Influence of joint constraints on lower limb kinematics estimation from skin markers using global optimization

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.

Tibio-femoral joint constraints for bone pose estimation during movement using multi-body optimization

“…The knee has also been modelled using a parallel mechanism incorporating articular surfaces, as sphere-on-plane contacts, and isometric pseudo-ligamentous structures, and allowing for both translations and rotations [9].…”

Influence of biomechanical multi-joint models used in global optimisation to estimate healthy and osteoarthritis knee kinematics

“…Dynamic knee kinematics obtained after GO greatly depended on the constraints imposed by the multi-joint models as stated by Duprey et al (2010). Moreover, although some multi-joint models were able to compensate for STA along some axes of movement and for some positions, mean RMSE values remained relatively large.…”

“…Despite the results, GO remains interesting and is increasingly used in gait analysis since it does not require multiple calibrations and easily enables computing inverse dynamics and developing musculoskeletal models (Duprey et al 2010). The use of knee kinematic constraints defined from a parallel mechanism is also quite interesting since it is the only model to consider the complexity of the knee and to allow translations.…”

“…Data from KneeKGe were optimised with GO using generalised coordinates (Duprey et al 2010). The eight multi-joint models corresponded to the following joints constraints at the ankle, knee and hip joints: NNN, SSS, USS, PSS, SHS, UHS, SPS and PPS (where N, S, U, H and P stand for 'no model', spherical, universal, hinge and parallel mechanism joints).…”

“…Among these methods, global optimisation (GO) provides the optimised position of the lower limbs by minimising the differences between measured and model-predicted markers coordinates under kinematic constraints (Lu et al 1999). Many multi-joint models were tested with GO (Duprey et al 2010), but few of them were directly validated on subjects (Stagni et al 2009;Andersen et al 2010;Li et al 2012). Moreover, GO has never been validated in osteoarthritis (OA) subjects.…”