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.

No abstract

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.

In the literature, conventional 3D inverse dynamic models are limited in three aspects related to inverse dynamic notation, body segment parameters and kinematic formalism. First, conventional notation yields separate computations of the forces and moments with successive coordinate system transformations. Secondly, the way conventional body segment parameters are defined is based on the assumption that the inertia tensor is principal and the centre of mass is located between the proximal and distal ends. Thirdly, the conventional kinematic formalism uses Euler or Cardanic angles that are sequence-dependent and suffer from singularities. In order to overcome these limitations, this paper presents a new generic method for inverse dynamics. This generic method is based on wrench notation for inverse dynamics, a general definition of body segment parameters and quaternion algebra for the kinematic formalism.

Musculo-tendon forces and joint reaction forces are typically estimated using a two-step method, computing first the musculo-tendon forces by a static optimization procedure and then deducing the joint reaction forces from the force equilibrium. However, this method does not allow studying the interactions between musculo-tendon forces and joint reaction forces in establishing this equilibrium and the joint reaction forces are usually overestimated. This study introduces a new 3D lower limb musculoskeletal model based on a one-step static optimization procedure allowing simultaneous musculo-tendon, joint contact, ligament and bone forces estimation during gait. It is postulated that this approach, by giving access to the forces transmitted by these musculoskeletal structures at hip, tibiofemoral, patellofemoral and ankle joints, modeled using anatomically consistent kinematic models, should ease the validation of the model using joint contact forces measured with instrumented prostheses. A blinded validation based on four datasets was made under two different minimization conditions (i.e., C1 - only musculo-tendon forces are minimized, and C2 - musculo-tendon, joint contact, ligament and bone forces are minimized while focusing more specifically on tibiofemoral joint contacts). The results show that the model is able to estimate in most cases the correct timing of musculo-tendon forces during normal gait (i.e., the mean coefficient of active/inactive state concordance between estimated musculo-tendon force and measured EMG envelopes was C1: 65.87% and C2: 60.46%). The results also showed that the model is potentially able to well estimate joint contact, ligament and bone forces and more specifically medial (i.e., the mean RMSE between estimated joint contact force and in vivo measurement was C1: 1.14BW and C2: 0.39BW) and lateral (i.e., C1: 0.65BW and C2: 0.28BW) tibiofemoral contact forces during normal gait. However, the results remain highly influenced by the optimization weights that can bring to somewhat aphysiological musculo-tendon forces.

ISB recommendations on the reporting of intersegmental forces and moments ISB recommendations on the reporting of intersegmental forces and moments during human motion analysis during human motion analysis