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International Conference on Ubiquitous Computing and Ambient Intelligence

UCAmI 2017: Ubiquitous Computing and Ambient Intelligence pp 265–274Cite as

Classification of Pathologies Using a Vision Based Feature Extraction

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

Abstract

A lot of studies linking gait to different pathologies exists. However, few have addressed the automatic classification of such pathologies through computer vision. In this paper, a method to classify different gait pathologies is proposed. Using a smartphone camera, a sagittal view of the subject’s gait is recorded. This record is processed by a computer vision algorithm that extract different gait parameters. These parameters are then used to perform a classification between 5 types of gait: normal, diplegic, hemiplegic, neuropathic and parkinsonian. Using a standard smartphone camera allows to simplify the data capturing step making this method suitable for Ambient Assisted Living. The experiments performed show an accuracy rate of 74% with a hierarchical classifier using Support Vector Machine combining Gait Energy Images and legs angle time series. The accuracy is improved to an 80% by applying data augmentation techniques during test, i.e., obtaining one sample per gait cycle and then combining the results to provide a more robust classification of the entire record.

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Acknowledgements

This research is part of the FRASE MINECO project (TIN2013-47152-C3-2-R) funded by the Ministry of Economy and Competitiveness of Spain.

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Authors and Affiliations

  1. Department of Computing Technology, University of Alicante, Campus San Vicente del Raspeig, Alicante, Spain

    Mario Nieto-Hidalgo & Juan Manuel García-Chamizo

Authors
  1. Mario Nieto-Hidalgo
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  2. Juan Manuel García-Chamizo
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Corresponding author

Correspondence to Mario Nieto-Hidalgo .

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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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Nieto-Hidalgo, M., García-Chamizo, J.M. (2017). Classification of Pathologies Using a Vision Based Feature Extraction. 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_28

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  • DOI: https://doi.org/10.1007/978-3-319-67585-5_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67584-8

  • Online ISBN: 978-3-319-67585-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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