Low back disorders (LBDs) comprise one of the major health issues in the United States. Previous research used isometric studies to understand the mechanisms that cause LBDs. Occupational tasks involving dynamic trunk movements, muscle fatigue, and spinal instability are identified as major risk factors for developing low back pain. Dynamic stability and muscle forces during trunk flexion-extension movements are studied in this dissertation. Torso muscle fatigue is known to affect the neuromuscular muscle recruitment that influences spinal stability. The first part of this dissertation investigates the effect of muscle fatigue on the stability of dynamic trunk flexion-extension movements. Participants with no self-reported low back pain history performed repetitive trunk flexion-extension exercises before and after extensor muscle fatigue. The extensor muscles were fatigued to 60% of their unfatigued isometric maximum voluntary exertion force. The maximum finite-time Lyapunov exponent, λ_Max, was used to quantify the dynamic stability. Values of λ_Max increased with fatigue, suggesting dynamic stability of the torso decreases with muscle fatigue. Fatigue-by-task asymmetry interactions did not influence spinal stability. The purpose of the second part of this dissertation was to predict time-dependent muscle forces and spinal loads during symmetric flexion-extension movements. A 2-dimensional sagittal plane, lumped parameter model was built with one thorax and five lumbar vertebrae stacked upon a stationary pelvis. Kinematics driven optimization was used to estimate time-dependent muscle forces. Muscle forces were determined by minimizing the metabolic power while satisfying the equations of motion. Spinal loads were calculated as the vector sum of the muscle forces and the trunk weight. Abdominal activity was observed at the onset of flexion and at the end of extension. The multifidus and psoas muscles played a major role in the spine dynamics. The compressive spinal loads were found to reach highest values at the onset of flexion, while the shear loads reached the highest values at large flexion angles. / Ph. D.

Mathematical musculoskeletal modeling and simulation provide a means for proactive injury prevention. To be effective, these models must physiologically replicate shoulder function. Although several muscle force prediction (MFP) shoulder models exist, few have attempted to integrate the force contributions of ligaments, especially the glenohumeral ligaments. The purpose of the current study was to integrate seven shoulder ligaments into an existing computational shoulder model, and analyze both individual ligament characteristics and the influence on the model outputs. Using data from the literature, seven shoulder ligaments were integrated into the model: the costoclavicular, conoid, trapezoid, coracohumeral, superior glenohumeral, middle glenohumeral, and inferior glenohumeral. 10 subjects performed isometric exertions in 56 posture-force combinations. Upper body posture and hand force collected were used as inputs for three different model versions; No-Ligaments (NL) included, Glenohumeral-Ligaments (GH) included, and All-Ligaments (AL) included. Electromyographic (EMG) signals from 11 muscle sites were used for comparison with model MFPs. The primary analysis focused on the differences between the GH and NL versions. Normalized EMG amplitudes were plotted against normalized MFPs from both models. Ligament effects on model outputs were measured by comparing changes in correlation between EMG and MFP, changes in slopes regression lines relating EMG to MFP, and the frequency of zero-force prediction by the model. Paired Student’s t-tests were used to measure significant differences. Results showed significant correlations (Pearson product) between EMG amplitude and MFP in the lower trapezius and infraspinatus muscles (p<0.01). No significant differences were found in r-values for these muscles between the NL and GH model. Slopes of regression lines decreased when GH ligaments were added, while the change in zero-force predictions varied by muscle. This study highlights the sensitivity of musculoskeletal models to the inclusion of ligament forces. Though correlations did not change, decreases in slope indicate increased force prediction by the GH model. Though zero-force predictions for some muscles increased, the results from those that decreased suggest muscles are active in postures where they were originally believed to be inactive. This finding suggests that inclusion of GH ligaments into our model may help predict antagonist muscle activity. However, further research is required.

