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2017 | Vol. 13, no. 1 | 37--42
Tytuł artykułu

Computational gait analysis using fuzzy logic for everyday clinical purposes - preliminary findings

Wybrane pełne teksty z tego czasopisma
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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.
Wydawca

Rocznik
Strony
37--42
Opis fizyczny
Bibliogr. 15 poz., rys.
Twórcy
  • Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Department of Physiotherapy, Jagiellońska 13-15, 86-067 Bydgoszcz, Poland, e.mikolajewska@wp.pl
  • 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
  • Institute of Mechanics and Applied Computer Science, Kazimierz Wielki University in Bydgoszcz, Kopernika 1, 85-064 Bydgoszcz, Poland
  • Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University in Toruń, Neurocognitive Laboratory, Wileńska 5, 87-100 Toruń, Poland
Bibliografia
  • 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.
  • 2. Jahn K, Heinze C, Selge C, Heßelbarth K, Schniepp R. Gait disorders in geriatric patients. Classification and therapy. Nervenarzt 2015;86:431–9.
  • 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.
  • 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.
  • 5. Senanayake CM, Senanayake SM. Computational intelligent gait-phase detection system to identify pathological gait. IEEE Trans Inf Technol Biomed 2010;14:1173–9.
  • 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.
  • 7. Simonsen EB. Contributions to the understanding of gait control. Dan Med J 2014;61:B4823.
  • 8. Simon SR. Quantification of human motion: gait analysis-benefits and limitations to its application to clinical problems. J Biomech 2004;37:1869–80.
  • 9. Lord SE, Halligan PW, Wade DT. Visual gait analysis: the development of a clinical assessment and scale. Clin Rehabil 1998;12:107–19.
  • 10. Tao W, Liu T, Zheng R, Feng H. Gait analysis using wearable sensors. Sensors (Basel) 2012;12:2255–83.
  • 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.
  • 12. Mikołajewska E. The value of the NDT-Bobath method in post-stroke gait training. Adv Clin Exp Med 2013;22:261–72.
  • 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.
  • 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.
  • 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.
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017)
Typ dokumentu
Bibliografia
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Identyfikator YADDA
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