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Automated Detection of Stereotypical Motor Movements

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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.

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Notes

  1. The term stereotypical motor movements will be used for the remainder of this work to distinguish this class of behavior from all other stereotyped behaviors.

  2. See Minnen et al. (2006) and Ward et al. (2006) for a more detailed description of how these performance measures are calculated.

  3. Due to space limitations, only general findings are discussed here. See Albinali et al. (2009) for an extended discussion of these findings at the individual participant level, including how the duration of stereotypical motor movements, the percentage of time engaged in stereotypical motor movements, and the consistency with which a stereotypical motor movement is performed impacts recognition accuracy. Albinali et al. (2009) also reports on additional analyses performed on this dataset, including: (1) Training on laboratory data and testing on classroom data, and vice versa; (2) Training on real-time vs. video records; and (3) Training on data from all participants but one and testing performance on the left out participant.

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Authors and Affiliations

  1. Media Laboratory, Massachusetts Institute of Technology, MIT Bldg E14-374 K, 75 Amherst Street, Cambridge, MA, 02139, USA

    Matthew S. Goodwin, Stephen S. Intille & Fahd Albinali

  2. Cancer Prevention Research Center, University of Rhode Island, Kingston, RI, USA

    Wayne F. Velicer

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  1. Matthew S. Goodwin
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  2. Stephen S. Intille
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  3. Fahd Albinali
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  4. Wayne F. Velicer
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Correspondence to Matthew S. Goodwin.

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Autism Speaks and the Nancy Lurie Marks Family Foundation funded this research. The sensors were developed through NSF grant #0312065. Special thanks also to Meghan Scrimgeour for help with data collection, coding, and analysis.

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Goodwin, M.S., Intille, S.S., Albinali, F. et al. Automated Detection of Stereotypical Motor Movements. J Autism Dev Disord 41, 770–782 (2011). https://doi.org/10.1007/s10803-010-1102-z

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