Mathematical musculoskeletal modeling and simulation provide a means for proactive injury prevention. To be effective, these models must physiologically replicate shoulder function. Although several muscle force prediction (MFP) shoulder models exist, few have attempted to integrate the force contributions of ligaments, especially the glenohumeral ligaments. The purpose of the current study was to integrate seven shoulder ligaments into an existing computational shoulder model, and analyze both individual ligament characteristics and the influence on the model outputs. Using data from the literature, seven shoulder ligaments were integrated into the model: the costoclavicular, conoid, trapezoid, coracohumeral, superior glenohumeral, middle glenohumeral, and inferior glenohumeral. 10 subjects performed isometric exertions in 56 posture-force combinations. Upper body posture and hand force collected were used as inputs for three different model versions; No-Ligaments (NL) included, Glenohumeral-Ligaments (GH) included, and All-Ligaments (AL) included. Electromyographic (EMG) signals from 11 muscle sites were used for comparison with model MFPs. The primary analysis focused on the differences between the GH and NL versions. Normalized EMG amplitudes were plotted against normalized MFPs from both models. Ligament effects on model outputs were measured by comparing changes in correlation between EMG and MFP, changes in slopes regression lines relating EMG to MFP, and the frequency of zero-force prediction by the model. Paired Student’s t-tests were used to measure significant differences. Results showed significant correlations (Pearson product) between EMG amplitude and MFP in the lower trapezius and infraspinatus muscles (p<0.01). No significant differences were found in r-values for these muscles between the NL and GH model. Slopes of regression lines decreased when GH ligaments were added, while the change in zero-force predictions varied by muscle. This study highlights the sensitivity of musculoskeletal models to the inclusion of ligament forces. Though correlations did not change, decreases in slope indicate increased force prediction by the GH model. Though zero-force predictions for some muscles increased, the results from those that decreased suggest muscles are active in postures where they were originally believed to be inactive. This finding suggests that inclusion of GH ligaments into our model may help predict antagonist muscle activity. However, further research is required.

<p>At every joint there is a redundant set of muscle activated during movement or loading of the system. Optimization techniques are needed to evaluate individual forces in every muscle. The objective in this thesis was to use static optimization techniques to calculate individual muscle forces in the human extremities.</p><p>A cost function based on a performance criterion of the involved muscular forces was set to be minimized together with constraints on the muscle forces, restraining negative and excessive values. Load-sharing, load capacity and optimal forces of a system can be evaluated, based on a description of the muscle architectural properties, such as moment arm, physiological cross-sectional area, and peak isometric force.</p><p>The upper and lower extremities were modelled in two separate studies. The upper extremity was modelled as a two link-segment with fixed configurations. Load-sharing properties in a simplified model were analyzed. In a more complex model of the elbow and shoulder joint system of muscular forces, the overall total loading capacity was evaluated.</p><p>A lower limb model was then used and optimal forces during gait were evaluated. Gait analysis was performed with simultaneous electromyography (EMG). Gait kinematics and kinetics were used in the static optimization to evaluate of optimal individual muscle forces. EMG recordings measure muscle activation. The raw EMG data was processed and a linear envelope of the signal was used to view the activation profile. A method described as the EMG-to-force method which scales and transforms subject specific EMG data is used to compare the evaluated optimal forces.</p><p>Reasonably good correlation between calculated muscle forces from static optimization and EMG profiles was shown. Also, the possibility to view load-sharing properties of a musculoskeletal system demonstrate a promising complement to traditional motion analysis techniques. However, validation of the accurate muscular forces are needed but not possible.</p><p>Future work is focused on adding more accurate settings in the muscle architectural properties such as moment arms and physiological cross-sectional areas. Further perspectives with this mathematic modelling technique include analyzing pathological movement, such as cerebral palsy and rheumatoid arthritis where muscular weakness, pain and joint deformities are common. In these, better understanding of muscular action and function are needed for better treatment.</p>

