Toggle Main Menu Toggle Search

ePrints

Automatic assessment of problem behavior in individuals with developmental disabilities

Lookup NU author(s): Dr Thomas Ploetz, Nils Hammerla

Downloads

Full text for this publication is not currently held within this repository. Alternative links are provided below where available.


Abstract

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.


Publication metadata

Author(s): Ploetz T, Hammerla NY, Rozga A, Reavis A, Call N, Abowd GD

Publication type: Conference Proceedings (inc. Abstract)

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


Actions

Link to this publication


Share