–141.
[45]
Etzel, J.A.; Johnsen, E.L.; Dickerson, J.A.; Tranel, D.; Adolphs, R. Cardiovascular and respiratory responses during musical mood induction. Int. J. Psychophysiol. 2006, 61, 57–69, doi:10.1016/j.ijpsycho.2005.10.025.
[46]
Khalfa, S.; Roy, M.; Rainville, P.; Bella, S.D.; Peretz, I. Role of tempo entrainment in psychophysiological differentiation of happy and sad music? Int. J. Psychophysiol. 2008, 68, 17–26, doi:10.1016/j.ijpsycho.2007.12.001.
[47]
Murakami, H.; Ohira, H. Influence of attention manipulation on emotion and autonomic responses. Percept. Motor Skills 2007, 105, 299–308.
[48]
Pauls, C.A.; Stemmler, G. Repressive and defensive coping during fear and anger. Emotion 2003, 3, 284–302.
[49]
Stern, R.M.; Ray, W.J.; Quigley, K.S. Psychophysiological Recording, 2nd ed.; Oxford University Press: New York, NY, USA, 2001.
[50]
Benedek, M.; Kaerbach, C. A continuous measure of phasic electrodermal activity. J. Neurosci. Method 2010, 190, 80–91, doi:10.1016/j.jneumeth.2010.04.028.
[51]
Dawson, M.E.; Schell, A.M.; Filion, D.L. The electrodermal system. In Handbook of Psychophysiology, 2nd; Cacippo, J.T., Tassinary, L.G., Berntson, G.G., Eds.; Cambridge University Press: Cambridge, UK, 2000; pp. 200–223.
[52]
Ritz, T.; Steptoe, A.; Wilde, S.D.; Costa, M. Emotions and stress increase respiratory resistance in asthma. Psychos. Med. 2000, 62, 401–412.
[53]
Stemmler, G.; Heldmann, M.; Pauls, C.A.; Scherer, T. Constraints for emotion specificity in fear and anger: The context counts. Psychophysiology 2001, 38, 275–291, doi:10.1111/1469-8986.3820275.
[54]
Haapalainen, E.; Kim, S.J.; Forlizzi, J.F.; Dey, A.K. Psycho-Physiological Measures for Assessing Cognitive Load. In Proceedings of the 12th ACM International Conference on Ubiquitous Computing, Copenhagen, Denmark, 26–29 September 2010; pp. 301–310.
[55]
Shi, Y.; Ruiz, N.; Taib, R.; Choi, E.; Chen, F. Galvanic Skin Response (GSR) as an Index of Cognitive Load. Ext. Abstracts CHI 2007; ACM Press: New York, NY, USA, 2007; pp. 2651–2656.
[56]
Raez, M.B.I.; Hussain, M.S.; Mohd-Yasin, F. Techniques of EMG signal analysis: Detection, processing, classification and application. Biol. Proced. Online 2006, 8, 11–35.
[57]
Neumann, D.L.; Westbury, H.R. The psychophysiological measurement of empathy. In Psychology of Empathy; Scapaletti, D.J., Ed.; Nova Science Publishers Inc.: Hauppauge, NY, USA, 2011; pp. 119–142.
[58]
Dimberg, U.; Thunberg, M.; Grunedal, S. Facial reactions to emotional stimuli: Automatically controlled emotional responses. Cogn. Emotion 2002, 16, 449–471, doi:10.1080/02699930143000356.
[59]
Wied, M.A.; van de Boxtel, A.; Zaalberg, R.; Goudena, P.P.; Matthys, W.C.H.J. Facial EMG responses to dynamic emotional facial expressions in boys with disruptive behavior disorders. J. Psychiatr. Res. 2006, 40, 112–121, doi:10.1016/j.jpsychires.2005.08.003.
[60]
Dimberg, U.; Thunberg, M.; Elmehed, K. Unconscious facial reactions to emotional facial expressions. Psychol. Sci. 2000, 11, 86–89, doi:10.1111/1467-9280.00221.
Coan, J.A.; Allen, J.J.B. Frontal EEG asymmetry as a moderator and mediator of emotion. Biol. Psychol. 2004, 67, 7–49, doi:10.1016/j.biopsycho.2004.03.002.
