In Canada, approximately 544,000 upper-limb injuries occurred in a 12-month period between 2009 and 2010, many of which were injuries to the rotator cuff muscles of the shoulder. Because of the complex structure and function of the shoulder, it is often difficult to determine which muscles have been injured. The most widely used technology to study human movement is motion capture, wherein markers are affixed to a subject’s skin and are tracked by cameras as the subject moves. The recorded marker trajectories are then used to estimate the bone locations and joint angles during the tracked motion. This is called an inverse kinematic simulation. The simulation can then be used to estimate variables that are difficult or impossible to measure directly, such as the activation of single muscle heads within a muscle group. However, muscles bulge and skin stretches during movement, so the markers that are affixed to the skin generally move relative to the underlying bones. These errors, known as soft-tissue artifacts, lead to uncertainty in the calculation of bone locations and, consequently, uncertainty in the computed skeletal joint angles. This uncertainty limits the use of inverse kinematic simulations in clinical settings. Given the skin tissue’s elastic behaviour, a spring-based equilibrium model can be used to estimate the behaviour of skin during non-impulsive motion. In the proposed model, markers were placed on the surface of ellipsoids (representing the thorax, abdomen, scapula, and upper arm) and were attached to each other via springs. The system was assumed to remain in static equilibrium during sufficiently slow movements to approximate the stretch of the skin. In this thesis, the development and application of a proof-of-concept model to estimate the pose of the skeleton is described. This work demonstrates the feasibility of using such a model to reduce errors due to soft-tissue motion.

Modeling and simulations are useful tools to help understand knee function and injuries. As there are more muscles in the human knee joint than equations of motion, optimization protocols are required to solve a problem. The purpose of this thesis was to improve the biofidelity of these simulations by adding in vivo constraints derived from experimental intra-cortical pin data and stability-inspired objective functions within an OpenSim-Matlab forward-inverse dynamics simulation framework on lower limb muscle activation predictions. Results of this project suggest that constraining the model knee joint’s ranges of motion with pin data had a significant impact on lower limb kinematics, especially in rotational degrees of freedom. This affected muscle activation predictions and knee joint loading when compared to unconstrained kinematics. Furthermore, changing the objective will change muscle activation predictions although minimization of muscle activation as an objective remains more accurate than the stability inspired functions, at least for gait. /// La modélisation et les simulations in-silico sont des outils importants pour approfondir notre compréhension de la fonction du genou et ses blessures. Puisqu’il y a plus de muscles autour du genou humain que d’équations de mouvement, des procédures d’optimisation sont requises pour résoudre le système. Le but de cette thèse était d’explorer l’effet de changer l’objectif de cette optimisation à des fonctions inspirées par la stabilité du genou par l’entremise d’un cadre de simulation de dynamique directe et inverse utilisant MatLab et OpenSim ainsi qu'un model musculo-squelétaire contraint cinématiquement par des données expérimentales dérivées de vis intra-corticales, sur les prédictions d’activation musculaire de la jambe. Les résultats de ce projet suggèrent que les contraintes de mouvement imposées sur le genou modélisé ont démontré des effets importants sur la cinématique de la jambe et conséquemment sur les prédictions d'activation musculaire et le chargement du genou. La fonction objective de l'optimisation change aussi les prédictions d’activations musculaires, bien que la fonction minimisant la consommation énergétique soit la plus juste, du moins pour la marche.

Joint center location is the driving parameter for determining the kinematics, and later kinetics, associated with human motion capture. Therefore the accuracy with which said location is determined is of great import to any and all subsequent calculation and analysis. The most significant barrier to accurate determination of this parameter is soft tissue artifact, which contaminates the measurements of on-body measurement devices by allowing them to move relative to the underlying rigid bone. This leads to inaccuracy in both bone pose estimation and joint center location. The complexity of soft tissue artifact (it is nonlinear, multimodal, subject-specific, and trial specific) makes it difficult to model, and therefore difficult to mitigate. This thesis proposes a novel method, termed Single Frame Optimization, for determining joint center location (though mitigation of soft tissue artifact) via a linearization approach, in which the optimal vector relating a joint center to a corresponding inertial sensor is calculated at each time frame. This results in a time-varying joint center location vector that captures the relative motion due to soft tissue artifact, from which the relative motion could be isolated and removed. The method’s, and therefore the optimization’s, driving assumption is that the derivative terms in the kinematic equation are negligible relative to the rigid terms. More plainly, it is assumed that any relative motion can be assumed negligible in comparison with the rigid body motion in the chosen data frame. The validity of this assumption is investigated in a series of numerical simulations and experimental investigations. Each item in said series is presented as a chapter in this thesis, but retains the format of a standalone article. This is intended to foment critical analysis of the method at each stage in its development, rather than solely in its practical (and more developed) form.

