Quantifying the social symptoms of autism using motion capture

Ian Budman, Gal Meiri, Michal Ilan, Michal Faroy, Allison Langer, Doron Reboh, Analya Michaelovski, Hagit Flusser, Idan Menashe, Opher Donchin, Ilan Dinstein

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Autism Spectrum Disorder (ASD) is a remarkably heterogeneous condition where individuals exhibit a variety of symptoms at different levels of severity. Quantifying the severity of specific symptoms is difficult, because it either requires long assessments or observations of the ASD individual, or reliance on care-giver questionnaires, which can be subjective. Here we present a new technique for objectively quantifying the severity of several core social ASD symptoms using a motion capture system installed in a clinical exam room. We present several measures of child-clinician interaction, which include the distance between them, the proportion of time that the child approached or avoided the clinician, and the direction that the child faced in relation to the clinician. Together, these measures explained ~30% of the variance in ADOS scores, when using only ~5 minute segments of “free play” from the recorded ADOS assessments. These results demonstrate the utility of motion capture for aiding researchers and clinicians in the assessment of ASD social symptoms. Further development of this technology and appropriate motion capture measures for use in kindergartens and at home is likely to yield valuable information that will aid in quantifying the initial severity of core ASD symptoms and their change over time.

Original languageEnglish
Article number7712
JournalScientific Reports
Issue number1
StatePublished - 1 Dec 2019

ASJC Scopus subject areas

  • General


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