No abstract

The purpose of this study was to compare the main kinematic, kinetic, and dynamic parameters of elite and well-trained sprinters during the starting block phase and the 2 subsequent steps. Six elite sprinters (10.06-10.43 s/100 m) and 6 well-trained sprinters (11.01-11.80 s/100 m) equipped with 63 passive reflective markers performed 4 maximal 10 m sprint starts on an indoor track. An opto-electronic motion analysis system consisting of 12 digital cameras (250 Hz) was used to record 3D marker trajectories. At the times "on your marks," "set," "clearing the block," and "landing and toe-off of the first and second step," the horizontal position of the center of mass (CM), its velocity (XCM and VCM), and the horizontal position of the rear and front hand (X(Hand_rear) and X(Hand_front)) were calculated. During the pushing phase on the starting block and the 2 first steps, the rate of force development and the impulse (F(impulse)) were also calculated. The main results showed that at each time XCM and VCM were significantly greater in elite sprinters. Moreover, during the pushing phase on the block, the rate of force development and F(impulse) were significantly greater in elite sprinters (respectively, 15,505 +/- 5,397 N.s and 8,459 +/- 3,811 N.s for the rate of force development; 276.2 +/- 36.0 N.s and 215.4 +/- 28.5 N.s for F(impulse), p < or = 0.05). Finally, at the block clearing, elite sprinters showed a greater XHand_rear and X(Hand_front) than well-trained sprinters (respectively, 0.07+/- 0.12 m and -0.27 +/- 0.36 m for X(Hand_rear); 1.00 +/- 0.14 m and 0.52 +/- 0.27 m for X(Hand_front); p < or = 0.05). The muscular strength and arm coordination appear to characterize the efficiency of the sprint start. To improve speed capacities of their athletes, coaches must include in their habitual training sessions of resistance training.

Magneto-Inertial Measurement Unit sensors (MIMU) display high potential for the quantitative evaluation of upper limb kinematics, as they allow monitoring ambulatory measurements. The sensor-to-segment calibration step, consisting of establishing the relation between MIMU sensors and human segments, plays an important role in the global accuracy of joint angles. The aim of this study was to compare sensor-to-segment calibrations for the MIMU-based estimation of wrist, elbow, and shoulder joint angles, by examining trueness (“close to the reference”) and precision (reproducibility) validity criteria. Ten subjects performed five sessions with three different operators. Three classes of calibrations were studied: segment axes equal to technical MIMU axes (TECH), segment axes generated during a static pose (STATIC), and those generated during functional movements (FUNCT). The calibrations were compared during the maximal uniaxial movements of each joint, plus an extra multi-joint movement. Generally, joint angles presented good trueness and very good precision in the range 5°–10°. Only small discrepancy between calibrations was highlighted, with the exception of a few cases. The very good overall accuracy (trueness and precision) of MIMU-based joint angle data seems to be more dependent on the level of rigor of the experimental procedure (operator training) than on the choice of calibration itself.

In order to obtain the lower limb kinematics from skin-based markers, the soft tissue artefact (STA) has to be compensated. Global optimization (GO) methods rely on a predefined kinematic model and attempt to limit STA by minimizing the differences between model predicted and skin-based marker positions. Thus, the reliability of GO methods depends directly on the chosen model, whose influence is not well known yet. This study develops a GO method that allows to easily implement different sets of joint constraints in order to assess their influence on the lower limb kinematics during gait. The segment definition was based on generalized coordinates giving only linear or quadratic joint constraints. Seven sets of joint constraints were assessed, corresponding to different kinematic models at the ankle, knee and hip: SSS, USS, PSS, SHS, SPS, UHS and PPS (where S, U and H stand for spherical, universal and hinge joints and P for parallel mechanism). GO was applied to gait data from five healthy males. Results showed that the lower limb kinematics, except hip kinematics, knee and ankle flexion-extension, significantly depend on the chosen ankle and knee constraints. The knee parallel mechanism generated some typical knee rotation patterns previously observed in lower limb kinematic studies. Furthermore, only the parallel mechanisms produced joint displacements. Thus, GO using parallel mechanism seems promising. It also offers some perspectives of subject-specific joint constraints.