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Knee Injured Recovery Analysis Using Extreme Learning Machine

  • João P. FerreiraEmail author
  • Bernardete Ribeiro
  • Alexandra Vieira
  • A. Paulo Coimbra
  • Manuel M. Crisóstomo
  • César Bouças
  • Tao Liu
  • João Páscoa Pinheiro
Conference paper
  • 51 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1194)

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.

Keywords

MLP ELM MSVR Knee pattern GVS 

Notes

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.

Compliance with Ethical Standards

Conflict of Interest. All authors declare that they have no conflict of interest.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Electrical EngineeringSuperior Institute of Engineering of CoimbraCoimbraPortugal
  2. 2.ISR - Department of Electrical and Computer EngineeringUniversity of CoimbraCoimbraPortugal
  3. 3.CISUC - Department of Informatics EngineeringUniversity of CoimbraCoimbraPortugal
  4. 4.State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical EngineeringZhejiang UniversityHangzhouChina
  5. 5.Rehabilitation Medicine DepartmentCoimbra University HospitalCoimbraPortugal

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