title: Automated detection of stereotypical motor movements.
abstract: To overcome problems with traditional methods for measuring stereotypical motor movements in persons with Autism Spectrum Disorders (ASD), we evaluated the use of wireless three-axis accelerometers and pattern recognition algorithms to automatically detect body rocking and hand flapping in children with ASD. Findings revealed that, on average, pattern recognition algorithms correctly identified approximately 90% of stereotypical motor movements repeatedly observed in both laboratory and classroom settings. Precise and efficient recording of stereotypical motor movements could enable researchers and clinicians to systematically study what functional relations exist between these behaviors and specific antecedents and consequences. These measures could also facilitate efficacy studies of behavioral and pharmacologic interventions intended to replace or decrease the incidence or severity of stereotypical motor movements.
authors.affiliation: Media Laboratory, Massachusetts Institute of Technology, MIT Bldg E14-374 K, 75 Amherst Street, Cambridge, MA 02139, USA. mgoodwin@media.mit.edu
authors.forename: Matthew S
authors.initials: MS
authors.lastname: Goodwin
authors.forename: Stephen S
authors.initials: SS
authors.lastname: Intille
authors.forename: Fahd
authors.initials: F
authors.lastname: Albinali
authors.forename: Wayne F
authors.initials: WF
authors.lastname: Velicer
mesh_terms: D000293:Adolescent; D000465:Algorithms; D002648:Child; D002659:Child Development Disorders, Pervasive / Q000503:physiopathology* / Q000523:psychology*; D006801:Humans; D008297:Male; D009068:Movement; D010363:Pattern Recognition, Automated / Q000379:methods*; D013239:Stereotyped Behavior; D055815:Young Adult