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Markers put on the arm undergo large soft tissue artefact (STA). Using markers on the forearm, multibody kinematics optimization (MKO) helps improve the accuracy of the arm kinematics especially its longitudinal rotation. However deleterious effect of STA may persist and affect other segment estimate. The objective was to present an innovative multibody kinematics optimization algorithm with projection of markers onto a requested axis of the local system of coordinates, to cancel their deleterious effect on this degree-of-freedom. Four subjects equipped with markers put on intracortical pins inserted into the humerus, on skin (scapula, arm and forearm) and subsequently on rigid cuffs (arm and forearm) performed analytic, daily-living, sports and range-of-motion tasks. Scapulohumeral kinematics was estimated using 1) pin markers (reference), 2) single-body optimization, 3) MKO, 4) MKO with projection of all arm markers and 5) MKO with projection of a selection of arm markers. Approaches 2-4 were applied to markers put on the skin and the cuff. The main findings were that multibody kinematics optimization improved the accuracy of 40-50% and the projection algorithm added an extra 20% when applied to cuff markers or a selection of skin markers (all but the medial epicondyle). Therefore, the projection algorithm performed better than multibody and single-body optimizations, especially when using markers put on a cuff. Error of humerus orientation was reduced by half to finally be less than 5°. To conclude, this innovative algorithm is a promising approach for estimating accurate upper-limb kinematics.

In the context of neuro-orthopedic pathologies affecting walking and thus patients' quality of life, understanding the mechanisms of gait deviations and identifying the causal motor impairments is of primary importance. Beside other approaches, neuromusculoskeletal simulations may be used to provide insight into this matter. To the best of our knowledge, no computational framework exists in the literature that allows for predictive simulations featuring muscle co-contractions, and the introduction of various types of perturbations during both healthy and pathological gait types. The aim of this preliminary study was to adapt a recently proposed EMG-marker tracking optimization process to a lower limb musculoskeletal model during equinus gait, a multiphase problem with contact forces. The resulting optimization method tracking EMG, ground reactions forces, and marker trajectories allowed an accurate reproduction of joint kinematics (average error of 5.4 ± 3.3 mm for pelvis translations, and 1.9 ± 1.3° for pelvis rotation and joint angles) and ensured good temporal agreement in muscle activity (the concordance between estimated and measured excitations was 76.8 ± 5.3 %) in a relatively fast process (3.88 ± 1.04 h). We have also highlighted that the tracking of ground reaction forces was possible and accurate (average error of 17.3 ± 5.5 N), even without the use of a complex foot-ground contact model.

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