[63]
Engels, A.S.; Heller, W.; Mohanty, A.; Herrington, J.D.; Banich, M.T.; Webb, A.G.; Miller, G.A. Specificity of regional brain activity in anxiety types during emotion processing. Psychophysiology 2007, 44, 352–363, doi:10.1111/j.1469-8986.2007.00518.x.
[64]
Nitschke, J.B.; Heller, W.; Miller, G.A. Anxiety, Stress, and Cortical Brain Function. In The Neuropsychology of Emotions; Borod, J.C., Ed.; Oxford University Press: New York, NY, USA, 2000; pp. 298–319.
[65]
Chaouachi, M.; Jraidi, I.; Frasson, C. Modeling mental workload using eeg features for intelligent systems. User modeling, adaption and personalization. Lect. Note. Comput. Sci. 2011, 6787, 50–60, doi:10.1007/978-3-642-22362-4_5.
[66]
Wilson, G.F. An analysis of mental workload in pilots during flight using multiple psychophysiological measures. Int. J. Aviat. Psychol. 2002, 12, 3–18, doi:10.1207/S15327108IJAP1201_2.
[67]
Phan, K.L.; Wager, T.D.; Taylor, S.F.; Liberzon, I. Functional brain imaging studies of human emotion. CNS Spectr. 2004, 9, 258–266.
[68]
Saygin, A.P.; Chaminade, T.; Urgen, B.A.; Ishiguro, H. Cognitive neuroscience and robotics: A mutually beneficial joining of forces. In Robotics: Systems and Science; Takayama, L., Ed.; University of Southern California: Los Angeles, CA, USA, 2011.
[69]
Dennett, D. The Intentional Stance; MIT Press: Cambridge, MA, USA, 1987.
[70]
Kim, K.J.; Lipson, H. Towards a Simple Robotic Theory of Mind. In Proceedings of the 9th Workshop on Performance Metrics for Intelligent Systems, PerMIS 2009, Gaithersburg, MD, USA, 21–23 September 2009; pp. 131–138.
[71]
Bonarini, A.; Mainardi, L.; Matteucci, M.; Tognetti, S.; Colombo, R. Stress Recognition in a Robotic Rehabilitation Task. In Proceedings of the Robotic Helpers: User Interaction, Interfaces and Companions in Assistive and Therapy Robotics, Amsterdam, the Netherlands, 12–15 March 2008.
[72]
Kuli?, D.; Croft, E.A. Anxiety Detection during Human-Robot Interaction. In Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Edmonton, AB, Canada, 2–6 August 2005; pp. 616–621.
[73]
Kulic, D.; Croft, E. Estimating Intent for Human-Robot Interaction. In Proceedings of the 11th International Conference on Advanced Robotics, Coimbra, Portugal, 30 June–3 July 2003; pp. 810–815.
[74]
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 Proceedings of the 16th IEEE International Symposium on Robot and Human Interactive Communication RO-MAN 2007, Jeju Island, Korea, 26–29 August 2007; pp. 1125–1130.
[75]
Nasoz, F.; Alvarez, K.; Lisetti, C.L.; Finkelstein, N. Emotion recognition from physiological signals for presence technologies. Int. J. Cogn. Technol. Work 2003, 6, 1–14.
[76]
Picard, R.W.; Vyzas, E.; Healy, J. Toward machine emotional intelligence: Analysis of affective psychological states. IEEE Trans. Pattern Anal. Mach. Intell. 2001, 23, 1175–1191, doi:10.1109/34.954607.
[77]
Rani, P.; Sims, J.; Brackin, R.; Sarkar, N. Online stress detection using psychophysiological signals for implicit human-robot cooperation. Robotica 2002, 20, 672–685.
[78]
Rani, P.; Sarkar, N. A new approach to implicit human-robot interaction using affective cues. In Mobile Robots, towards New Applications; Lazinica, A., Ed.; InTech: New York, NY, USA, 2006; pp. 233–252.
[79]
Rani, P.; Sarkar, N.; Smith, C.A.; Adams, J.A. Affective Communication for Implicit Human-Machine Interaction. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Washington, DC, USA, 5–8 October 2003; Volume 5, pp. 4896–4903.
[80]
Zhai, J.; Barreto, A.B.; Chin, C.; Chao, Li. Realization of Stress Detection Using Psychophysiological Signals for Improvement of Human-Computer Interactions. In Proceedings of the IEEE SoutheastCon 2005, Fort Lauderdale, FL, USA, 8–10 April 2005; pp. 415–420.
