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A new classification of hemiplegia gait patterns based on bicluster analysis of joint moments

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Purpose: Hemiplegia is a paralysis on one side of the body resulting from disease or injury to the motor centers of the brain that may lead to difficulty in walking and problems in balance. A new methodology for hemiplegia gait patterns classification based on bicluster analysis, which aims to identify a group of patients with similar gait patterns, and verify if spatial-temporal gait parameters are correlated with the Barthel Index, has been proposed. Methods: Eighteen hemiplegia patients were recruited. Measurements included spatial-temporal gait parameters and joint moments. Gait data were measured using a motion tracking system and two force platforms. Bicluster analysis was used to classify the subjects' gait patterns. The relation between Barthel Index and spatial-temporal gait parameters was determined based on the Spearman correlation. Results: A high correlation between spatial-temporal gait parameters and Barthel Index (r > .5, p < .05) was observed. Well-separated biclusters presenting similarity among the lower limb joints during the gait cycles were obtained from the data. Conclusions: Bicluster analysis can be useful for identifying patients with similar gait patterns. The relation between the gait patterns and the underlying impairments would allow clinicians to target rehabilitation strategies at the patient’s individual needs.
Rocznik
Strony
33--40
Opis fizyczny
Bibliogr. 24 poz., tab., wykr.
Twórcy
autor
  • Bialystok University of Technology, Bialystok, Poland
  • Glenrose Rehabilitation Hospital, 10230 111 Ave NW, Edmonton, AB T5G 0B7, Canada
Bibliografia
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  • [2] Alaqtash M, Sarkodie-Gyan T, Yu H, Fuentes O, et al. Automatic classification of pathological gait patterns using ground reaction forces and machine learning algorithms. Conf Proc IEEE Eng Med Biol Soc.; 2011, 453-457.
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  • [5] BENERJEE T.K., MUKHARJEE C.S., SARKHEL A., Stroke in urban population of Calcutta – An epidemiological study, Neuroepidemiology, 2001, 2(3), 201–207.
  • [6] CAROD-ARTAL J., EGIDO J.A. et al., Quality of life among stroke survivors evaluated 1 year after stroke, Stroke, 2000, 31, 2995–3006.
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  • [10] Dettmann M, Linder M, Sepie S. Relationships among walking performance, postural stability and functional assessments of the hemiplegic patient. Am J Phys Med 1987; 66: 77– 90.
  • [11] Dogan A. et al.: The Rehabilitation Results of Hemiplegic Patients, Turk J Med Sci 34 (2004) 385-389.
  • [12] Hill K, Goldie P, Baker P, Greenwood K. Retest reliability of the temporal and distance characteristics of hemiplegic gait using a footswitch System. Arch Phys Med Rehabil 1994; 75: 577–583.
  • [13] Hodgson C (1998) Prevalence and disabilities of community-living seniors who report the effect of stroke. CMAJ 159: S9-S14.
  • [14] Kim CM, Eng JJ. Magnitude and pattern of 3D kinematic and kinetic gait profiles in persons with stroke: relationship to walking speed. Gait Posture. 2004 Oct; 20(2):140-6.
  • [15] Kinsella S, Moran K. Gait pattern categorization of stroke participants with equinus deformity of the foot. Gait & Posture. 2008 Jan;27(1):144-51.
  • [16] Lamontagne A, Malouin F, Richards CL (2001) Locomotor-specific measure of spasticity of plantarflexor muscles after stroke. Arch Phys Med Rehabil 82: 1696-1704.
  • [17] Lauzière S., Betschart M., Aissaoui R., Nadeau S. (2014) Understanding Spatial and Temporal Gait Asymmetries in Individuals Post Stroke, Int J Phys Med Rehabil, 2:3.
  • [18] Mackintosh SF, Hill KD, Dodd KJ, Goldie PA, Culham EG (2006) Balance score and a history of falls in hospital predict recurrent falls in the 6 months following stroke rehabilitation. Arch Phys Med Rehabil 87: 1583-1589.
  • [19] Malone LA, Bastian AJ (2014) Spatial and temporal asymmetries in gait predict split-belt adaptation behavior in stroke. Neurorehabil Neural Repair 28: 230-240.
  • [20] Manca M, Ferraresi G, Cosma M, et al. (2014). Gait patterns in hemiplegic patients with equinus foot deformity. BioMed Research International: 1-8.
  • [21] Mulroy S, Gronley J, Weiss W, Newsam C, Perry J. Use of cluster analysis for gait pattern classification of patients in the early and late recovery phases following stroke. Gait Posture. 2003 Aug;18(1):114-25.
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  • [24] Winter D (1987). The biomechanics of motor control of human gait. University of Waterloo Press.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-0df91e01-92b8-42b7-9e5b-d037384e909f
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