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Abstrakty
The patients may show various patellar order samples. Patellofemoral disorders are defined in all cases with patellofemoral pain syndrome. Right and left knees belonging to the 60 men and 60 women volunteers were measured. By using the distances from the measurements, ligamentum patellae (LP) angle was estimated by artificial neural network (ANN) method. According to the results, one can confidently say that the method is capable for estimating this angle. The related root mean square error belonging to the test data of ANN is between 0.488 and 1.188. These angles for women are two times larger than men's. Eventually, the risk of patellofemoral disorders is greater for women than men.
Wydawca
Czasopismo
Rocznik
Tom
Strony
184--188
Opis fizyczny
Bibliogr. 19 poz., rys., tab., wykr.
Twórcy
autor
- Cumhuriyet University Vocational School of Health, Sivas, Turkey
autor
- Cumhuriyet University School of Physical Education and Sports, Sivas, Turkey
autor
- Cumhuriyet University Vocational School of Health, Sivas, Turkey
autor
- Cumhuriyet University Faculty of Medicine Department of Anatomy, Sivas, Turkey
Bibliografia
- [1] Smith TO, Davies L, Donell ST. The reliability and validity of assessing medio-lateral patellar position: a systematic review. Manual Ther 2009;14:355–62.
- [2] Grelsamer RP. Patellar malalignment. J Bone Joint Surg 2000;82:1639–50.
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- [4] Byl T, Cole J, Livingston L. What determines the magnitude of the Q-angle? A preliminary study of selected skeletal and muscular measures. J Sport Rehabil 2000;9:26–34.
- [5] Herrington L. The inter-tester reliability of a clinical measurement used to determine the medial lateral orientation of the patella. Manual Ther 2002;7:163–7.
- [6] Schöllhorn WI. Applications of artificial neural nets in clinical biomechanics. Clin Biomech 2004;19:876–98.
- [7] Michalski R, Wit A, Gajewski J. Use of artificial neural networks for assessing parameters of gait symmetry. Acta Bioeng Biomech 2011;13(4):65–70.
- [8] Baspinar U, Varol HS, Senyurek VY. Performance comparison of artificial neural network and Gaussian mixture model in classifying hand motions by using sEMG signals. Biocybern Biomed Eng 2013;33:33–45.
- [9] Keijsers NLW, Stolwijk NM, Louwerens JWK, Duysens J. Classification of forefoot pain based on plantar pressure measurements. Clin Biomech 2013;28:350–6.
- [10] Kutilek P, Farkasova B. Prediction of lower extremities' movement by angle–angle diagrams and neural networks. Acta Bioeng Biomech 2011;13(2):57–65.
- [11] Haykin S. Neural networks: a comprehensive foundation. 2nd ed. New Jersey: Prentice-Hall; 1999.
- [12] Hornik K, Stinchcombe M, White H. Multilayer feedforward networks are universal approximator. Neural Networks 1989;2:359–66.
- [13] Neurosolutions, http://www.neurosolutions.com/.
- [14] Levenberg KA. Method for the solution of certain non-linear problems in least squares. Q Appl Math 1944;2:164–8.
- [15] Marquardt D. An algorithm for least-squares estimation of nonlinear parameters. SIAM J Appl Math 1963;11:431–41.
- [16] Wilson T, Kitsell F. Is the Q-angle an absolute or variable measure? Physiotherapy 2002;88:296–302.
- [17] Wong YM. Recording the vastii muscle onset timing as a diagnostic parameter for patellofemoral pain syndrome: factor or fad? Phys Ther Sport 2009;10:71–4.
- [18] Ferber R, Davis IM, Williams III DS. Gender differences in lower extremity mechanics during running. Clin Biomech 2003;18:350–7.
- [19] Taunton JE, Ryan MB, Clement DB, McKenzie DC, Lloyd-Smith DR, Zunbo BD. A retrospective case–control analysis of 2002 running injuries. Brit J Sport Med 2002;36:95–101.
Typ dokumentu
Bibliografia
Identyfikator YADDA
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