Abstract

Socially assistive robots are widely deployed in interventions with children on the autism spectrum, exploiting the benefits of this technology in social behavior intervention plans, while reducing their autistic behavior. Furthermore, innovations in modern technologies such as machine learning enhance these robots with great capabilities. Since the results of this implementation are promising, their total cost makes them unaffordable for some organizations while the needs are growing progressively. In this paper, a low-cost robot for autism interventions is proposed, benefiting from the advantages of machine learning and low-cost hardware. The mechanical design of the robot and the development of machine learning models are presented. The robot was evaluated by a small group of educators for children with ASD. The results of various model implementations, together with the design evaluation of the robot, are encouraging and indicate that this technology would be advantageous for deployment in child–robot interaction scenarios.

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