research-article

Automatic assessment of problem behavior in individuals with developmental disabilities

Authors:

Nils Y. Hammerla,

Gregory D. AbowdAuthors Info & Claims

UbiComp '12: Proceedings of the 2012 ACM Conference on Ubiquitous Computing

Pages 391 - 400

https://doi.org/10.1145/2370216.2370276

Published: 05 September 2012 Publication History

Abstract

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.

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Index Terms

Automatic assessment of problem behavior in individuals with developmental disabilities
1. Applied computing
  1. Law, social and behavioral sciences
2. Human-centered computing
  1. Human computer interaction (HCI)

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cover image ACM Conferences

UbiComp '12: Proceedings of the 2012 ACM Conference on Ubiquitous Computing

September 2012

1268 pages

ISBN:9781450312240

DOI:10.1145/2370216

General Chair:
Anind K. Dey
Carnegie Mellon University
,
Program Chairs:
Hao-Hua Chu
National Taiwan University, Taiwan
,
Gillian Hayes
University of California, Irvine

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 05 September 2012

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September 5 - 8, 2012

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UbiComp '12 Paper Acceptance Rate 58 of 301 submissions, 19%;

Overall Acceptance Rate 764 of 2,912 submissions, 26%

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https://doi.org/10.3390/s24123898
Kim WSeong MDelPreto JMatusik WRus DKim SKostakos VKay JHoang T(2024)Exploring Potential Application Areas of Artificial Intelligence-Infused System for Engagement Recognition: Insights from Special Education ExpertsCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3678376(803-808)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3675094.3678376
Kim WSeong MKim KKim S(2024)Engagnition: A multi-dimensional dataset for engagement recognition of children with autism spectrum disorderScientific Data10.1038/s41597-024-03132-311:1Online publication date: 15-Mar-2024
https://doi.org/10.1038/s41597-024-03132-3
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Ahmad NLeung H(2023)ALAE-TAE-CutMix+: Beyond the State-of-the-Art for Human Activity Recognition Using Wearable Sensors2023 IEEE International Conference on Pervasive Computing and Communications (PerCom)10.1109/PERCOM56429.2023.10099138(222-231)Online publication date: 13-Mar-2023
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Gupta AKaur I(2023)Machine Learning for Autism Spectrum Disorder Detection: A Systematic Survey2023 6th International Conference on Contemporary Computing and Informatics (IC3I)10.1109/IC3I59117.2023.10397940(1305-1309)Online publication date: 14-Sep-2023
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