[81]
Itoh, K.; Miwa, H.; Nukariya, Y.; Zecca, M.; Takanobu, H.; Roccella, S.; Carrozza, M.C.; Dario, P.; Atsuo, T. Development of a Bioinstrumentation System in the Interaction between a Human and a Robot. In Proceedings of the International Conference of Intelligent Robots and Systems, Beijing, China, 9–15 October 2006; pp. 2620–2625.
[82]
Badesa, F.J.; Morales, R.; Garcia-Aracil, N.; Sabater, J.M.; Perez-Vidal, C.; Fernandez, E. Multimodal interfaces to improve therapeutic outcomes in robot-assisted rehabilitation. IEEE Trans. Syst. Man Cybern. Appl. Rev. 2012, 42, 1152–1158.
[83]
Guerrero, C.R.; Marinero, J.F.; Turiel, J.P.; Farina, P.R. Using Psychophysiological Feedback to Enhance Physical Human Robot Interaction in a Cooperative Scenario. In Proceedings of the 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), Roma, Italy, 24–27 June 2012; pp. 1176–1181.
[84]
Koenig, A.; Novak, D.; Omlin, X.; Pulfer, M.; Perreault, E.; Zimmerli, L.; Mihelj, M.; Riener, R. Real-time closed-loop control of cognitive load in neurological patients during robot-assisted gait training. IEEE Trans. Neural Syst. Rehabil. Eng. 2011, 19, 453–464, doi:10.1109/TNSRE.2011.2160460.
[85]
Liu, C.; Conn, K.; Sarkar, N.; Stone, W. Online affect detection and robot behavior adaptation for intervention of children with autism. IEEE Trans. Robot. 2008, 24, 883–896, doi:10.1109/TRO.2008.2001362.
Ganesh, M. Introduction to Fuzzy Sets and Fuzzy Logic; Prentice Hall of India: New Delhi, India, 2006.
[88]
Dehais, F.; Sisbot, E-A.; Alami, R.; Causse, M. Physiological and subjective evaluation of a human-robot object hand over task. Appl. Ergon. 2011, 42, 785–791, doi:10.1016/j.apergo.2010.12.005.
[89]
Goljar, N.; Javh, M.; Poje, J.; Ocepek, J.; Novak, D.; Ziherl, J.; Olen?ek, A.; Mihelj, M.; Munih, M. Psychophysiological Responses to Robot Training in Different Recovery Phases after Stroke. In Proceedings of the IEEE International Conference on Rehabilitation Robotics (ICORR 2011), Zurich, Switzerland, 29 June–1 July 2011.
[90]
Swangnetr, M.; Kaber, D.B. Emotional state classification in patient-robot interaction using wavelet analysis and statistics-based feature selection. IEEE Trans. Hum. Mach. Syst. 2013, 43, 63–75, doi:10.1109/TSMCA.2012.2210408.
[91]
Zhang, T.; Kaber, D.B.; Zhu, B.; Swangnetr, M.; Mosaly, P.; Hodge, L. Service robot feature design effects on user perceptions and emotional responses. Intell. Serv. Robot. 2010, 3, 73–88, doi:10.1007/s11370-010-0060-9.
[92]
Broadbent, E.; Lee, Y.I.; Stafford, R.Q.; Kuo, Y.H.; MacDonald, B.A. Mental schemas of robots as more human-like are associated with higher blood pressure and negative emotions in a human-robot interaction. Int. J. Soc. Robot 2011, 3, 291–297, doi:10.1007/s12369-011-0096-9.
[93]
Watson, D.; Clark, L.A.; Tellegen, A. Development and validation of brief measures of positive and negative affect: The PANAS scales. J. Pers. Soc. Psychol. 1988, 54, 1063–1070, doi:10.1037/0022-3514.54.6.1063.
[94]
Chaminade, T.; da Fonseca, D.; Rosset, D.; Lutcher, E.; Cheng, G.; Deruelle, C. FMRI Study of Young Adults with Autism Interacting with a Humanoid Robot. In Proceedings of the 21st IEEE International Symposium on Robot and Human Interactive Communication, Paris, France, 9–13 September 2012; pp. 380–385.
