No abstract

Existing literature regarding social communication outcomes of interventions in autism spectrum disorder (ASD) depends upon human raters, with limited generalizability to real world settings.Terms of use and reuse: academic research for non-commercial purposes, see here for full terms. https://www.springer.com/aamterms-v1

Collaborative Virtual Environments (CVE) have shown potential to be an effective social skill training platform for children with Autism Spectrum Disorders (ASD) to learn and practice collaborative and communication skills through peer interactions. However, most existing CVE systems require that appropriately matched partners be available at the same time to promote interaction, which limits their applicability to some community settings due to scheduling constraints. A second shortcoming of these more naturalistic peer-based designs is the intensive resources required to manually code the unrestricted conversations that occurred during the peer-based interactions. To preserve the benefits of CVE-based platforms and mitigate some of the resource limitations related to peer availability, we developed an Intelligent Collaborative Haptic-Gripper System (INC-Hg). This system provides an intelligent agent partner who can understand, communicate, and haptically interact with the user, without requiring the presence of another human peer. The INC-Hg operates in real time and thus is able to perform collaborative training tasks at any time and at the user's pace. INC-Hg can also record the real-time data regarding spoken language and task performance, thereby greatly reducing the resource burden of communication and interaction performance analysis. A preliminary usability study with 10 participants with ASD (ages 8–12 years) indicated that the system could classify the participant's utterances into five classes with an accuracy of 70.34%, which suggested the potential of INC-Hg to automatically recognize and analyze conversational content. The results also indicated high accuracies of the agent to initiate a conversation (97.56%) and respond to the participants (86.52%), suggesting the capability of the agent to conduct proper conversations with the participants. Compared to the results of human-to-human collaborative tasks, the human-to-agent mode achieved higher average collaborative operation ratio (61% compared to 40%) and comparable average frequencies for Initiations and Responses among the participants with ASD. These results offer preliminary support as well as areas of improvement regarding the agent's ability to respond to participants, work with participants to complete tasks, engage in back-and-forth conversations, and support the potential of the agent to be a useful partner for individuals with ASD completing CVE tasks.

Many children with Autism Spectrum Disorder (ASD) exhibit atypical gaze behaviors related to joint attention, a fundamental social-communication skill. Specifically, children with ASD show differences in the skills of gaze sharing and gaze following. In this work we present a novel virtual reality (VR)-based system, called InViRS, in which children with ASD play games allowing them to practice gaze sharing and gaze following. InViRS has three main design contributions: (i) a closed-loop joint attention paradigm with real-time tracking of the participant’s eye gaze and game performance measures, (ii) an assistive feedback mechanism that provides guidance and hints in real time, and (iii) a controller that adaptively changes the avatar’s gaze prompts according to the performance measures. Results from a pilot study to evaluate the feasibility of InViRS with 9 autistic 1 children and 9 typically developing (TD) children offered preliminary support for the feasibility of successful gameplay as well as positive impacts on the targeted skills of gaze sharing and gaze following.