Skip to main content
Springer Nature Link
Log in

Sensorized Toys to Identify the Early ‘Red Flags’ of Autistic Spectrum Disorders in Preschoolers

  • Conference paper
  • First Online:
Ambient Intelligence – Software and Applications –, 9th International Symposium on Ambient Intelligence (ISAmI2018 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 806))

Included in the following conference series:

Abstract

The observation of the games in children affected by autistic spectrum disorders is an important and widely used diagnostic method, which, at the state of the art, is conducted in a clinic with the direct observation of the specialist. This paper presents a prototype of a sensorized toy that can detect movements and orientation of the toy in real-time so to allow for an indirect observation of the young patients. This will open to new ways of performing such tests in a non-invasive way and in the own environments of the patient. The prototype is based on a low-power platform embedding accelerometers, magnetometer and gyroscope, and on a server that provides different views to the specialist in real-time.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://www.particle.io/.

  2. 2.

    https://www.raspberrypi.org/.

  3. 3.

    http://www.mosca.io/.

References

  1. Baranek, G.T., et al.: Object play in infants with autism: methodological issues in retrospective video analysis. Am. J. Occup. Therapy 59(1), 20–30 (2005)

    Article  Google Scholar 

  2. Ozonoff, S., et al.: Atypical object exploration at 12 months of age is associated with autism in a prospective sample. Autism 12(5), 457–472 (2008)

    Article  Google Scholar 

  3. Gubbi, J., et al.: Internet of Things (IoT): A vision, architectural elements, and future directions. Futur. Gener. Comput. Syst. 29(7), 1645–1660 (2013)

    Article  Google Scholar 

  4. Amoretti, M., et al.: Sensor data fusion for activity monitoring in ambient assisted living environments. LNICST, vol. 24, pp. 206–221 (2010)

    Google Scholar 

  5. Fagerberg, G., et al.: Platforms for AAL applications. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), LNCS, vol. 6446, pp. 177–201 (2010)

    Google Scholar 

  6. Mottron, L., et al.: Lateral glances toward moving stimuli among young children with autism: early regulation of locally oriented perception? Dev. Psychopathol. 19(1), 23–36 (2007)

    Article  Google Scholar 

  7. Bruckner, C.T., Yoder, P.: Restricted object use in young children with autism: definition and construct validity. Autism 11(2), 161–171 (2007)

    Article  Google Scholar 

  8. Wetherby, A.M., et al.: Early indicators of autism spectrum disorders in the second year of life. J. Autism Dev. Disord. 34(5), 473–493 (2004)

    Article  Google Scholar 

  9. Cabibihan, J.J., et al.: Sensing technologies for autism spectrum disorder screening and intervention. Sensors 17(46) (2017)

    Article  Google Scholar 

  10. Goodwin, M.S., et al.: Automated detection of stereotypical motor movements. J. Autism Dev. Disord. 41(6), 770–782 (2011)

    Article  Google Scholar 

  11. Plötz, T., et al.: Automatic assessment of problem behavior in individuals with developmental disabilities. In: Proceedings of the ACM UbiComp, Pittsburg, USA, pp. 391–400 (2012)

    Google Scholar 

  12. Goncalves, N., et al.: Automatic detection of stereotyped hand flapping movements: two different approaches. In: Proceedings of the IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), Paris, France, pp. 392–397 (2012)

    Google Scholar 

  13. Kang, J.Y., et al.: Automated tracking and quantification of autistic behavioral symptoms using microsoft kinect. Stud Health Technol. Inform. 220, 167–170 (2016)

    Google Scholar 

  14. Sebastian, O.H., et al.: Estimation of IMU and MARG orientation using a gradient descent algorithm. In: IEEE International Conference on Rehabilitation Robotics, ETH Zurich (CH) (2011)

    Google Scholar 

  15. Madgwick, S.O.H.: An efficient orientation filter for inertial and inertial/magnetic sensor arrays. Dep. of Mechanical Engineering, University of Bristol, 30 April (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Pisa, Largo Pontecorvo 3, 56127, Pisa, Italy

    Marco Lanini, Mariasole Bondioli, Susanna Pelagatti & Stefano Chessa

  2. IRCCS Fondazione Stella Maris, Calambrone, Italy

    Antonio Narzisi

Authors
  1. Marco Lanini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mariasole Bondioli
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Antonio Narzisi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Susanna Pelagatti
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Stefano Chessa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mariasole Bondioli .

Editor information

Editors and Affiliations

  1. ALGORITMI Centre/Departamento de Informatica, University of Minho, Braga, Portugal

    Paulo Novais

  2. Department of Computer Engineering, Chung-Ang University, South Korea, Korea (Republic of)

    Jason J. Jung

  3. Departamento de Informática y Automática, Facultad de Ciencias, Universidad de Salamanca, Salamanca, Spain

    Gabriel Villarrubia González

  4. Departamento de Sistemas Informáticos, University of Castilla-La Mancha, Albacete, Spain

    Antonio Fernández-Caballero

  5. Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete, Spain

    Elena Navarro

  6. Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete, Spain

    Pascual González

  7. Instituto Politécnico do Porto, Escola Superior de Tecnologia e Gestão, Felgueiras, Portugal

    Davide Carneiro

  8. ESTG, Politécnico do Porto and CRACS & INESC TEC, Porto, Portugal

    António Pinto

  9. Department of Computer Science, Dartmouth College, Hanover, NH, USA

    Andrew T. Campbell

  10. Department of Artificial Intelligence, Technical University of Madrid, Madrid, Spain

    Dalila Durães

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lanini, M., Bondioli, M., Narzisi, A., Pelagatti, S., Chessa, S. (2019). Sensorized Toys to Identify the Early ‘Red Flags’ of Autistic Spectrum Disorders in Preschoolers. In: Novais, P., et al. Ambient Intelligence – Software and Applications –, 9th International Symposium on Ambient Intelligence. ISAmI2018 2018. Advances in Intelligent Systems and Computing, vol 806. Springer, Cham. https://doi.org/10.1007/978-3-030-01746-0_22

Download citation

Publish with us

Policies and ethics