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Fizička kultura
2020, vol. 74, iss. 1, pp. 57-64
Assessment of a knee resistance by applying the computational methods
Adamović Katarinaa, Vukićević Arsob, Vulović Radunc, Đorović Smiljanad, Radaković Radivojec, Jovičić Gordanab, Filipović Nenadd ORCID
aUniverzitet u Sinsinatiju, Odeljenje za sport, Sinsinati, SAD
bUniversity of Kragujevac, Faculty of Mechanical Engineering
cBioengineering Research and Development Center - BioIRC, Kragujevac
dUniversity of Kragujevac, Faculty of Mechanical Engineering + Bioengineering Research and Development Center - BioIRC, Kragujevac

email: smiljana@kg.ac.rs
Project:
Application of biomedical engineering for preclinical and clinical practice (MESTD - 41007)
Multiscale Methods and Their Applicatios in Nanomedicine (MESTD - 174028)

Keywords: non-invasive diagnostics; prediction of knee injury; biomechanical model; inverse dynamics; tennis
Abstract
In tennis, the complex serving motions produce high mechanical stresses on player's musculoskeletal, tendon and ligament joints. In this paper, different cognitive methods have been integrated in order to non-invasively assess the knee's bone and cartilage resistance at the maximum power tennis serve. The proposed methodology is based on the creation of patientspecific biomechanical model, as well as on the tracking the knee's kinematics, ground force measurement, inverse dynamics modelling and analysis of the knee using the Finite Element Method with aim to assess the knee resistance of a tennis player, considering acute deformations and potential injuries. The main objective of this paper is development of the optimised computational technology and creation of practical diagnostic tool for non-invasive assessment of the knee function during specific moves and motions in tennis. It is expected that this approach can provide prediction and injury prevention in training and competitive tennis to a significant extent.
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About

article language: Serbian, English
document type: Preliminary Report
DOI: 10.5937/fizkul2001057A
received: 05/11/2019
accepted: 25/04/2020
published online: 22/05/2020
published in SCIndeks: 20/11/2020
peer review method: double-blind
Creative Commons License 4.0

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