Investigating Therapist Vocal Nonverbal Behavior for Applications in Robot-Mediated Therapies for Individuals Diagnosed with Autism

摘要

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.