Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Recognizing Mimicked Autistic Self-Stimulatory Behaviors Using HMMs
ISWC '05 Proceedings of the Ninth IEEE International Symposium on Wearable Computers
Proceedings of the 11th international conference on Ubiquitous computing
Markov Models for Pattern Recognition: From Theory to Applications
Markov Models for Pattern Recognition: From Theory to Applications
Feature learning for activity recognition in ubiquitous computing
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Why do they still use paper?: understanding data collection and use in Autism education
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A tutorial on human activity recognition using body-worn inertial sensors
ACM Computing Surveys (CSUR)
TalkBetter: family-driven mobile intervention care for children with language delay
Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing
Computational behaviour modelling for autism diagnosis
Proceedings of the 15th ACM on International conference on multimodal interaction
Automatic assessment of problem behaviour in developmental disabilities
ACM SIGACCESS Accessibility and Computing
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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, usually in a controlled clinical environment. In this paper, we present a technique for using on-body accelerometers to assist in automated classification of problem behavior during such direct observation. Using simulated data of episodes of severe behavior acted out by trained specialists, we demonstrate how machine learning techniques can be used to segment relevant behavioral episodes from a continuous sensor stream and to classify them into distinct categories of severe behavior (aggression, disruption, and self-injury). We further validate our approach by demonstrating it produces no false positives when applied to a publicly accessible dataset of activities of daily living. Finally, we show promising classification results when our sensing and analysis system is applied to data from a real assessment session conducted with a child exhibiting problem behaviors.