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Lookup NU author(s): Dr Thomas Ploetz, Nils Hammerla
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Severe behavior problems of children with developmental disabilities often require intervention by specialists. These specialists rely on direct observation of the behavior, usu- ally in a controlled clinical environment. In this paper, we present a technique for using on-body accelerometers to as- sist in automated classification of problem behavior during such direct observation. Using simulated data of episodes of severe behavior acted out by trained specialists, we demon- strate how machine learning techniques can be used to seg- ment relevant behavioral episodes from a continuous sensor stream and to classify them into distinct categories of se- vere behavior (aggression, disruption, and self-injury). We further validate our approach by demonstrating it produces no false positives when applied to a publically accessible dataset of activities of daily living. Finally, we show promis- ing classification results when our sensing and analysis sys- tem is applied to data from a real assessment session con- ducted with a child exhibiting problem behaviors.
Author(s): Ploetz T, Hammerla NY, Rozga A, Reavis A, Call N, Abowd GD
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: Ubicomp 2012: Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Year of Conference: 2012
Pages: 391-400
Publisher: ACM
URL: http://dx.doi.org/10.1145/2370216.2370276
DOI: 10.1145/2370216.2370276
Library holdings: Search Newcastle University Library for this item
ISBN: 9781450312240
