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Musculoskeletal modeling and optimization theory are often used to determine muscle forces in vivo. However, convincing quantitative evaluation of these predictions has been limited to date. The present study evaluated model predictions of knee muscle forces during walking using in vivo measurements of joint contact loading acquired from an instrumented implant. Joint motion, ground reaction force, and tibial contact force data were recorded simultaneously from a single subject walking at slow, normal, and fast speeds. The body was modeled as an 8-segment, 21-degree-of-freedom articulated linkage, actuated by 58 muscles. Joint moments obtained from inverse dynamics were decomposed into leg-muscle forces by solving an optimization problem that minimized the sum of the squares of the muscle activations. The predicted knee muscle forces were input into a 3D knee implant contact model to calculate tibial contact forces. Calculated and measured tibial contact forces were in good agreement for all three walking speeds. The average RMS errors for the medial, lateral, and total contact forces over the entire gait cycle and across all trials were 140 AE 40 N, 115 AE 32 N, and 183 AE 45 N, respectively. Muscle coordination predicted by the model was also consistent with EMG measurements reported for normal walking. The combined experimental and modeling approach used in this study provides a quantitative framework for evaluating model predictions of muscle forces in human movement. ß

Soft tissue artefact (STA) represents one of the main obstacles for obtaining accurate and reliable skeletal kinematics from motion capture. Many studies have addressed this issue, yet there is no consensus on the best available bone pose estimator and the expected errors associated with relevant results. Furthermore, results obtained by different authors are difficult to compare due to the high variability and specificity of the phenomenon and the different metrics used to represent these data. Therefore, the aim of this study was twofold: firstly, to propose standards for description of STA; and secondly, to provide illustrative STA data samples for body segments in the upper and lower extremities and for a range of motor tasks specifically, level walking, stair ascent, sit-to-stand, hip- and knee-joint functional movements, cutting motion, running, hopping, arm elevation and functional upper-limb movements. The STA dataset includes motion of the skin markers measured in vivo and ex vivo using stereophotogrammetry as well as motion of the underlying bones measured using invasive or bio-imaging techniques (i.e., X-ray fluoroscopy or MRI). The data are accompanied by a detailed description of the methods used for their acquisition, with information given about their quality as well as characterization of the STA using the proposed standards. The availability of open-access and standard-format STA data will be useful for the evaluation and development of bone pose estimators thus contributing to the advancement of three-dimensional human movement analysis and its translation into the clinical practice and other applications.

Accurate measurement of knee-joint kinematics is critical for understanding the biomechanical function of the knee in vivo. Measurements of the relative movements of the bones at the knee are often used in inverse dynamics analyses to estimate the net muscle torques exerted about the joint, and as inputs to finite-element models to accurately assess joint contact. The fine joint translations that contribute to patterns of joint stress are impossible to measure accurately using traditional video-based motion capture techniques. Sub-millimetre changes in joint translation can mean the difference between contact and no contact of the cartilage tissue, leading to incorrect predictions of joint loading. This paper describes the use of low-dose X-ray fluoroscopy, an in vivo dynamic imaging modality that is finding increasing application in human joint motion measurement. Specifically, we describe a framework that integrates traditional motion capture, X-ray fluoroscopy and anatomically-based finite-element modelling for the purpose of assessing joint function during dynamic activity. We illustrate our methodology by applying it to study patellofemoral joint function, wherein the relative movements of the patella are predicted and the corresponding joint-contact stresses are calculated for a step-up task.

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