A Music-Therapy Robotic Platform for Children With Autism: A Pilot Study

Huanghao Feng; Mohammad H Mahoor; Francesca Dino

doi:10.3389/frobt.2022.855819

A Music-Therapy Robotic Platform for Children With Autism: A Pilot Study

Front Robot AI. 2022 May 23:9:855819. doi: 10.3389/frobt.2022.855819. eCollection 2022.

Authors

Huanghao Feng¹, Mohammad H Mahoor², Francesca Dino²

Affiliations

¹ Changshu Institute of Technology, Suzhou, China.
² Computer Vision and Social Robotics Labarotory, Department of Electrical and Computer Engineering, University of Denver, Denver, CO, United States.

Abstract

Children with Autism Spectrum Disorder (ASD) experience deficits in verbal and nonverbal communication skills including motor control, turn-taking, and emotion recognition. Innovative technology, such as socially assistive robots, has shown to be a viable method for Autism therapy. This paper presents a novel robot-based music-therapy platform for modeling and improving the social responses and behaviors of children with ASD. Our autonomous social interactive system consists of three modules. Module one provides an autonomous initiative positioning system for the robot, NAO, to properly localize and play the instrument (Xylophone) using the robot's arms. Module two allows NAO to play customized songs composed by individuals. Module three provides a real-life music therapy experience to the users. We adopted Short-time Fourier Transform and Levenshtein distance to fulfill the design requirements: 1) "music detection" and 2) "smart scoring and feedback", which allows NAO to understand music and provide additional practice and oral feedback to the users as applicable. We designed and implemented six Human-Robot-Interaction (HRI) sessions including four intervention sessions. Nine children with ASD and seven Typically Developing participated in a total of fifty HRI experimental sessions. Using our platform, we collected and analyzed data on social behavioral changes and emotion recognition using Electrodermal Activity (EDA) signals. The results of our experiments demonstrate most of the participants were able to complete motor control tasks with 70% accuracy. Six out of the nine ASD participants showed stable turn-taking behavior when playing music. The results of automated emotion classification using Support Vector Machines illustrates that emotional arousal in the ASD group can be detected and well recognized via EDA bio-signals. In summary, the results of our data analyses, including emotion classification using EDA signals, indicate that the proposed robot-music based therapy platform is an attractive and promising assistive tool to facilitate the improvement of fine motor control and turn-taking skills in children with ASD.

Keywords: autism; emotion classification; motor control; music therapy; social robotics; turn-taking.