Abstract
Our aim is to explore raw data quality in the first evaluation of the first fully playable prototype of a social-serious-game-based, multi-modal, interactive software system for screening for high functioning cases of autism spectrum condition at kindergarten age. Data were collected from 10 high functioning children with autism spectrum condition and 10 typically developing children. Mouse and eye-tracking data, and data from automated emotional facial expression recognition were analyzed quantitatively. Results show a sub-optimal level of raw data quality and suggest that it is a bottleneck in developing screening/diagnostic/assessment tools based on multi-mode behavioral data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
World Health Organization: International Classification of Diseases and Disorders (ICD-10). World Health Organization, Geneva (1993)
APA [American Psychiatric Association]: Diagnostic and Statistical Manual of Mental Disorders (DSM-5). American Psychiatric Association, Washington DC (2013)
O’Reilly, M., Karim, K., Lester, J.N.: Should autism be classified as a mental illness/disability? evidence from empirical work. In: O’Reilly, M., Lester, J.N. (eds.) The Palgrave Handbook of Child Mental Health. Discourse and Conversation Studies, pp. 252–271. Palgrave Macmillan, UK (2015)
Eikeseth, S.: Intensive early intervention. In: Matson, J.L., Sturmey, P. (eds.) International Handbook of Autism and Pervasive Developmental Disorders, pp. 321–338. Springer, New York (2011)
Daniels, A.M., Mandell, D.S.: Explaining differences in age at autism spectrum disorder diagnosis: a critical review. Autism 18, 583–597 (2014)
García-Primo, P., Hellendoorn, A., Charman, T., Roeyers, H., Dereu, M., Roge, B., et al.: Screening for autism spectrum disorders: state of the art in Europe. Eur. Child Adolesc. Psychiatry 23, 1005–1021 (2014)
Wall, D.P., Kosmicki, J., Deluca, T.F., Harstad, E., Fusaro, V.A.: Use of machine learning to shorten observation-based screening and diagnosis of autism. Transl. Psychiatry. 2, e100 (2012)
Rynkiewicz, A., Schuller, B., Marchi, E., Piana, S., Camurri, A., Lassalle, A., Baron-Cohen, S.: An investigation of the ‘female camouflage effect’ in autism using a computerized ADOS-2 and a test of sex/gender differences. Mol. Autism 7, 1–8 (2016)
Klein, T.J., Al-Ghasani, T., Al-Ghasani, M., Akbar, A., Tang, E., Al-Farsi, Y.: A mobile application to screen for autism in Arabic-speaking communities in Oman. Lancet Glob. Heal. 3, S15 (2015)
Kobak, K.A., Stone, W.L., Ousley, O.Y., Swanson, A.: Web-based training in early autism screening: results from a pilot study. Telemed. e-Health 17, 640–644 (2011)
Bölte, S., Bartl-Pokorny, K.D., Jonsson, U., Berggren, S., Zhang, D., Kostrzewa, E., et al.: How can clinicians detect and treat autism early? Methodological trends of technology use in research. Acta Paediatr. 105(2), 137–144 (2016)
Dehkordi, P.S., Moradi, H., Mahmoudi, M., Pouretemad, H.R.: The design, development, and deployment of roboparrot for screening autistic children. Int. J. Soc. Robot. 7(4), 513–522 (2015)
Gyori, M., Borsos, Z., Stefanik, K.: Evidence-based development and first usability testing of a social serious game based multi-modal system for early screening for atypical socio-cognitive development. In: Sik-Lányi, C., Hoogerwerf, E.-J., Miesenberger, K. (eds.) Assistive Technology: Building Bridges. Studies in Health Technology and Informatics, vol. 217, pp. 48–54. IOS Press, Amsterdam (2015)
Csákvári, J., Gyori, M.: Applicability of standard eye-tracking technique in people with intellectual disability: methodological conclusions from a series of studies. Stud. Health Technol. Inform. 217, 63–70 (2015)
Nyström, M., Andersson, R., Holmqvist, K., Weijer, J., van de Weijer, J.: The influence of calibration method and eye physiology on eyetracking data quality. Behav. Res. Methods 45, 272–288 (2012)
Dhall, A., Goecke, R., Joshi, J., Sikka, K., Gedeon, T.: Emotion recognition. In: The Wild Challenge 2014: Baseline, Data and Protocol. ICMI 2014 Proceedings of the 16th International Conference Multimodal Interact, pp. 461–466 (2014)
Sodian, B., Frith, U.: Deception and sabotage in autistic, retarded and normal children. J. Child Psychol. Psychiatry 33, 591–605 (1992)
Sasson, N.J., Elison, J.T.: Eye tracking young children with autism. JoVE (J. Vis. Exp.), e3675 (2012)
Acknowledgements
This research was approved by the Research Ethics Committee of the ‘Bárczi Gusztáv’ Faculty of Special Education, ELTE University. There is no conflict of interests. Some elements of the project were funded by a grant within the EIT ICT Labs Hungarian Node (PI: András Lőrincz), and via a TÁMOP grant (4.2.1./B-09/KMR-2010-0003). We thank András Lőrincz for his support in the preparatory phases of the project, and Tibor Gregorics for coordinating software development.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Gyori, M., Borsos, Z., Stefanik, K., Csákvári, J. (2016). Data Quality as a Bottleneck in Developing a Social-Serious-Game-Based Multi-modal System for Early Screening for ‘High Functioning’ Cases of Autism Spectrum Condition. In: Miesenberger, K., Bühler, C., Penaz, P. (eds) Computers Helping People with Special Needs. ICCHP 2016. Lecture Notes in Computer Science(), vol 9759. Springer, Cham. https://doi.org/10.1007/978-3-319-41267-2_51
Download citation
DOI: https://doi.org/10.1007/978-3-319-41267-2_51
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-41266-5
Online ISBN: 978-3-319-41267-2
eBook Packages: Computer ScienceComputer Science (R0)