Pervasive and Mobile Computing 8 (2012) 103–114 Contents lists available at SciVerse ScienceDirect Pervasive and Mobile Computing journal homepage: www.elsevier.com/locate/pmc Fast track article Detecting stereotypical motor movements in the classroom using accelerometry and pattern recognition algorithms Fahd Albinali a,* , Matthew S. Goodwin a,b , Stephen Intille a,c a Massachusetts Institute of Technology, Cambridge, MA 02139, USA b The Groden Center, Inc., Providence, RI 02906, USA c Northeastern University, Boston, MA 02115, USA article info Article history: Received 16 January 2010 Received in revised form 13 April 2011 Accepted 14 April 2011 Available online 21 April 2011 Keywords: Autism Accelerometers Activity recognition Pattern recognition abstract Individuals with Autism Spectrum Disorders (ASD) frequently engage in stereotyped and repetitive motor movements. Automatically detecting these movements using comfortable, miniature wireless sensors could advance autism research and enable new intervention tools for the classroom that help children and their caregivers monitor, understand, and cope with this potentially problematic class of behavior. We present activity recognition results for stereotypical hand flapping and body rocking using accelerometer data collected wirelessly from six children with ASD repeatedly observed by experts in real classroom settings. An overall recognition accuracy of 88.6% (TP: 0.85; FP: 0.08) was achieved using three sensors. We also present pilot work in which non- experts use software on mobile phones to annotate stereotypical motor movements for classifier training. Preliminary results indicate that non-expert annotations for training can be as effective as expert annotations. Challenges encountered when applying machine learning to this domain, as well as implications for the development of real-time classroom interventions and research tools are discussed. © 2011 Elsevier B.V. All rights reserved. 1. Introduction Health researchers in many disciplines lack effective tools for unobtrusively acquiring information about peoples’ behavior in natural settings. Ubiquitous computing systems that detect certain behaviors might create new opportunities to improve scientific understanding of the interaction between context, behavior, and health. The goal of the current work is to use ubiquitous monitoring tools for the automated detection of stereotypical motor movements observed in persons with Autism Spectrum Disorders. Autism Spectrum Disorders (ASD) affect as many as 1 in 110 children [1] and are characterized by deficits in socialization and communication, including stereotypical behavior [2]. Stereotyped behaviors are generally defined as repetitive interests and/or motor or vocal sequences that appear to the observer to be invariant in form and without any obvious eliciting stimulus or adaptive function [3]. The current work focuses on stereotypical motor movements. Several stereotypical motor movements have been identified [4], the most prevalent among them being body- rocking, mouthing, and complex hand and finger movements [5]. The majority of research in ASD focuses on social and communication deficits, rather than on restricted and repetitive behavior [4]. A lack of research in stereotypical movements is a potential problem given the high prevalence of stereotypical motor movements reported in individuals with ASD (e.g., [6]). * Corresponding author. E-mail address: albinali@mit.edu (F. Albinali). 1574-1192/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.pmcj.2011.04.006