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Autism Screening Using an Intelligent Toy Car

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Ubiquitous Computing and Ambient Intelligence (UCAmI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10586))

Abstract

The number of cases reported with Autism Spectrum disorder (ASD), as a developmental disorder, has increased sharply in recent decades. Early diagnosis of ASD in children is essential for proper treatment and intervention. The difficulties in early detection of autism encouraged the authors to design a novel intelligent toy car for autism screening. The toy car is equipped with an accelerometer, which records a subject’s usage behavior in terms of accelerations in three dimensions. A set of features, consisting of forty-four movement characteristics, has been extracted which can be used to discriminate between children with autism and normal children. The intelligent toy car has been tested on 25 children with autism and 25 normal children as the test and control groups respectively. Support Vector Machine (SVM) is used to distinguish between the children with autism and other children. The system has 85% correct classification rate, 93% sensitivity and 76% specificity. The results are the same for boys and girls indicating the possible widespread use of this system among all children.

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References

  1. American Psychiatric Association: Task Force on DSMIV. Diagnostic and Statistical Manual of Mental Disorders: DSM-IV-TR. American Psychiatric Publishing, Inc, Washington, DC (2000)

    Google Scholar 

  2. Reznick, J.S., Baranek, G.T., Reavis, S., Watson, L.R., Crais, E.R.: A parent-report instrument for identifying one-year-olds at risk for an eventual diagnosis of autism: the first year inventory. J. Autism Dev. Disord. 37, 1691–1710 (2007)

    Article  Google Scholar 

  3. Baron-Cohen, S.: Autism and Asperger Syndrome. Oxford University Press, Oxford (2008)

    Google Scholar 

  4. Bryson, S.E., Rogers, S.J., Fombonne, E.: Autism spectrum disorders: early detection, intervention, education, and psychopharmacological management (2003). http://www.ncbi.nlm.nih.gov/pubmed/14574826

  5. Ospina, M.B., Seida, J.K., Clark, B., Karkhaneh, M., Hartling, L., Tjosvold, L., Vandermeer, B., Smith, V.: Behavioural and developmental interventions for autism spectrum disorder: a clinical systematic review (2008). http://www.ncbi.nlm.nih.gov/pubmed/19015734

  6. Levy, S.E., Mandell, D.S., Schultz, R.T.: Autism. Lancet 374, 1627–1638 (2009)

    Article  Google Scholar 

  7. Dawson, G., Osterling, J.: The Effectiveness of Early Intervention. Paul H. Brookes, Baltimore (1997)

    Google Scholar 

  8. Charman, T., Baird, G.: Practitioner review: diagnosis of autism spectrum disorder in 2- and 3-year-old children (2002). http://www.ncbi.nlm.nih.gov/pubmed/11944873

  9. Shen, Y., Dies, K.A., Holm, I.A., Bridgemohan, C., Sobeih, M.M., Caronna, E.B., Miller, K.J., Frazier, J.A., Silverstein, I., Picker, J., et al.: Clinical genetic testing for patients with autism spectrum disorders. Pediatrics 125, e727–e735 (2010)

    Article  Google Scholar 

  10. Wang, K., Zhang, H., Ma, D., Bucan, M., Glessner, J.T., Abrahams, B.S., Salyakina, D., Imielinski, M., Bradfield, J.P., Sleiman, P.M.A., et al.: Common genetic variants on 5p14. 1 associate with autism spectrum disorders. Nature 459, 528–533 (2009)

    Article  Google Scholar 

  11. Vorstman, J.A.S., Staal, W.G., Van Daalen, E., Van Engeland, H., Hochstenbach, P.F.R., Franke, L.: Identification of novel autism candidate regions through analysis of reported cytogenetic abnormalities associated with autism. Mol. Psychiatry 11, 18–28 (2005)

    Article  Google Scholar 

  12. Veenstra-VanderWeele, J., Christian, S.L., Cook Jr., E.H.: Autism as a paradigmatic complex genetic disorder. Annu. Rev. Genom. Hum. Genet. 5, 379–405 (2004)

    Article  Google Scholar 

  13. Momeni, N., Bergquist, J., Brudin, L., Behnia, F., Sivberg, B., Joghataei, M.T., Persson, B.L.: A novel blood-based biomarker for detection of autism spectrum disorders. Transl. Psychiatry 2, e91 (2012)

    Article  Google Scholar 

  14. Sheikhani, A., Behnam, H., Mohammadi, M.R., Noroozian, M., Mohammadi, M.: Detection of abnormalities for diagnosing of children with autism disorders using of quantitative electroencephalography analysis. J. Med. Syst. 36, 957–963 (2012)

    Article  Google Scholar 

  15. Kamel, M.I., Alhaddad, M.J., Malibary, H.M., Thabit, K., Dahlwi, F., Alsaggaf, E.A., Hadi, A.A.: EEG based autism diagnosis using regularized Fisher linear discriminant analysis. Int. J. Image Graph. Signal Process. 4, 35 (2012)

    Article  Google Scholar 

  16. Bosl, W., Tierney, A., Tager-flusberg, H., Nelson, C.: EEG complexity as a biomarker for autism spectrum disorder risk. BMC Med. 9, 18 (2011)

