How An Automated Gesture Imitation Game Can Improve Social Interactions With Teenagers With ASD

TL;DR

This paper explores a robot-based imitation game designed to enhance social interaction skills in teenagers with ASD, using machine learning for pose detection and preliminary testing with caregivers.

Contribution

It introduces an adapted interaction scenario and a computational architecture leveraging Openpose for ASD teenagers, with initial validation results.

Findings

Openpose achieved accurate human pose detection in the scenario.

Preliminary user study indicates the game elicits expected responses.

Technical assessment shows promising performance of the system.

Abstract

With the outlook of improving communication and social abilities of people with ASD, we propose to extend the paradigm of robot-based imitation games to ASD teenagers. In this paper, we present an interaction scenario adapted to ASD teenagers, propose a computational architecture using the latest machine learning algorithm Openpose for human pose detection, and present the results of our basic testing of the scenario with human caregivers. These results are preliminary due to the number of session (1) and participants (4). They include a technical assessment of the performance of Openpose, as well as a preliminary user study to confirm our game scenario could elicit the expected response from subjects.