research-article

Recognizing stereotypical motor movements in the laboratory and classroom: a case study with children on the autism spectrum

Authors:

Matthew S. Goodwin,

Stephen S. IntilleAuthors Info & Claims

UbiComp '09: Proceedings of the 11th international conference on Ubiquitous computing

Pages 71 - 80

https://doi.org/10.1145/1620545.1620555

Published: 30 September 2009 Publication History

Abstract

Individuals with Autism Spectrum Disorders (ASD) frequently engage in stereotyped and repetitive motor movements. Automatically detecting these movements in real-time using comfortable, miniature wireless sensors could advance autistic research and enable new intervention tools for the classroom that help children and their caregivers monitor and cope with this potentially problematic class of behavior. We present activity recognition results for stereotypical hand flapping and body rocking using data collected from six children with ASD repeatedly observed in both laboratory and classroom settings. In the classroom, an overall recognition accuracy of 88.6% (TP: 0.85; FP: 0.08) was achieved using three sensors. 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.

References

[1]

U.S. CDC, "Prevalence of Autism Spectrum Disorders -- Autism and Developmental Disabilities Monitoring Network," 2002.

[2]

A.P.A., Diagnostic and Statistical Manual on Mental Disorders, 4th Ed., vol. IV-TR. Washington, DC: Amer. Psychiatric Publishing, 2000.

[3]

A. Baumeister and R. Forehand, "Stereotyped acts," in Int'l. Rev. of Res. in Mental Retardation: VI., Ed. New York, 1973, pp. 55--96.

[4]

M.H. Lewis and J.W. Bodfish, "Repetitive behavior disorders in autism," Mental Retardation and Devel. Disabilities Res. Rev., vol. 4, pp. 80--89, 1998.

[5]

S.J. LaGrow and A.C. Repp, "Stereotypic responding: A review of intervention research," Amer. J. of Mental Deficiency, vol. 88, pp. 595--609, 1984.

[6]

G. Berkson and R.K. Davenport Jr, "Stereotyped movements of mental defectives. I. Initial survey," Amer. J. of Mental Deficiency, vol. 66, pp. 849--52, 1962.

[7]

O.I. Lovaas, A. Litrownik, and R. Mann, "Response latencies to auditory stimuli in autistic children engaged in self-stimulatory behavior," Behavior Res. and Therapy, vol. 9, pp. 39--49, 1971.

[8]

R.S. Jones, D. Wint, and N.C. Ellis, "The social effects of stereotyped behaviour," J. of Mental Deficiency Res., vol. 34, pp. 261--8, 1990.

[9]

C.H. Kennedy, "Evolution of stereotypy into self-injury," in Self-Injurious Behavior: Gene-Brain-Behavior Relationships., Washington, DC: Amer. Psych. Assoc., 2002, pp. 133--143.

[10]

R.L. Sprague and K.M.E. Newell, Stereotyped Movements: Brain and Behavior Relationships. Washington, DC: Amer. Psych. Assoc., 1996.

[11]

D.A. Pyles, M.M. Riordan, and J.S. Bailey, "The stereotypy analysis: An instrument for examining environmental variables associated with differential rates of stereotypic behavior," Res. in Developmental Disabilities, vol. 18, pp. 11--38, 1997.

[12]

J.A. Kientz, G.R. Hayes, T.L. Westeyn, T. Starner, and G.D. Abowd, "Pervasive computing and autism: Assisting caregivers of children with special needs," Pervasive Computing, vol. Jan-Mar, pp. 28--35, 2007.

Digital Library

[13]

T. Westeyn, K. Vadas, X. Bian, T. Starner, and G.D. Abowd, "Recognizing mimicked autistic self-stimulatory behaviors using HMMs," in Proc. of ISWC, 2005, pp. 164--169.

Digital Library

[14]

L. Bao and S. S. Intille, "Activity recognition from user-annotated acceleration data," in Proc. of PERVASIVE, 2004, pp. 1--17.

[15]

J. Lester, T. Choudhury, N. Kern, G. Borriello, and B. Hannaford, "A hybrid discriminative/generative approach for modeling human activities," in Proc. of IJCAI, 2005, pp. 766--722.

Digital Library

[16]

P. Lukowicz, J.A. Ward, H. Junker, M. Stager, G. Troster, A. Atrash, and T. Starner, "Recognizing workshop activity using body worn microphone and accelerometers," in Proc.of PerCom, 2004, pp. 18--32.

[17]

J.W. Bodfish, F.J. Symons, D.E. Parker, and M.H. Lewis, "Varieties of repetitive behavior in autism: Comparisons to mental retardation," J. of Autism and Developmental Disorders, vol. 30, pp. 237--243, 2000.

[18]

E. Munguia Tapia, S.S. Intille, L. Lopez, and K. Larson, "The design of a portable kit of wireless sensors for naturalistic data collection," in Proc. of PERVASIVE, 2006, pp. 117--134.

