Abstract
Socially assistive robots (SARs) are being utilized for delivering a variety of healthcare services to patients. The design of these human-robot interactions (HRIs) for healthcare applications have primarily focused on the interaction flow and verbal behaviors of a SAR. To date, there has been minimal focus on investigating how SAR nonverbal behaviors should be designed according to the context of the SAR’s communication goals during a HRI. In this paper, we present a methodology to investigate nonverbal behavior during specific human-human healthcare interactions so that they can be applied to a SAR. We apply this methodology to study the context-dependent vocal nonverbal behaviors of therapists during discrete trial training (DTT) therapies delivered to children with autism. We chose DTT because it is a therapy commonly being delivered by SARs and modeled after human-human interactions. Results from our study led to the following recommendations for the design of the vocal nonverbal behavior of SARs during a DTT therapy: 1) the consequential error correction should have a lower pitch and intensity than the discriminative stimulus but maintain a similar speaking rate; and 2) the consequential reinforcement should have a higher pitch and intensity than the discriminative stimulus but a slower speaking rate.
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
Similar content being viewed by others
References
Papadopoulos, I., et al.: Enablers and barriers to the implementation of socially assistive humanoid robots in health and social care: a systematic review. BMJ Open 10(1), 1–13 (2020)
Rabbitt, S.M., Kazdin, A.E., Scassellati, B.: Integrating socially assistive robotics into mental healthcare interventions: applications and recommendations for expanded use. Clin. Psychol. Rev. 35, 35–46 (2015)
Martinez-Martin, E., Cazorla, M.: A socially assistive robot for elderly exercise promotion. IEEE Access 7, 75515–75529 (2019)
Blanch-Hartigan, D., et al.: Measuring nonverbal behavior in clinical interactions: a pragmatic guide. Patient Educ. Couns. 101, 2209–2218 (2018)
Brown, A.B., Elder, J.H.: Communication in autism spectrum disorder: a guide for pediatric nurses. Pediatr. Nurs. 40(5), 219–225 (2014)
Ambady, N., et al.: Physical therapists’ nonverbal communication predicts geriatric patients’ health outcomes. Psychol. Aging 17(3), 443–452 (2002)
Gable, R.A., et al.: Back to basics: rules, praise, ignoring, and reprimands revisited. Interv. Sch. Clin. 44(4), 195–205 (2009)
Begum, M., et al.: Measuring the efficacy of robots in autism therapy: how informative are standard HRI metrics. In: ACM/IEEE International Conference on Human-Robot Interaction, pp. 335–342 (2015)
Feng, Y., et al.: A control architecture of robot-assisted intervention for children with autism spectrum disorders. J. Robot. pp. 1–12 (2018)
Salvador, M., Marsh, A.S., Gutierrez, A., Mahoor, M.H.: Development of an ABA autism intervention delivered by a humanoid robot. In: Agah, A., C, J.-J., H, Ayanna M., S, Miguel A., He, Hongsheng (eds.) ICSR 2016. LNCS (LNAI), vol. 9979, pp. 551–560. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47437-3_54
Centers for Disease Control and Prevention (2020) Data & Statistics on Autism Spectrum Disorder. https://www.cdc.gov/. Accessed Jun 2020
Hurt, A.A., et al.: Personality traits associated with occupational “burnout” in ABA therapists. J. Appl. Res. Intellect. Disabil. 26(4), 299–308 (2013)
Louie. W.-Y. G., Korneder, J.A., Abbas, I.: A pilot study for a robot-mediated listening comprehension intervention for children with ASD. In: IEEE International Symposium on Robot and Human Interactive Communication, pp. 1–4 (2020)
Google Cloud (2020) Cloud Text-to-Speech - Speech Synthesis. https://cloud.google.com/. Accessed Jun 2020
