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.

Soft tissue artefact (STA), i.e. the motion of the skin, fat and muscles gliding on the underlying bone, may lead to a marker position error reaching up to 8.7cm for the particular case of the scapula. Multibody kinematics optimisation (MKO) is one of the most efficient approaches used to reduce STA. It consists in minimising the distance between the positions of experimental markers on a subject skin and the simulated positions of the same markers embedded on a kinematic model. However, the efficiency of MKO directly relies on the chosen kinematic model. This paper proposes an overview of the different upper limb models available in the literature and a discussion about their applicability to MKO. The advantages of each joint model with respect to its biofidelity to functional anatomy are detailed both for the shoulder and the forearm areas. Models capabilities of personalisation and of adaptation to pathological cases are also discussed. Concerning model efficiency in terms of STA reduction in MKO algorithms, a lack of quantitative assessment in the literature is noted. In priority, future studies should concern the evaluation and quantification of STA reduction depending on upper limb joint constraints