[95]
Chaminade, T.; Zecca, M.; Blakemore, S.-J.; Takanishi, A.; Frith, C.D.; Micera, S.; Dario, P.; Rizzolatti, G.; Gallese, V.; Umità, M.A. Brain response to a humanoid robot in areas implicated in the perception of human emotional gestures. PLoS One 2010, 5, doi:10.1371/journal.pone.0011577.
[96]
Miura, N.; Sugiura, M.; Takahashi, M.; Moridaira, T.; Miyamoto, A.; Kuroki, Y.; Kawashima, R. An Advantage of Bipedal Humanoid Robot on the Empathy Generation: A Neuroimaging Study. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, France, 22–26 September 2008; pp. 2465–2470.
[97]
Tiberio, L.; Cesta, A.; Cortellessa, G.; Padua, L.; Pellegrino, A.R. Assessing Affective Response of Older Users to a Telepresence Robot Using a Combination of Psychophysiological Measures. In Proceedings of the IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication, Paris, France, 9–13 September 2012.
[98]
Cesta, A.; Cortellessa, G.; Orlandini, A.; Tiberio, L. Addressing the Long-Term Evaluation of a Telepresence Robot for the Elderly; Joaquim, F., Ana, L.N., Eds.; SciTePress: Rome, Italy, 2012; pp. 652–663.
[99]
Beer, J.; Takayama, L. Mobile Remote Presence Systems for Older Adults: Acceptance, Benefits, and Concerns. In Proceedings of the 6th International Conference on Human-Robot Interaction, Lausanne, Switzerland, 6–9 March 2011; pp. 19–26.
[100]
Sharkey, A.; Sharkey, N. Children, the elderly, and interactive robots. IEEE Robot. Autom. Mag. 2011, 18, 32–38, doi:10.1109/MRA.2010.940151.
[101]
Giraff. Available online: http://www.giraff.org (accessed on 17 May 2013).
[102]
ExCITE (Enabling SoCial Interaction Through Embodiment). Available online: http://www.oru.se/excite (accessed on 17 May 2013).
[103]
Spielberger, C.D.; Gorsuch, R.L.; Lushene, R.E. Manual for the State-Trait Anxiety Inventory; Consulting Psychologists Press: Palo Alto, CA, USA, 1970.
[104]
Davis, F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quart. 1989, 13, 319–340, doi:10.2307/249008.
[105]
Tapus, A.; Matari?, M.J.; Scassellati, S. The grand challenges in socially assistive robotics. IEEE Robot. Autom. Mag. 2007, 14, 35–42.
[106]
Kim, K.H.; Bang, S.W.; Kim, S.R. Emotion recognition system using short-term monitoring of physiological signals. Med. Biol. Eng. Comp. 2004, 42, 419–427, doi:10.1007/BF02344719.
[107]
Kreibig, S.D. Autonomic nervous system activity in emotion: A review. Biol. Psychol. 2010, 84, 394–421.
[108]
Minato, T.; Shimada, M.; Itakura, S.; Lee, K.; Ishiguro, H. Evaluating the human likeness of an android by comparing gaze behaviors elicited by the android and a person. Adv. Robot. 2006, 20, 1147–1163, doi:10.1163/156855306778522505.
[109]
Mori, M. The uncanny valley. Energy 1970, 74, 33–35.
[110]
Czaja, S.; Charness, N.; Fisk, A.; Hertzog, C.; Nair, S.; Rogers, W.; Sharit, J. Factors predicting the use of technology: Findings from the center for research and education on aging and technology enhancement. Psychol. Aging 2006, 21, 333–352, doi:10.1037/0882-7974.21.2.333.
[111]
Novak, D.; Mihelj, M.; Ziherl, J.; Olensek, A.; Munih, M. Psychophysiological measurements in a biocooperative feedback loop for upper extremity rehabilitation. IEEE Trans. Neural Syst. Rehabil. Eng. 2011, 19, 400–410, doi:10.1109/TNSRE.2011.2160357.
Novak, D.; Mihelj, M.; Munih, M. A survey of methods for data fusion and system adaptation using autonomic nervous system responses in physiological computing. Interact. Comput. 2012, 24, 154–172, doi:10.1016/j.intcom.2012.04.003.
[114]
Fisher, R.J. Social desirability bias and the validity of indirect questioning. J. Consum. Res. 1993, 2, 303–315.
[115]
Posner, J.; Russell, J.A.; Peterson, B.S. The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology. Dev. Psychopathol. 2005, 17, 715–734.