Burroughs, N.F.: A reinvestigation of the relationship of teacher nonverbal immediacy and student compliance-resistance with learning. Commun. Educ. 56(4), 453–475 (2007)
Gorawara-Bhat, R., Dethmers, D.L., Cook, M.A.: Physician eye contact and elder patient perceptions of understanding and adherence. Patient Educ. Couns. 92, 375–380 (2013)
Cevasco, A.M.: Effects of the therapist’s nonverbal behavior on participation and affect of individuals with Alzheimer’s disease during group music therapy sessions. J. Music Ther. 47(3), 282–299 (2010)
Yarczower, M., Kilbride, J.E., Beck, A.T.: Changes in nonverbal behavior of therapists and depressed patients during cognitive therapy. Psychol. Rep. 69(3), 915–919 (1991)
Mutlu, B., et al.: Conversational gaze mechanisms for humanlike robots. ACM Trans. Interact. Intell. Syst. 1(2), 1–33 (2012)
Yoon, Y., et al.: Robots learn social skills: end-to-end learning of co-speech gesture generation for humanoid robots. In: 19th IEEE International Conference in Robotics and Automation, pp. 4303–4309 (2019)
Feng, W.., et al.: Learn2Smile: learning non-verbal interaction through observation. In: IEEE International Conference on Intelligent Robots and Systems, pp. 4131–4138 (2017)
Randall, A., et al.: Nonverbal behaviour as communication. In: Owen, H. (ed.) The Handbook of Communication Skills, 73–119. Routledge, New York (2016)
Woodall, W.G., Burgoon, J.K.: The effects of nonverbal synchrony on message comprehension and persuasiveness. J. Nonverbal Behav. 5(4), 07–223 (1981)
Eikeseth, S., et al.: Intensive behavioral treatment at school for 4-to-7-year-old children with autism. Behav. Modif. 26, 49–68 (2002)
Lovaas, O.I.: Behavioral treatment and normal educational and intellectual functioning in young autistic children. J. Consult. Clin. Psychol. 55, 3–9 (1987)
National Autism Center: National standards project: Findings and conclusions. NAC, Randolph (2009)
Claude, S.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27(3), 379–423 (1948)
Boersma, P., Weenink, D.: Praat: doing phonetics by computer. http://www.praat.org/, Amsterdam (2020)
Quené, H., Van Den Bergh, H.: On multi-level modeling of data from repeated measures designs: a tutorial. Speech Commun. 43(1–2), 103–121 (2004)
Barr, D.J., et al.: Random effects structure for confirmatory hypothesis testing: keep it maximal. J. Mem. Lang. 68(3), 255–278 (2013)
Vogeley, K., Bente, G.: Artificial humans’: psychology and neuroscience perspectives on embodiment and nonverbal communication. Neural Netw. 23, 1077–1090 (2010)
Sigler, E.A., Aamidor, S.: From positive reinforcement to positive behaviors: an everyday guide for the practitioner. Early Child. Educ. J. 32(4), 249–253 (2005)
Quam, C., Swingley, D.: Development in children’s interpretation of pitch cues to emotions. Child Dev. 83(1), 236–250 (2012)
Fosler-Lussier, E., Morgan, N.: Effects of speaking rate and word frequency on pronunciations in convertional speech. Speech Commun. 29(2–4), 137–158 (1999)
Acknowledgements
This work was supported by the National Science Foundation CRII Award (#1948224). We would like to thank all the participants from the Applied Behavior Analysis Clinic.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Louie, WY.G., Korneder, J., Hijaz, A., Sochanski, M. (2020). Investigating Therapist Vocal Nonverbal Behavior for Applications in Robot-Mediated Therapies for Individuals Diagnosed with Autism. In: Wagner, A.R., et al. Social Robotics. ICSR 2020. Lecture Notes in Computer Science(), vol 12483. Springer, Cham. https://doi.org/10.1007/978-3-030-62056-1_35
Download citation
DOI: https://doi.org/10.1007/978-3-030-62056-1_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-62055-4
Online ISBN: 978-3-030-62056-1
eBook Packages: Computer ScienceComputer Science (R0)