Digital Library

[19]

I.H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. San Francisco, CA: Morgan Kaufmann, 1999.

Digital Library

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Cibrian FMin LArnold V(2024)Towards a Multimodal Approach for Assessing ADHD Hyperactivity BehaviorsProceedings of the International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2024)10.1007/978-3-031-77571-0_1(3-14)Online publication date: 21-Dec-2024
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Zhou LZhang HWang Y(2024)DramaPlaya: A Multi-sensory Interactive Toolkit for the Home-Based Drama Therapy of Children with Developmental DelaysUniversal Access in Human-Computer Interaction10.1007/978-3-031-60881-0_16(250-263)Online publication date: 29-Jun-2024
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Index Terms

Recognizing stereotypical motor movements in the laboratory and classroom: a case study with children on the autism spectrum
1. Applied computing
  1. Law, social and behavioral sciences
    1. Psychology
2. Computing methodologies
  1. Machine learning

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cover image ACM Conferences

UbiComp '09: Proceedings of the 11th international conference on Ubiquitous computing

September 2009

292 pages

ISBN:9781605584317

DOI:10.1145/1620545

General Chair:
Sumi Helal
University of Florida, USA
,
Program Chairs:
Hans Gellersen
Lancaster University, UK
,
Sunny Consolvo
Intel Labs, USA

Copyright © 2009 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: 30 September 2009

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Ubicomp '09: The 11th International Conference on Ubiquitous Computing

September 30 - October 3, 2009

Florida, Orlando, USA

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UbiComp '09 Paper Acceptance Rate 31 of 251 submissions, 12%;

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

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Zhang YLi DLi XZhou XNewman G(2024)The Integration of Geographic Methods and Ecological Momentary Assessment in Public Health Research: A Systematic Review of Methods and ApplicationsSocial Science & Medicine10.1016/j.socscimed.2024.117075(117075)Online publication date: Jun-2024
https://doi.org/10.1016/j.socscimed.2024.117075
Cibrian FMin LArnold V(2024)Towards a Multimodal Approach for Assessing ADHD Hyperactivity BehaviorsProceedings of the International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2024)10.1007/978-3-031-77571-0_1(3-14)Online publication date: 21-Dec-2024
https://doi.org/10.1007/978-3-031-77571-0_1
Zhou LZhang HWang Y(2024)DramaPlaya: A Multi-sensory Interactive Toolkit for the Home-Based Drama Therapy of Children with Developmental DelaysUniversal Access in Human-Computer Interaction10.1007/978-3-031-60881-0_16(250-263)Online publication date: 29-Jun-2024
https://dl.acm.org/doi/10.1007/978-3-031-60881-0_16
Sadek ESeada NGhoniemy S(2023)Neural Network-Based Method for Early Diagnosis of Autism Spectral Disorder Head-Banging Behavior from Recorded VideosInternational Journal of Pattern Recognition and Artificial Intelligence10.1142/S021800142356003737:05Online publication date: 19-Apr-2023
https://doi.org/10.1142/S0218001423560037
Banarjee CTao YFasano RSong CVitale LWang JShyu MPerry LMessinger D(2023)Objective quantification of homophily in children with and without disabilities in naturalistic contextsScientific Reports10.1038/s41598-023-27819-613:1Online publication date: 17-Jan-2023
https://doi.org/10.1038/s41598-023-27819-6
AlQahtani OEfstratopoulou MElhoweris H(2022)The Role of Assistive Technology in Teaching Children With ASD in UAETechnology-Supported Interventions for Students With Special Needs in the 21st Century10.4018/978-1-7998-8874-1.ch003(56-74)Online publication date: 18-Mar-2022
https://doi.org/10.4018/978-1-7998-8874-1.ch003
Bell MRobinson EDay SGilbert THamilton AWard J(2022)Lessons on Collecting Data from Autistic Children using Wrist-worn SensorsProceedings of the 2022 ACM International Symposium on Wearable Computers10.1145/3544794.3558478(6-10)Online publication date: 11-Sep-2022
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Jo EPark SBang HHong YKim YChoi JKim BEpstein DHong H(2022)GeniAuti: Toward Data-Driven Interventions to Challenging Behaviors of Autistic Children through Caregivers' TrackingProceedings of the ACM on Human-Computer Interaction10.1145/35129396:CSCW1(1-27)Online publication date: 7-Apr-2022
https://dl.acm.org/doi/10.1145/3512939
Lv XYu CJin HZhang R(2022)Cost Efficient Sensor Positions Determination For Human Activity RecognitionIEEE Transactions on Sustainable Computing10.1109/TSUSC.2021.31014947:1(125-134)Online publication date: 1-Jan-2022
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Manocha ABhatia M(2022)IoT-Inspired Monitoring Framework for Real-Time Stereotypic Movement AnalysisIEEE Systems Journal10.1109/JSYST.2021.308437016:2(2788-2796)Online publication date: Jun-2022
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