{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T22:04:03Z","timestamp":1761948243353},"reference-count":15,"publisher":"Index Copernicus","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,3,1]]},"abstract":"Abstract<\/jats:title>\n \n Background:<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Objective:<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Materials and methods:<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Results:<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Conclusions:<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>","DOI":"10.1515\/bams-2016-0023","type":"journal-article","created":{"date-parts":[[2017,2,3]],"date-time":"2017-02-03T09:44:14Z","timestamp":1486115054000},"page":"37-42","source":"Crossref","is-referenced-by-count":30,"title":["Computational gait analysis using fuzzy logic for everyday clinical purposes \u2013 preliminary findings"],"prefix":"10.5604","volume":"13","author":[{"given":"Emilia","family":"Miko\u0142ajewska","sequence":"first","affiliation":[{"name":"Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toru\u0144, Department of Physiotherapy, Jagiello\u0144ska 13-15, 86-067 Bydgoszcz, Poland"},{"name":"Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University in Toru\u0144, Neurocognitive Laboratory, Wile\u0144ska 5, 87-100 Toru\u0144, Poland"}]},{"given":"Piotr","family":"Prokopowicz","sequence":"additional","affiliation":[{"name":"Institute of Mechanics and Applied Computer Science, Kazimierz Wielki University in Bydgoszcz, Kopernika 1, 85-064 Bydgoszcz, Poland"}]},{"given":"Dariusz","family":"Mikolajewski","sequence":"additional","affiliation":[{"name":"Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University in Toru\u0144, Neurocognitive Laboratory, Wile\u0144ska 5, 87-100 Toru\u0144, Poland"},{"name":"Institute of Mechanics and Applied Computer Science, Kazimierz Wielki University in Bydgoszcz, Kopernika 1, 85-064 Bydgoszcz, Poland"}]}],"member":"3689","published-online":{"date-parts":[[2017,2,3]]},"reference":[{"key":"2023010916422369871_j_bams-2016-0023_ref_001_w2aab2b8b1b1b7b1ab2b1b1Aa","doi-asserted-by":"crossref","unstructured":"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\u201390.","DOI":"10.1016\/j.jamda.2015.12.092"},{"key":"2023010916422369871_j_bams-2016-0023_ref_002_w2aab2b8b1b1b7b1ab2b1b2Aa","doi-asserted-by":"crossref","unstructured":"Jahn K, Heinze C, Selge C, He\u00dfelbarth K, Schniepp R. Gait disorders in geriatric patients. Classification and therapy. Nervenarzt 2015;86:431\u20139.","DOI":"10.1007\/s00115-014-4182-8"},{"key":"2023010916422369871_j_bams-2016-0023_ref_003_w2aab2b8b1b1b7b1ab2b1b3Aa","doi-asserted-by":"crossref","unstructured":"Rosano C, Rosso AL, Studenski SA. Aging, brain, and mobility: progresses and opportunities. J Gerontol A Biol Sci Med Sci 2014;69:1373\u20134.","DOI":"10.1093\/gerona\/glu159"},{"key":"2023010916422369871_j_bams-2016-0023_ref_004_w2aab2b8b1b1b7b1ab2b1b4Aa","doi-asserted-by":"crossref","unstructured":"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\u20139.","DOI":"10.1016\/j.gaitpost.2014.03.189"},{"key":"2023010916422369871_j_bams-2016-0023_ref_005_w2aab2b8b1b1b7b1ab2b1b5Aa","doi-asserted-by":"crossref","unstructured":"Senanayake CM, Senanayake SM. Computational intelligent gait-phase detection system to identify pathological gait. IEEE Trans Inf Technol Biomed 2010;14:1173\u20139.","DOI":"10.1109\/TITB.2010.2058813"},{"key":"2023010916422369871_j_bams-2016-0023_ref_006_w2aab2b8b1b1b7b1ab2b1b6Aa","doi-asserted-by":"crossref","unstructured":"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\u2013804.","DOI":"10.1080\/10255842.2013.848859"},{"key":"2023010916422369871_j_bams-2016-0023_ref_007_w2aab2b8b1b1b7b1ab2b1b7Aa","unstructured":"Simonsen EB. Contributions to the understanding of gait control. Dan Med J 2014;61:B4823."