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Automatic recognition of therapy progress among children with autism

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The article presents a research study on recognizing therapy progress among children with autism spectrum disorder. The progress is recognized on the basis of behavioural data gathered via five specially designed tablet games. Over 180 distinct parameters are calculated on the basis of raw data delivered via the game flow and tablet sensors - i.e. touch screen, accelerometer and gyroscope. The results obtained confirm the possibility of recognizing progress in particular areas of development. The recognition accuracy exceeds 80%. Moreover, the study identifies a subset of parameters which appear to be better predictors of therapy progress than others. The proposed method - consisting of data recording, parameter calculation formulas and prediction models - might be implemented in a tool to support both therapists and parents of autistic children. Such a tool might be used to monitor the course of the therapy, modify it and report its results.

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Kategoria:
Publikacja w czasopiśmie
Typ:
artykuł w czasopiśmie wyróżnionym w JCR
Opublikowano w:
Scientific Reports nr 7, strony 1 - 14,
ISSN: 2045-2322
Język:
angielski
Rok wydania:
2017
Opis bibliograficzny:
Kołakowska A., Landowska A., Anzulewicz A., Sobota K.: Automatic recognition of therapy progress among children with autism// Scientific Reports. -Vol. 7, (2017), s.1-14
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1038/s41598-017-14209-y
Bibliografia: test
  1. Jedrzejewska-Szczerska, M., Karpienko, K. & Landowska, A. System supporting behavioral therapy for children with autism. J. Innov. Opt. Heal. Sci 8 (2015). otwiera się w nowej karcie
  2. Landowska, A. & Smiatacz, M. Mobile activity plan applications for behavioral therapy of autistic children. In Machine Interactions 4, Advances in Intelligent Systems and Computing, 15-125, https://doi.org/10.1007/978-3-319-23437-3_9 (Springer International Publishing, 2015). otwiera się w nowej karcie
  3. Cheng, L., Kimberly, G. & Orlich, F. Kidtalk: online therapy for aspergers syndrome. Technical Report MSR-TR-2002-08, Microsoft Research, Redmont, WA (2002). otwiera się w nowej karcie
  4. Scientific REPORTS | 7: 13863 | DOI:10.1038/s41598-017-14209-y otwiera się w nowej karcie
  5. Tanaka, J. W. et al. Using computerized games to teach face recognition skills to children with autism spectrum disorder: The lets face it! Program. J. Child Psychol. Psychiatry 51, 944-952, https://doi.org/10.1111/j.1469-7610.2010.02258.x (2010). otwiera się w nowej karcie
  6. Deriso, D., Susskind, J., Krieger, L. & Bartlett, M. Emotion mirror: a novel intervention for autism based on real-time expression recognition. In Computer Vision-ECCV2012. Workshops and Demonstration, 671-674 (Springer International Publishing, Florence, 2012). otwiera się w nowej karcie
  7. Kaliouby, R. & Robinson, P. The emotional hearing aid: An assistive tool for children with Asperger syndrome. Univers. Access Inf. Soc. 4, 121-134, https://doi.org/10.1007/s10209-005-0119-0 (2005). otwiera się w nowej karcie
  8. Robins, D., Fein, D., Barton, M. & Green, J. The modified-checklist for autism in toddlers (m-chat): An initial investigation in the early detection of autism and pervasive developmental disorders. J. Autism. Dev. Disord. 31, 131-144 (2001). otwiera się w nowej karcie
  9. Saitovitch, A. et al. Studying gaze abnormalities in autism: Which type of stimulus to use? Open J. Psychiatry 3, 32-38, https://doi. org/10.4236/ojpsych.2013.32A006 (2013). otwiera się w nowej karcie
  10. Jones, W. & Klin, A. Attention to eyes is present but in decline in 2-6-month-old infants later diagnosed with autism. Nat. 504, 427-431, https://doi.org/10.1038/nature12715 (2013). otwiera się w nowej karcie
  11. Hashemi, J. et al. Computer vision tools for the noninvasive assessment of autism-related behavioral markers. In IEEE International Conference on Development and Learning and Epigenetic Robotics, https://doi.org/10.1109/devlrn.2012.6400865 (San Diego, 2012). otwiera się w nowej karcie
  12. Torres, E. et al. Autism: the micromovement perspective. Front. Integr. Neurosci. 7, https://doi.org/10.3389/fnint.2013.00032 (2013). otwiera się w nowej karcie
