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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.

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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.

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.

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Inverse dynamics is the most comprehensive method that gives access to the net joint forces and moments during walking. However it is based on assumptions (i.e., rigid segments linked by ideal joints) and it is known to be sensitive to the input data (e.g., kinematic derivatives, positions of joint centres and centre of pressure, inertial parameters). Alternatively, transducers can be used to measure directly the load applied on the residuum of transfemoral amputees. So, the purpose of this study was to compare the forces and moments applied on a prosthetic knee measured directly with the ones calculated by three inverse dynamics computations--corresponding to 3 and 2 segments, and "ground reaction vector technique"--during the gait of one patient. The maximum RMSEs between the estimated and directly measured forces (i.e., 56 N) and moment (i.e., 5 N m) were relatively small. However the dynamic outcomes of the prosthetic components (i.e., absorption of the foot, friction and limit stop of the knee) were only partially assessed with inverse dynamic methods.