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Bio-Algorithms and Med-Systems

Editor-in-Chief: Roterman-Konieczna , Irena


CiteScore 2018: 0.29

SCImago Journal Rank (SJR) 2018: 0.129
Source Normalized Impact per Paper (SNIP) 2018: 0.324

ICV 2018: 120.80

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1896-530X
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Computational gait analysis using fuzzy logic for everyday clinical purposes – preliminary findings

Emilia Mikołajewska
  • Corresponding author
  • Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Department of Physiotherapy, Jagiellońska 13-15, 86-067 Bydgoszcz, Poland
  • Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University in Toruń, Neurocognitive Laboratory, Wileńska 5, 87-100 Toruń, Poland
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Piotr Prokopowicz
  • Institute of Mechanics and Applied Computer Science, Kazimierz Wielki University in Bydgoszcz, Kopernika 1, 85-064 Bydgoszcz, Poland
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Dariusz Mikolajewski
  • Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University in Toruń, Neurocognitive Laboratory, Wileńska 5, 87-100 Toruń, Poland
  • Institute of Mechanics and Applied Computer Science, Kazimierz Wielki University in Bydgoszcz, Kopernika 1, 85-064 Bydgoszcz, Poland
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2017-02-03 | DOI: https://doi.org/10.1515/bams-2016-0023

Abstract

Background:

Proper, early, and exact identification of gait impairments and their causes is regarded as a prerequisite for specific therapy and a useful control tool to assess efficacy of rehabilitation. There is a need for simple tools allowing for quickly detecting general tendencies.

Objective:

The aim of this paper is to present the outcomes of traditional and fuzzy-based analysis of the outcomes of post-stroke gait reeducation using the NeuroDevelopmental Treatment-Bobath (NDT-Bobath) method.

Materials and methods:

The research was conducted among 40 adult people: 20 of them after ischemic stroke constituted the study group, and 20 healthy people constituted the reference group. Study group members were treated through 2 weeks (10 therapeutic sessions) using the NDT-Bobath method. Spatio-temporal gait parameters were assessed before and after therapy and compared using novel fuzzy-based assessment tool.

Results:

Achieved results of rehabilitation, observed as changes of gait parameters, were statistically relevant and reflected recovery. One-number outcomes from the proposed fuzzy-based estimator proved moderate to high consistency with the results of the traditional gait assessment.

Conclusions:

Observed statistically significant and favorable changes in the health status of patients, described by gait parameters, were reflected also in outcomes of fuzzy-based analysis. Proposed fuzzy-based measure increases possibility of the clinical gait assessment toward more objective clinical reasoning based on common use of the mHealth solutions.

Keywords: fuzzy-based analysis; gait; neurologic gait disorders; physical therapy modalities; rehabilitation

References

  • 1.

    Beauchet O, Annweiler C, Callisaya ML, De Cock AM, Helbostad JL, Kressig RW, et al. Poor gait performance and prediction of dementia: results from a meta-analysis. J Am Med Dir Assoc 2016;17:482–90.Google Scholar

  • 2.

    Jahn K, Heinze C, Selge C, Heßelbarth K, Schniepp R. Gait disorders in geriatric patients. Classification and therapy. Nervenarzt 2015;86:431–9.Google Scholar

  • 3.

    Rosano C, Rosso AL, Studenski SA. Aging, brain, and mobility: progresses and opportunities. J Gerontol A Biol Sci Med Sci 2014;69:1373–4.Google Scholar

  • 4.

    Shull PB, Jirattigalachote W, Hunt MA, Cutkosky MR, Delp SL. Quantified self and human movement: a review on the clinical impact of wearable sensing and feedback for gait analysis and intervention. Gait Posture 2014;40:11–9.Google Scholar

  • 5.

    Senanayake CM, Senanayake SM. Computational intelligent gait-phase detection system to identify pathological gait. IEEE Trans Inf Technol Biomed 2010;14:1173–9.Google Scholar

  • 6.

    Vimieiro C, Andrada E, Witte H, Pinotti M. A computational model for dynamic analysis of the human gait. Comput Methods Biomech Biomed Eng 2015;18:799–804.Google Scholar

  • 7.

    Simonsen EB. Contributions to the understanding of gait control. Dan Med J 2014;61:B4823.Google Scholar

  • 8.

    Simon SR. Quantification of human motion: gait analysis-benefits and limitations to its application to clinical problems. J Biomech 2004;37:1869–80.Google Scholar

  • 9.

    Lord SE, Halligan PW, Wade DT. Visual gait analysis: the development of a clinical assessment and scale. Clin Rehabil 1998;12:107–19.Google Scholar

  • 10.

    Tao W, Liu T, Zheng R, Feng H. Gait analysis using wearable sensors. Sensors (Basel) 2012;12:2255–83.Google Scholar

  • 11.

    Kim SJ, Lee HJ, Hwang SW, Pyo H, Yang SP, Lim MH, et al. Clinical characteristics of proper robot-assisted gait training group in non-ambulatory subacute stroke patients. Ann Rehabil Med 2016;40:183–9.Google Scholar

  • 12.

    Mikołajewska E. The value of the NDT-Bobath method in post-stroke gait training. Adv Clin Exp Med 2013;22:261–72.Google Scholar

  • 13.

    Mikołajewska E. Associations between results of post-stroke NDT-Bobath rehabilitation in gait parameters, ADL and hand functions. Adv Clin Exp Med 2013;22:731–8.Google Scholar

  • 14.

    Armand S, Watelain E, Roux E, Mercier M, Lepoutre FX. Linking clinical measurements and kinematic gait patterns of toe-walking using fuzzy decision trees. Gait Posture 2007;25:475–84.Google Scholar

  • 15.

    Sagawa Y Jr, Watelain E, De Coulon G, Kaelin A, Gorce P, Armand S. Are clinical measurements linked to the gait deviation index in cerebral palsy patients? Gait Posture 2013;38:276–80.Google Scholar

About the article

Received: 2016-11-02

Accepted: 2016-12-28

Published Online: 2017-02-03

Published in Print: 2017-03-01


Author contributions: The authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

Research funding: None declared.

Employment or leadership: None declared.

Honorarium: None declared.

Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.


Citation Information: Bio-Algorithms and Med-Systems, Volume 13, Issue 1, Pages 37–42, ISSN (Online) 1896-530X, ISSN (Print) 1895-9091, DOI: https://doi.org/10.1515/bams-2016-0023.

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