  13. Wall, D. P., Dally, R., Luyster, R., Jung, J.-Y. & DeLuca, T. F. Use of artificial intelligence to shorten the behavioral diagnosis of autism. PloS one 7, https://doi.org/10.1371/journal.pone.0043855 (2012). otwiera się w nowej karcie
  14. Wall, D., Kosmicki, J., Deluca, T., Harstad, E. & Fusaro, V. Use of machine learning to shorten observation-based screening and diagnosis of autism. Transl. psychiatry, https://doi.org/10.1038/tp.2012.10 (2012). otwiera się w nowej karcie
  15. Yampolskiy, R. & Govindaraju, V. Behavioural biometrics: a survey and classification. Int. J. Biom. 1, 81-113, https://doi.org/10.1504/ IJBM.2008.018665 (2008). otwiera się w nowej karcie
  16. Kolakowska, A. User authentication based on keystroke dynamics analysis. In Springer-Verlag (ed.) Computer Recognition Systems 4, vol. 95 of Advances in Intelligent and Soft Computing, 667-675, https://doi.org/10.1007/978-3-642-20320-6_68 (2011). otwiera się w nowej karcie
  17. Hansen, J. H. L. & Hasan, T. Speaker recognition by machines and humans: A tutorial review. IEEE Signal Process. Mag. 32, 74-99, https://doi.org/10.1109/MSP.2015.2462851 (2015). otwiera się w nowej karcie
  18. Nickel, C., Wirtl, T. & Busch, C. Authentication of smartphone users based on the way they walk using k-nn algorithm. In Proceedings of the 2012 Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP '12, 16-20, https://doi.org/10.1109/IIH-MSP.2012.11 (IEEE Computer Society, Washington, DC, USA, 2012). otwiera się w nowej karcie
  19. Kolakowska, A. Recognizing emotions on the basis of keystroke dynamics. In Proc. of the 8th International Conference on Human System Interaction, 667-675, https://doi.org/10.1109/HSI.2015.7170682 (Warsaw, 2015). otwiera się w nowej karcie
  20. Kolakowska, A., Landowska, A., Jarmolkowicz, P., Jarmolkowicz, M. & Sobota, K. Automatic recognition of males and females among web browser users based on behavioural patterns of peripherals usage. Internet Res. 26, 1093-1111, https://doi.org/10.1108/ intr-04-2015-0100 (2016). otwiera się w nowej karcie
  21. Luyster, R. et al. The autism diagnostic observation schedule-toddler module: a new module of a standardized diagnostic measure for autism spectrum disorders. J. Autism. Dev. Disord. 39, 1305-1320, https://doi.org/10.1007/s10803-009-0746-z (2009). otwiera się w nowej karcie
  22. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders (Fifth ed.) (2013). otwiera się w nowej karcie
  23. Taffoni, F. et al. Sensor-based technology in the study of motor skills in infants at risk for ASD. In IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, 1879-1883, https://doi.org/10.1109/BioRob.2012.6290922 (2012). otwiera się w nowej karcie
  24. Trevarthen, C. & Delafield-Butt, J. Autism as a developmental disorder in intentional movement and affective engagement. Front. Integr. Neurosci. 7, https://doi.org/10.3389/fnint.2013.00049 (2013). otwiera się w nowej karcie
  25. Cook, J., Blakemore, S. & Press, C. Atypical basic movement kinematics in autism spectrum conditions. Brain 136, 2816-2824, https://doi.org/10.1093/brain/awt208 (2013). otwiera się w nowej karcie
  26. David, F., Baranek, G., Wiesen, C., Miao, A. F. & Thorpe, D. Coordination of precision grip in 2-6 years-old children with autism spectrum disorders compared to children developing typically and children with developmental disabilities. Front. Integr. Neurosci. 6, https://doi.org/10.3389/fnint.2012.00122 (2012). otwiera się w nowej karcie
  27. Anzulewicz, A., Sobota, K. & Delafield-Butt, J. Toward the autism motor signature: Gesture patterns during smart tablet gameplay identify children with autism. Sci. Reports 6, https://doi.org/10.1038/srep31107 (2016). otwiera się w nowej karcie
  28. Dowd, A., McGinley, J., Taffe, J. & Rinehart, N. Do planning and visual integration difficulties underpin motor dysfunction in autism? A kinematic study of young children with autism. J. Autism. Dev. Disord. 42, 1539-1548, https://doi.org/10.1007/s10803- 011-1385-8 (2012). otwiera się w nowej karcie
  29. von Hofsten, C. Action in development. Dev. Sci. 10, 54-60, https://doi.org/10.1111/j.1467-7687.2007.00564.x (2007). otwiera się w nowej karcie
  30. MacDonald, M., Lord, C. & Ulrich, D. The relationship of motor skills and adaptive behavior skills in young children with autism spectrum disorders. Res. Autism. Spectr. Discord. 7, 1383-1390 (2013). otwiera się w nowej karcie
  31. Wilkins, J. The relationship between social skills and challenging behaviors in children with autism spectrum disorders. Ph.D. thesis (2010).
