Gait patterns classification based on cluster and bicluster analysis

• Autorzy: Pauk J. , Minta-Bielecka K.

Identyfikatory

DOI

10.1016/j.bbe.2016.03.002

• Języki publikacji: EN

Abstrakty

EN

Gait patterns of hemiplegia patients have many potential applications such as assistance in diagnosis or clinical decision-making. Many techniques were developed to classify gait patterns in past years; however, these methods have some limitations. The main goal of the study was to present the performance evaluation results of the new biclustering algorithm called KMB. The second objective was to compare clustering and biclustering methods. The study was performed based on the gait patterns of 41 hemiplegia patients over 12 months post-stroke, at the age of 48.6 ± 19.6 years. Spatial–temporal gait parameters and joint moments were measured using motion capture system and force plates. Clustering and biclustering algorithms were applied for data consisting of joint moments of lower limbs. The obtained results of this study based on joint moments, clustering, and biclustering can be applied to evaluate patient condition and treatment effectiveness. We suggest that the biclustering algorithm compared to clustering algorithms better characterizes the specific traits and abnormalities of the joint moments, especially in case of hemiplegia patients.

Słowa kluczowe

EN

hemiplegia; gait patterns; clustering; biclustering; joints moments; spatial temporal gait parameter

PL

porażenie połowicze; wzorzec chodu; grupowanie; biklasteryzacja

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2016
• Tom: Vol. 36, no. 2
• Strony: 391--396
• Opis fizyczny: Bibliogr. 22 poz., tab., wykr.

Twórcy

autor

Pauk J.

• Bialystok Technical University, Wiejska 45C, 15-351 Bialystok, Poland; Glenrose Rehabilitation Hospital, Edmonton, Canada

autor

Minta-Bielecka K.

• Bialystok University of Technology, Bialystok, Poland

Bibliografia

• [1] Olney SJ, Richards C. Hemiparetic gait following stroke. Part I: Characteristics. Gait Posture 1996;4(2):136–48.
• [2] Wasay M, Khatri IA, Kaul S. Stroke in South Asian countries. Nat Rev Neurol 2014;10(3):135–43.
• [3] Winter DT. Biomechanics and motor control of human gait: normal elderly and pathological. Waterloo University of Waterloo Press; 1991.
• [4] Kramers De Quervain IA, Simon SR, Leurgans S, Pease WS, McAllister D. Gait pattern in the early recovery period after stroke. J Bone Jt Surg A 1996;78(10):1506–14.
• [5] 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;20(2):140–6.
• [6] Straudi S, Manca M, Aiello E, Ferraresi G, Cavazza S, Basaglia N. Sagittal plane kinematic analysis of the six-minute walk test: a classification of hemiplegic gait. Eur J Phys Rehab Med 2009;45(3):341–7.
• [7] Begg RK, Palaniswami M, Owen B. Support vector machines for automated gait classification. IEEE Trans Biomed Eng 2005;52(5):828.
• [8] Kamruzzaman J. Support vector machines and other pattern recognition approaches to the diagnosis of cerebral palsy gait. IEEE Trans Biomed Eng 2006;53(12):2479–89.
• [9] Kinsella S, Moran K. Gait pattern categorization of stroke participants with equinus deformity of the foot. Gait Posture 2008;27(1):144–51.
• [10] Enachescu D, Enachescu C. Clustering and spatial variation in risk. Biocybern Biomed Eng 2009;29(2):19–30.
• [11] Salazar AJ, De Castro OC, Bravo RJ. Novel approach for spastic hemiplegia classification through the use of support vector machines. Conf Proc IEEE Eng Med Biol Soc, vol. 1. 2004. pp. 466–9.
• [12] 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;18(1):114–25.
• [13] Agrawal R, Gehrke J, Gunopulos D, Raghavan P. Automatic subspace clustering of high dimensional data for data mining applications. Proc. 1998 ACM SIGMOD International Conference on Management of Data; 1998.
• [14] Fisher DH. Knowledge acquisition via incremental conceptual clustering. Mach Learn 1987;2(2):139–72.
• [15] Jain AK, Dubes RC. Algorithms for clustering data. Prentice Hall; 1988.
• [16] Cheng Y, Church GM. Biclustering of expression data. American Association for Artificial Intelligence; 2000.
• [17] Yang J, Wang W, Wang H, Yu P. Enhanced biclustering on expression data. Proc. 2003 Third IEEE Conference on Bioinformatics and Bioengineering; 2003.
• [18] Borah B, Bhattachatyya DK. Biclustering expression data using node addition algorithm. Proc. 2007 15th International Conference on Advanced Computing and Communications; 2007.
• [19] Zhao H, Wee-Chung Liew A, Wang DZ, Yan H. Biclustering analysis for pattern discovery: current techniques, comparative studies and applications. Curr Bioinform 2012;7:43–55.
• [20] Wall J, Turnbull G. Gait asymmetries in residual hemiplegia. Arch Phys Med Rehabil 1986;67:550–3.
• [21] Manca M, Ferraresi G, Cosma M, Cavazzuti L, Morelli M, Benedetti MG. Gait patterns in hemiplegic patients with equinus foot deformity. BioMed Res Int 2014;2014: 1–8.
• [22] Perry J, Garrett M, Gronley JK, Mulroy SJ. Classification of walking handicap in the stroke population. Stroke 1995;26 (6):982–9.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.