    Article  Google Scholar 

  17. Perego, P., Forti, S., Crippa, A., Valli, A., Reni, G.: Reach and throw movement analysis with support vector machines in early diagnosis of autism. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2555–2558 (2009)

    Google Scholar 

  18. Min, C.H., Tewfik, A.H.: Automatic characterization and detection of behavioral patterns using linear predictive coding of accelerometer sensor data. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 220–223 (2010)

    Google Scholar 

  19. Xu, D., Gilkerson, J., Richards, J., Yapanel, U., Gray, S.: Child vocalization composition as discriminant information for automatic autism detection. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2518–2522 (2009)

    Google Scholar 

  20. Kannappan, A., Tamilarasi, A., Papageorgiou, E.I.: Analyzing the performance of fuzzy cognitive maps with non-linear hebbian learning algorithm in predicting autistic disorder. Expert Syst. Appl. 38, 1282–1292 (2011)

    Article  Google Scholar 

  21. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley-Interscience, Hoboken (2012)

    MATH  Google Scholar 

  22. Newschaffer, C.J., Croen, L.A., Daniels, J., Giarelli, E., Grether, J.K., Levy, S.E., Mandell, D.S., Miller, L.A., Pinto-Martin, J., Reaven, J., et al.: The epidemiology of autism spectrum disorders*. Annu. Rev. Public Health 28, 235–258 (2007)

    Article  Google Scholar 

  23. Gilliam, J.E.: Gilliam Asperger’s Disorder Scale (GADS). Pro-Ed, Austin, TX (2001)

    Google Scholar 

  24. Lord, C., Rutter, M., Le Couteur, A.: Disorders, autism diagnostic interview-revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental. J. Autism Dev. Disord. 24, 659–685 (1994)

    Article  Google Scholar 

  25. Wii Official Site. http://www.nintendo.com/wii/what-is-wii

  26. A. Devices: ADXL330 Small, Low Power, 3-Axis\pm2 g iMEMS Accelerometer. Data Sheet), Rev. PrA, pp. 1–8 (2005)

    Google Scholar 

  27. Sautter, R.A., LeBlanc, L.A., Gillett, J.N.: Using free operant preference assessments to select toys for free play between children with autism and siblings. Res. Autism Spectr. Disord. 2, 17–27 (2008)

    Article  Google Scholar 

  28. Burrus, C.S.: Fast Fourier Transforms. Connexions, Rice University, Houston (2008)

    Google Scholar 

  29. Shmaliy, Y.: Continuous-Time Signals. Springer, New York (2006)

    MATH  Google Scholar 

  30. Baron-Cohen, S.: Autism: the empathizing-systemizing (E-S) theory. Ann. N. Y. Acad. Sci. 1156, 68–80 (2009)

    Article  Google Scholar 

  31. Allen, J.B.: Short term spectral analysis, synthesis, and modification by discrete {Fourier} transform. In: IEEE Trans Acoust. Speech, Signal Process. ASSP-25, pp. 235–238 (1977)

    Google Scholar 

  32. Theodoridis, S.: Pattern Recognition, 2nd edn. Academic Press an imprint of Elsevier Science (2003). ISBN:0-12-685875-6

    Google Scholar 

  33. Kohavi, R., et al.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: International Joint Conference on Artificial Intelligence, pp. 1137–1145 (1995)

    Google Scholar 

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Acknowledgments

This research is partially funded by Cognitive Sciences and Technologies Council (COGC) of Iran. The authors would like to thank the Center for the Treatment of Autistic Disorders (CTAD) and Vista kindergarten for their support to perform experiments.

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Authors and Affiliations

  1. School of ECE, University of Tehran, North Karegar Street, Tehran, Iran

    Hadi Moradi, Rozhina Ghanavi & Babak Nadjar Aarabi

  2. Intelligent Systems Research Institute, SKKU, Suwon, South Korea

    Hadi Moradi

  3. Shahid Beheshti University, Tehran, Iran

    Hamid-Reaza Pouretemad

  4. Computer Science Department, Virginia Tech, Blacksburg, VA, USA

    Sorour E. Amiri

Authors
  1. Hadi Moradi
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  2. Sorour E. Amiri
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  3. Rozhina Ghanavi
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  4. Babak Nadjar Aarabi
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  5. Hamid-Reaza Pouretemad
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Correspondence to Hadi Moradi .

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Editors and Affiliations

  1. University of Santiago Chile, Santiago, Chile

    Sergio F. Ochoa

  2. Villanova University, Villanova, Pennsylvania, USA

    Pritpal Singh

  3. University of Castilla La Mancha, Ciudad Real, Spain

    José Bravo

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Moradi, H., Amiri, S.E., Ghanavi, R., Aarabi, B.N., Pouretemad, HR. (2017). Autism Screening Using an Intelligent Toy Car. In: Ochoa, S., Singh, P., Bravo, J. (eds) Ubiquitous Computing and Ambient Intelligence. UCAmI 2017. Lecture Notes in Computer Science(), vol 10586. Springer, Cham. https://doi.org/10.1007/978-3-319-67585-5_79

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  • DOI: https://doi.org/10.1007/978-3-319-67585-5_79

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67584-8

  • Online ISBN: 978-3-319-67585-5

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