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Knee Injured Recovery Analysis Using Extreme Learning Machine

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Applied Technologies (ICAT 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1194))

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Abstract

The physiotherapists analyse gait patterns to recognize normal and pathological gait movements. The gait patterns are affected by the characteristics of the individual (gender, age, weight and height) and the walking speed. In this paper, a gait analysis system to evaluate the severity of gait pathology is proposed. The Machine Learning (ML) algorithm can generate reference knee patterns for specific individuals. Gait index are used to compare the patterns generated by the ELM and patterns of the patients who suffered a surgical knee reconstruction. Two gait index are compared: The Gait Variable Score (GVS) and the Global Index (GIndex) developed by the authors. The GIndex classified 7 patients as not recovery, corroborating with the opinion of physiotherapists, while the GVS only classified 2 as not recovered. The proposed gait analysis system using the Extreme Learning Machine (ELM) and the GIndex can be useful tool for physiotherapy team in the gait pathology diagnosis and evaluation of future pathologies.

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Acknowledgments

The Fundação para a Ciência e Tecnologia (FCT) is gratefully acknowledged for funding this work with the grants SFRH/BD/132408/2017 and PTDC/EEI-AUT/5141/2014 (Automatic Adaptation of a Humanoid Robot Gait to Different Floor-Robot Friction Coefficients). The authors also acknowledge the COMPETE 2020 program for the financial support with the PTDC/EEI-AUT/5141/2014.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Superior Institute of Engineering of Coimbra, Coimbra, Portugal

    João P. Ferreira

  2. ISR - Department of Electrical and Computer Engineering, University of Coimbra, Coimbra, Portugal

    João P. Ferreira, Alexandra Vieira, A. Paulo Coimbra, Manuel M. Crisóstomo & César Bouças

  3. CISUC - Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Bernardete Ribeiro & Alexandra Vieira

  4. State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China

    Tao Liu

  5. Rehabilitation Medicine Department, Coimbra University Hospital, Coimbra, Portugal

    João Páscoa Pinheiro

Authors
  1. João P. Ferreira
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  2. Bernardete Ribeiro
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  3. Alexandra Vieira
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  4. A. Paulo Coimbra
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  5. Manuel M. Crisóstomo
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  7. Tao Liu
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  8. João Páscoa Pinheiro
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Corresponding author

Correspondence to João P. Ferreira .

Editor information

Editors and Affiliations

  1. Eindhoven University of Technology, Eindhoven, The Netherlands

    Miguel Botto-Tobar

  2. Universidad Técnica del Norte, Ibarra, Ecuador

    Marcelo Zambrano Vizuete

  3. Universidad Técnica Particular de Loja, Loja, Ecuador

    Pablo Torres-Carrión

  4. Universidad de las Fuerzas Armadas (ESPE), Quito, Ecuador

    Sergio Montes León

  5. Universidad Politécnica Salesiana, Guayaquil, Ecuador

    Guillermo Pizarro Vásquez

  6. International University of Sarajevo, Sarajevo, Bosnia and Herzegovina

    Benjamin Durakovic

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Ferreira, J.P. et al. (2020). Knee Injured Recovery Analysis Using Extreme Learning Machine. In: Botto-Tobar, M., Zambrano Vizuete, M., Torres-Carrión, P., Montes León, S., Pizarro Vásquez, G., Durakovic, B. (eds) Applied Technologies. ICAT 2019. Communications in Computer and Information Science, vol 1194. Springer, Cham. https://doi.org/10.1007/978-3-030-42520-3_6

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  • DOI: https://doi.org/10.1007/978-3-030-42520-3_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-42519-7

  • Online ISBN: 978-3-030-42520-3

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