},{"key":"2023010916422369871_j_bams-2016-0023_ref_008_w2aab2b8b1b1b7b1ab2b1b8Aa","doi-asserted-by":"crossref","unstructured":"Simon SR. Quantification of human motion: gait analysis-benefits and limitations to its application to clinical problems. J Biomech 2004;37:1869\u201380.","DOI":"10.1016\/j.jbiomech.2004.02.047"},{"key":"2023010916422369871_j_bams-2016-0023_ref_009_w2aab2b8b1b1b7b1ab2b1b9Aa","doi-asserted-by":"crossref","unstructured":"Lord SE, Halligan PW, Wade DT. Visual gait analysis: the development of a clinical assessment and scale. Clin Rehabil 1998;12:107\u201319.","DOI":"10.1191\/026921598666182531"},{"key":"2023010916422369871_j_bams-2016-0023_ref_010_w2aab2b8b1b1b7b1ab2b1c10Aa","doi-asserted-by":"crossref","unstructured":"Tao W, Liu T, Zheng R, Feng H. Gait analysis using wearable sensors. Sensors (Basel) 2012;12:2255\u201383.","DOI":"10.3390\/s120202255"},{"key":"2023010916422369871_j_bams-2016-0023_ref_011_w2aab2b8b1b1b7b1ab2b1c11Aa","doi-asserted-by":"crossref","unstructured":"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\u20139.","DOI":"10.5535\/arm.2016.40.2.183"},{"key":"2023010916422369871_j_bams-2016-0023_ref_012_w2aab2b8b1b1b7b1ab2b1c12Aa","unstructured":"Miko\u0142ajewska E. The value of the NDT-Bobath method in post-stroke gait training. Adv Clin Exp Med 2013;22:261\u201372."},{"key":"2023010916422369871_j_bams-2016-0023_ref_013_w2aab2b8b1b1b7b1ab2b1c13Aa","unstructured":"Miko\u0142ajewska E. Associations between results of post-stroke NDT-Bobath rehabilitation in gait parameters, ADL and hand functions. Adv Clin Exp Med 2013;22:731\u20138."},{"key":"2023010916422369871_j_bams-2016-0023_ref_014_w2aab2b8b1b1b7b1ab2b1c14Aa","doi-asserted-by":"crossref","unstructured":"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\u201384.","DOI":"10.1016\/j.gaitpost.2006.05.014"},{"key":"2023010916422369871_j_bams-2016-0023_ref_015_w2aab2b8b1b1b7b1ab2b1c15Aa","doi-asserted-by":"crossref","unstructured":"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\u201380.","DOI":"10.1016\/j.gaitpost.2012.11.026"}],"container-title":["Bio-Algorithms and Med-Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.degruyter.com\/view\/j\/bams.2017.13.issue-1\/bams-2016-0023\/bams-2016-0023.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/bams-2016-0023\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/bams-2016-0023\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,24]],"date-time":"2024-06-24T10:08:29Z","timestamp":1719223709000},"score":1,"resource":{"primary":{"URL":"https:\/\/bamsjournal.com\/resources\/html\/article\/details?id=616714"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,2,3]]},"references-count":15,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2017,2,28]]},"published-print":{"date-parts":[[2017,3,1]]}},"alternative-id":["10.1515\/bams-2016-0023"],"URL":"https:\/\/doi.org\/10.1515\/bams-2016-0023","relation":{},"ISSN":["1896-530X","1895-9091"],"issn-type":[{"value":"1896-530X","type":"electronic"},{"value":"1895-9091","type":"print"}],"subject":[],"published":{"date-parts":[[2017,2,3]]}}}