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ASD: ML Perspective for Individual Performance Evaluation

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Soft Computing Applications (SOFA 2014)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 356))

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Abstract

There are several approaches to interact with ASD patients. Many of them do not relay on vocal abilities because of the wide spectrum of symptoms. But taking data from ML perspective may introduce new sources of information for a better interpretation of patient’s behavior and helps to build new parameters to model individual performance. This paper presents some initial findings in that direction based on the automatic evaluation of real cases as a way to build a parameterized description of patients. Preliminary analysis in patients suggests that the individual performance may be compared with the universe of patients, yet it is possible to elaborate a performance profiling and to model evolution profiles that are present in a predefined universe of patients. The team is currently defining and testing new audio and video protocols to collect additional parameters. From these findings, it is possible to build a parametric reasoning model using MLW.

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Notes

  1. 1.

    In verbal behavior theory there is a discrimination between verbal and vocal activities. The first refers to any expression of the individual (movements, sounds, gesturing, manners, etc.) while the other remains to language expressions (written or not).

  2. 2.

    Windows are mathematical functions used to avoid discontinuities in both ends of a piece of signal. This function results in a signal limited in the time. .

  3. 3.

    It is important to note that many times the exercise with the therapist coexists with the interaction with the background.

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Acknowledgments

Authors thanks IEEE SIGHT for the initiative and initial sponsorship of the CIIS Lab BIOTECH project, and Universities of Mendoza, San Juan, and Favaloro for providing expertise in visual, acoustic signal processing, and mathematical knowledge. Also thank to CIIS Lab and EAndes foundation, for expert knowledge in Machine Learning, MLW, and image processing. Special thanks to CIEL in Spain and Argentina, and TIPNEA for the permanent collaboration with data, and expert knowledge in ASD.

Author information

Authors and Affiliations

  1. CI2S Lab, Pringles 10 2nd FL, C1183ADB, Buenos Aires, Argentina

    D. López De Luise, M. Agüero, D. Hisgen & S. Planes

  2. Centro CIEL, Calle Ecce Homo, 2 Bajo Izqda, 33009, Oviedo, Spain

    M. Fernandez Vuelta

  3. DICYTyV Eng. Dep.—Universidad de Mendoza, Arístides Villanueva 773, M5502ITH, Mendoza, Argentina

    R. Azor & C. Párraga

  4. Gab. de Tecnología Médica Eng. Dep.—UNSJ, Libertador 1109 Oeste, J5400ARL, San Juan, Argentina

    N. López & P. Bustamante

  5. EAndes Foundation, Cnel. Suarez 725, M5602HHO, San Rafael Mendoza, Argentina

    M. Marquez & R. Bielli

  6. Dep. de Ingeniería Traslacional (FICEyN), Universidad Favaloro, Solís 453, C1078AAI, Buenos Aires, Argentina

    R. Fairbain

Authors
  1. D. López De Luise
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  2. M. Fernandez Vuelta
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  3. R. Azor
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  4. M. Agüero
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  5. C. Párraga
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  6. N. López
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  7. P. Bustamante
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  8. M. Marquez
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  9. R. Bielli
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  10. D. Hisgen
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  11. R. Fairbain
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  12. S. Planes
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Corresponding author

Correspondence to D. López De Luise .

Editor information

Editors and Affiliations

  1. Department of Automation and Applied Informatics, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  2. Faculty of Science and Technology, Data Science Institute, Bournemouth University, Poole, United Kingdom

    Lakhmi C. Jain

  3. University of Belgrade, Belgrade, Serbia

    Branko Kovačević

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De Luise, D.L. et al. (2016). ASD: ML Perspective for Individual Performance Evaluation. In: Balas, V., C. Jain, L., Kovačević, B. (eds) Soft Computing Applications. SOFA 2014. Advances in Intelligent Systems and Computing, vol 356. Springer, Cham. https://doi.org/10.1007/978-3-319-18296-4_32

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  • DOI: https://doi.org/10.1007/978-3-319-18296-4_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18295-7

  • Online ISBN: 978-3-319-18296-4

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