(JPES), 19(1), Art 95, pp. 658 668, 2019
online ISSN: 2247 806X; pISSN: 2247 – 8051; ISSN L = 2247 8051 © JPES
658
Corresponding Author: SAMIHA AMARA, Email: samiha_ath@yahoo.fr
SAMIHA AMARA
1
, BESSEM MKAOUER
2
, HELMI CHAABENE
3
, YASSINE NEGRA
4
, FATMA Z. BEN
SALAH
5
1,2
Higher Institute of Sport and Physical Education of Ksar Said, Manouba University, Manouba, TUNISIA;
3
Division of Training and Movement Sciences, Research Focus Cognition Sciences, University of Potsdam,
Potsdam, GERMANY;
3,4
Higher Institute of Sport and Physical Education of Kef, Jandouba University, Kef, TUNISIA;
4
Research Unit, Higher Institute of Sports and Physical Education, Ksar Said, Manouba, TUNISIA;
5
Faculty of Medicine of Tunis, University of Tunis Elmanar, TUNISIA.
Published online:March 31, 2019
(Accepted for publication February 28, 2019)
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This study aimed at exploring the key kinetic and kinematic factors of 110m hurdle clearance performance
using threedimensional (3D) analysis system. Ten national level athletes participated in this study. The
kinematic analysis of the hurdling sequences was recorded using ten mutually synchronized digital cameras.
Body markers were digitized using SkillSpector
®
software. Groundreaction force was calculated by using rigid
body inverse dynamics using the Smith’s equations. All variables were combined into components through a
principal component analysis. The retained components have been used in a multiple regression analysis.
Twenty variables were retained as key hurdling performance determinants. Specifically, the horizontal and
vertical velocity of the centre of mass (COM) and the leadleg/trailleg in all phases (i.e., takeoff, flight, and
landing), horizontal and vertical displacement of the COM, the leadleg/trailleg vertical displacement, and the
flighttime at clearance are among the main hurdling performance determinants. Overall, to improve hurdling
performance, greater horizontal velocity, lower vertical displacement at flight and lower contacttime at the take
off phases through a higher rate of force development are needed.
’ Motion analysis; inverse dynamic; hurdling; track and field
Hurdling is a complex technical event that requires high levels of physical fitness (Iskra, 1995). In fact,
sprint speed, intersegmental coordination, reactive strength, and great technical skills are the most key physical
fitness aspects that should regularly be developed and routinely implemented in training programs to succeed the
race (Coh, 2003; Coh & Zvan, 2018). In particular, the technique of clearing the hurdle represents one of the
most determinant elements defining the competitive result (López, Padullés, & Olsson, 2011; McLean, 1994;
Sidhu & Singh, 2015). In this context, Iskra (1995) indicated that the improvement of the 110m hurdle race
technique represents one the central component of training.
Kinetic and kinematic analysis of 110m hurdle clearance, in particular, may help understanding the
critical factors that influence performance and assist coaches exploring the theoretical basis for hurdle running
training (Salo, Grimshaw, & Viitasalo, 1999). Additionally, the kinetic and kinematic outcomes are widely used
to help improving athletes’ training and performance, alike (Coh, Jost, & Skof, 2000; Salo et al., 1999). Previous
studies examined the kinematic analysis of Colin Jackson’s clearance (World Record Holder) at the fourth hurdle
in the 110m race (Coh,2003; Coh, Zvan, and Jost,2004; Coh and Zvan, 2018). Authors agreed that the horizontal
velocity of the centre of mass (COM) at takeoff and during clearance, the height of COM above the hurdle, the
leadleg’s knee swing velocity, the flighttime, and the contacttime at the landing phase represent the key
hurdling performance factors. In terms of kinetic factors, it has been demonstrated that the peak horizontal force
at landing is paramount for an efficient hurdling (Coh and Iskra, 2012).
It is noteworthy that all previous biomechanical analyses of hurdling were carried out on only one to
three hurdles (Iskra & Coh, 2006) with a large variation in the hurdles selected for analysis. For instance,
previous research focused on only the first (Lee, 2004; Lee, Park, Ryu, & Kim, 2008; Salo, 2002; Xu, Wang, &
Yan, 2005), the second (Iliew & Primakov, 1978; Mclean, 1994), the third (Lee, 2009; Salo, Peltola, & Viitasalo,
1993; Tsarouchas, Papadopoulos, Kalamaras, & Giavroglu, 1993), the fourth (Coh, 2003; Cooh et al., 2000; Li,
Zhou, Li, & Wang, 2011; Ryu & Chang, 2011), the fifth (Coh & Zvan, 2018; Sidhu, 2016; Sidhu & Singh,
2015), the sixth (Li & Fu, 2000; Peak et al., 2011), the seventh (Shibayama, Fujii, Takenaka, Tanigawa, & Ae,
2011) the ninth (Iwkin, Jegorow, & Zukow, 1987; Salo & Scarborough, 2006), and the tenth hurdle (Lopez et
al., 2011; Chow, 1998).