Human-robot upper body gesture imitation analysis for autism spectrum disorders

Isura Ranatunga, Monica Beltran, Nahum A. Torres, Nicoleta Bugnariu, Rita M. Patterson, Carolyn Garver, Dan O. Popa

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

16 Scopus citations


In this paper we combine robot control and data analysis techniques into a system aimed at early detection and treatment of autism. A humanoid robot - Zeno is used to perform interactive upper body gestures which the human subject can imitate or initiate. The result of interaction is recorded using a motion capture system, and the similarity of gestures performed by human and robot is measured using the Dynamic Time Warping algorithm. This measurement is proposed as a quantitative similarity measure to objectively analyze the quality of the imitation interaction between the human and the robot. In turn, the clinical hypothesis is that this will serve as a consistent quantitative measurement, and can be used to obtain information about the condition and possible improvement of children with autism spectrum disorders. Experimental results with a small set of child subjects are presented to illustrate our approach.

Original languageEnglish
Title of host publicationSocial Robotics - 5th International Conference, ICSR 2013, Proceedings
Number of pages11
StatePublished - 1 Dec 2013
Event5th International Conference on Social Robotics, ICSR 2013 - Bristol, United Kingdom
Duration: 27 Oct 201329 Oct 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8239 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other5th International Conference on Social Robotics, ICSR 2013
Country/TerritoryUnited Kingdom


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