  32. Adolf, K., Tamis-Lemonda, C. & Karasik, L. Cinderella indeed -a commentary on Iverson's 'Developing language in a developing body: the relationship between motor development and language development' . J. Child. Lang. 37, 269-273, https://doi.org/10.1017/ S030500090999047X (2010). otwiera się w nowej karcie
  33. Iverson, J. Developing language in a developing body: the relationship between motor development and language development. J. Child. Lang. 37, 229-261, https://doi.org/10.1017/S0305000909990432 (2010). otwiera się w nowej karcie
  34. Lord, C. et al. Austism diagnostic observation schedule: A standardized observation of communicative and social behavior. J. Autism. Dev. Disord. 19, 185-212, https://doi.org/10.1007/BF02211841 (1989). otwiera się w nowej karcie
  35. AUTMON. Automated therapy monitoring for children with developmental disorders of autism spectrum. http://autmon.eti. pg.gda.pl/.
  36. Kolakowska, A. et al. Applications for investigating therapy progress of autistic children. In Federated Conference on Computer Science and Information Systems, 1693-1697, https://doi.org/10.15439/2016F507 (Gdansk, 2016). otwiera się w nowej karcie
  37. Kolakowska, A., Landowska, A. & Karpienko, K. Gyroscope-based game revealing progress of children with autism. In Int. Conf. Machine Learning and Soft Computing (Ho Chi Minh, 2017). otwiera się w nowej karcie
  38. Wolpert, D. H. The lack of a priori distinctions between learning algorithms. Neural. Comput. 8, 1341-1390, https://doi.org/10.1162/ neco.1996.8.7.1341 (1996). otwiera się w nowej karcie
  39. Breiman, L. Bagging predictors. Mach. Learn. 24, 123-140, https://doi.org/10.1023/A:1018054314350 (1996). otwiera się w nowej karcie
  40. Breiman, L. Random forests. Mach. Learn. 45, 5-32, https://doi.org/10.1023/A:1010933404324 (2001). otwiera się w nowej karcie
  41. Rodriguez, J. J., Kuncheva, L. I. & Alonso, C. J. Rotation forest: A new classifier ensemble method. IEEE. Trans. Pattern. Anal. Mach. Intell. 28, 1619-1630, https://doi.org/10.1109/TPAMI.2006.211 (2006). otwiera się w nowej karcie
  42. Freund, Y. & Schapire, R. E. A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55, 119-139, https://doi.org/10.1006/jcss.1997.1504 (1997). otwiera się w nowej karcie
  43. Kohavi, R. & Quinlan, R. Decision tree discovery. In Handbook of data mining and knowledge discovery, chap. 16, 267-276 (Oxford University Press, 2002).
  44. Goodfellow, I., Bengio, Y. & Courville, A. Deep Learning, chap. 6 (MIT Press, 2016).
  45. Scientific REPORTS | 7: 13863 | DOI:10.1038/s41598-017-14209-y 44. Friedman, N., Geiger, D. & Goldszmidt, M. Bayesian network classifiers. Mach. Learn. 29, 131-163, https://doi.org/10.1023/A: 1007465528199 (1997). otwiera się w nowej karcie
  46. Hall, M. et al. The WEKA data mining software: an update. SIGKDD Explorations 11, 10-18 (2009). otwiera się w nowej karcie
  47. Witten, I., Frank, E. & M.A., H. Data Mining: Practical Machine Learning Tools and Techniques, chap. 5 (Morgan Kaufmann Publishers Inc., 2011). otwiera się w nowej karcie
  48. Nadeau, C. & Bengio, Y. Inference for the generalization error. Mach. Learn. 52, 239-281, https://doi.org/10.1023/A:1024068626366 (2003). otwiera się w nowej karcie
  49. Fawcett, T. An introduction to ROC analysis. Pattern Recognit. Lett. 27, 861-874, https://doi.org/10.1016/j.patrec.2005.10.010 (2006). otwiera się w nowej karcie
  50. Cook, J. From movement kinematics to social cognition: the case of autism. Philos. Transactions of the Royal Soc. Lond. B: Biol. Sci. 371, https://doi.org/10.1098/rstb.2015.0372 (2016). otwiera się w nowej karcie
  51. Travers, B. et al. Brainstem white matter predicts individual differences in manual motor difficulties and symptom severity in autism. J. Autism. Dev. Disord. 45, 3030-3040, https://doi.org/10.1007/s10803-015-2467-9 (2015). otwiera się w nowej karcie
  52. Gernsbacher, M., Sauer, E., Geye, H., Schweigert, E. & Hill Goldsmith, H. Infant and toddler oral-and manual-motor skills predict later speech fluency in autism. J. Child. Psychol. Psychiatry. 49, 43-50, https://doi.org/10.1111/j.1469-7610.2007.01820.x (2008). otwiera się w nowej karcie
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Politechnika Gdańska

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