Daniel Felix Ritchie School of Engineering and Computer Science, Electrical and Computer Engineering
Social robotics, Autism, Music therapy, Turn-taking, Motor control, Emotion classification
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.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
This article was originally published as:
Feng, H., Mahoor, M. H., & Dino, F. (2022). A music-therapy robotic platform for children with autism: A pilot study. Frontiers in Robotics and AI, 9, 855819. doi: 10.3389/frobt.2022.855819
The correction was originally published as:
Feng, H., Mahoor, M. H., & Dino, F. (2022). Corrigendum: A music-therapy robotic platform for children with autism: A pilot study. Frontiers in Robotics and AI, 9, 965369. doi: 10.3389/frobt.2022.965369
Feng, Huanghao; Mahoor, Mohammad H.; and Dino, Francesca, "A Music-Therapy Robotic Platform for Children with Autism: A Pilot Study" (2022). Electrical and Computer Engineering: Faculty Scholarship. 54.