title: Automated Tracking and Quantification of Autistic Behavioral Symptoms Using Microsoft Kinect.
abstract: The prevalence of autism spectrum disorder (ASD) has risen significantly in the last ten years, and today, roughly 1 in 68 children has been diagnosed. One hallmark set of symptoms in this disorder are stereotypical motor movements. These repetitive movements may include spinning, body-rocking, or hand-flapping, amongst others. Despite the growing number of individuals affected by autism, an effective, accurate method of automatically quantifying such movements remains unavailable. This has negative implications for assessing the outcome of ASD intervention and drug studies. Here we present a novel approach to detecting autistic symptoms using the Microsoft Kinect v.2 to objectively and automatically quantify autistic body movements. The Kinect camera was used to film 12 actors performing three separate stereotypical motor movements each. Visual Gesture Builder (VGB) was implemented to analyze the skeletal structures in these recordings using a machine learning approach. In addition, movement detection was hard-coded in Matlab. Manual grading was used to confirm the validity and reliability of VGB and Matlab analysis. We found that both methods were able to detect autistic body movements with high probability. The machine learning approach yielded highest detection rates, supporting its use in automatically quantifying complex autistic behaviors with multi-dimensional input.
authors.affiliation: Department of Neurology, New York University School of Medicine.
authors.forename: Joon Young
authors.initials: JY
authors.lastname: Kang
authors.affiliation: Department of Neurology, New York University School of Medicine.
authors.forename: Ryunhyung
authors.initials: R
authors.lastname: Kim
authors.affiliation: Department of Neurology, New York University School of Medicine.
authors.forename: Hyunsun
authors.initials: H
authors.lastname: Kim
authors.affiliation: Department of Neurology, New York University School of Medicine.
authors.forename: Yeonjune
authors.initials: Y
authors.lastname: Kang
authors.affiliation: Department of Neurology, New York University School of Medicine.
authors.forename: Susan
authors.initials: S
authors.lastname: Hahn
authors.affiliation: Department of Electrical Engineering, New York University.
authors.forename: Zhengrui
authors.initials: Z
authors.lastname: Fu
authors.affiliation: Department of Electrical Engineering, New York University.
authors.forename: Mamoon I
authors.initials: MI
authors.lastname: Khalid
authors.affiliation: Department of Neurology, New York University School of Medicine.
authors.forename: Enja
authors.initials: E
authors.lastname: Schenck
authors.affiliation: Department of Neurology, New York University School of Medicine.
authors.forename: Thomas
authors.initials: T
authors.lastname: Thesen
mesh_terms: D056044:Actigraphy / Q000379:methods*; D001321:Autistic Disorder / Q000150:complications / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D000069550:Machine Learning; D008297:Male; D001523:Mental Disorders / Q000175:diagnosis / Q000209:etiology; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D018910:Video Games; D051598:Whole Body Imaging / Q000379:methods*