Biomechanics research investigating the knee kinematics of youth participants is very limited. The most accurate method of measuring knee kinematics utilizes invasive procedures such as bone pins. However, various experimental techniques have improved the accuracy of gait kinematic analyses using minimally invasive methods. In this study, gait trials were conducted with two participants between the ages of 11 and 13 to obtain the knee flexion-extension (FE), adduction-abduction (AA) and internal-external (IE) rotation angles of the right knee. The objectives of this study were to (1) conduct pilot experiments with youth participants to test whether any adjustments were necessary in the experimental methods used for adult gait experiments, (2) apply a Triangular Cosserat Point Element (TCPE) analysis for Soft-Tissue Artifact (STA) correction of knee kinematics with youth participants, and (3) develop a code to conduct a Principal Component Analysis (PCA) to find the PCA-defined flexion axis and calculate knee angles with both STA and PCA-correction for youth participants. The kinematic results were analyzed for six gait trials on a participant-specific basis. The TCPE knee angle results were compared between uncorrected angles and another method of STA correction, Procrustes Solution, with a repeated measures ANOVA of the root mean square errors between each group and a post-hoc Tukey test. The PCA-corrected results were analyzed with a repeated measures ANOVA of the FE-AA correlations from a linear regression analysis between TCPE, PS, PCA-TCPE and PCA-PS angles. The results indicated that (1) youth experiments can be conducted with minor changes to experimental methods used for adult gait experiments, (2) TCPE and PS analyses did not yield statistically different knee kinematic results, and (3) PCA-correction did not reduce FE-AA correlations as predicted.

Human motion capture technology is a powerful tool for advancing the understanding of human motion biomechanics (Andriacchi and Alexander, 2000). This is most readily accomplished by applying retroreflective markers to a participant’s skin and tracking the position of the markers during motion. Skin and adipose tissue move independently of the underlying bone during motion creating error known as soft tissue artifact (STA), the primary source of error in human motion capture (Leardini et al., 2005). (Solav et al., 2014) proposed and (Solav et al., 2015) expanded the triangular Cosserat point element (TCPE) method to reduce the effect of STA on derived kinematics through application of a marker cluster analyzed as a set of triangular Cosserat point elements. This method also provides metrics for three different modes of STA. Here the updated TCPE method (Solav et al., 2015) was compared to the established point cluster (PC) method of (Andriacchi et al., 1998) and the marker position error minimizing Procrustes solution (PS) method of (Söderkvist and Wedin, 1993) in two implant-based simulations, providing quantitative measures of error, and standard gait analysis, providing qualitative comparisons of each method’s determined kinematics. Both of these experiments allowed the TCPE method to generate observed STA parameters, informing the efficacy of the simulation. The TCPE method’s performance was similar to the PS method’s in the implant simulations and in standard gait. The PC method’s results seemed to be affected by numerical instability: simulation trial errors were larger and standard gait results were only similar to the other methods’ in general terms. While the PS and TCPE results were comparable, the TCPE method’s physiological basis provided the added benefit of non-rigid behavior quantization through its STA parameters. In this study, these parameters were on the same order of v magnitude between the standard gait experiments and the simulations, suggesting that implant simulations could be valuable substitutes when invasive methods are not available.

Studies have shown that modeling errors due to unaccounted for soft-tissue deformations – known as soft-tissue artifact (STA) – can reduce the efficacy and usefulness of musculoskeletal simulations. Recent work has proven that adding degrees of freedom (DOF) to the joint definitions of a musculoskeletal model’s lower limbs can significantly change the prediction of an individual’s kinematics and dynamics while simultaneously improving estimates of their mechanical work. This indicates that additional modeling complexity may mitigate the effects of STA. However, it remains to be determined whether adding DOF to the lower limb joints can impact a model’s satisfaction of Newton’s Second Law of Motion, or whether a specific number of DOF must be incorporated in order to produce the most biomechanically accurate simulations. To investigate these unknowns, I recruited ten subjects of variable body-mass-indices (BMI) and recorded subject walking data at three speeds normalized by Froude number (Fr) using optical motion capture and an instrumented treadmill (eight male, two females; mean ± s.d.; age 21.6 ± 2.87 years; BMI 25.1 ± 5.1). Then, I added DOF to the lower limb joints of OpenSim’s 23 DOF lower body and torso model until it minimized the magnitude of the pelvis residual forces and moments for a single, representative subject trial (BMI = 24.0, Fr = 0.15). These artificial residual forces and moments are applied at the pelvis to maintain the model’s orientation in space by satisfying Newton’s Second Law. Finally, I simulated all 30 trials with both the original and the edited model and observed how the biomechanical predictions of the two models differed over the range of subject BMIs and walking speeds. After applying both the original and the edited model to the entire data set, I found that the edited model resulted in statistically lower (α = 0.05) residual forces and moments in four of the six directions. Then, after investigating the impact of changes in BMI and Froude number on these residual reductions, I found that two of the six directions exhibited statistically significant correlations with Froude number while none of the six possessed correlations with BMI. Therefore, adding DOF to the lower limb joints can improve a model’s dynamic consistency and combat the effects of STA, and simulations of higher speed behaviors may benefit more from additional DOF. For BMI, it remains to be determined if a higher BMI indicates greater potential for residual reduction, but it was shown that this method of tuning the model for one representative subject was agnostic to BMI. Overall, the method of tuning the model for one representative subject was found to be quite limited. There were multiple subject trials for which reduced residuals corresponded to drastic changes in kinematic and dynamic estimates until they were no longer representative of normal human walking. Therefore, it is possible to improve dynamic consistency by adding DOF to the lower limb joints. But, for biomechanically relevant estimates to be consistently preserved and soft-tissue artifact to be completely minimized, subject-specific model tuning is likely necessary. / Mechanical Engineering