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.
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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.
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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
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DOI: https://doi.org/10.1007/978-3-030-62056-1_35
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