At every joint there is a redundant set of muscle activated during movement or loading of the system. Optimization techniques are needed to evaluate individual forces in every muscle. The objective in this thesis was to use static optimization techniques to calculate individual muscle forces in the human extremities. A cost function based on a performance criterion of the involved muscular forces was set to be minimized together with constraints on the muscle forces, restraining negative and excessive values. Load-sharing, load capacity and optimal forces of a system can be evaluated, based on a description of the muscle architectural properties, such as moment arm, physiological cross-sectional area, and peak isometric force. The upper and lower extremities were modelled in two separate studies. The upper extremity was modelled as a two link-segment with fixed configurations. Load-sharing properties in a simplified model were analyzed. In a more complex model of the elbow and shoulder joint system of muscular forces, the overall total loading capacity was evaluated. A lower limb model was then used and optimal forces during gait were evaluated. Gait analysis was performed with simultaneous electromyography (EMG). Gait kinematics and kinetics were used in the static optimization to evaluate of optimal individual muscle forces. EMG recordings measure muscle activation. The raw EMG data was processed and a linear envelope of the signal was used to view the activation profile. A method described as the EMG-to-force method which scales and transforms subject specific EMG data is used to compare the evaluated optimal forces. Reasonably good correlation between calculated muscle forces from static optimization and EMG profiles was shown. Also, the possibility to view load-sharing properties of a musculoskeletal system demonstrate a promising complement to traditional motion analysis techniques. However, validation of the accurate muscular forces are needed but not possible. Future work is focused on adding more accurate settings in the muscle architectural properties such as moment arms and physiological cross-sectional areas. Further perspectives with this mathematic modelling technique include analyzing pathological movement, such as cerebral palsy and rheumatoid arthritis where muscular weakness, pain and joint deformities are common. In these, better understanding of muscular action and function are needed for better treatment. / QC 20101116

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L'analyse musculo-squelettique est un outil de plus en plus utilisé dans les domaines d'application tels que l'ergonomie, la rééducation ou le sport. Cette analyse permet une estimation des efforts articulaires et des tensions musculaires mises en jeu au cours du mouvement. Les modèles et méthodes que cette analyse exploite conduisent à des résultats de plus en plus réalistes. Cela a pour conséquence de limiter les performances de ces logiciels : le temps de calcul augmente et il est nécessaire de mettre en place des protocoles ainsi que qu’un post-traitement des données long et complexe pour adapter les modèles au sujet. Enfin, de tels logiciels nécessitent une expertise importante des utilisateurs pour être pleinement opérationnels. Ces différents points limitent dans la plupart des cas l'utilisation de ces logiciels au domaine de la recherche.Dans l'objectif de démocratiser l'utilisation des analyses musculo-squelettiques, cette thèse propose des contributions permettant d’améliorer les performances de telles analyses en conservant un bon niveau de précision, ainsi que des contributions permettant une calibration spécifique au sujet des modèles facile à mettre en œuvre. Tout d'abord, dans un souci de maîtrise complète des outils de l’analyse du mouvement, cette thèse développe une approche globale sur l'ensemble des étapes qui la constitue : les étapes de cinématique, de dynamique et d'estimation des efforts musculaires. Pour chacune de ces étapes, des méthodes de résolution en temps rapide ont été proposées. Une méthode de résolution de la question de la répartition des efforts musculaires utilisant une base de données pré-calculée est notamment largement développée. De plus, un processus complet de calibration utilisant uniquement le matériel disponible dans une salle d'analyse de mouvement classique a été développé, où les données utilisées sont issues de capture de mouvement ainsi que de plateformes de force. / Musculoskeletal analysis becomes popular in applications fields such as ergonomics, rehabilitation or sports. This analysis enables an estimation of joint reaction forces and muscles tensions generated during motion. Models and methods used in such an analysis give more and more accurate results. As a consequence, performances of software are limited: computation time increases, and experimental protocols and associated post-process are long and tedious to define subject-specific models. Finally, such software need a high expertise level to be driven properly.In order to democratize the use of musculoskeletal analysis for a wide range of users, this thesis proposes contributions enabling better performances of such analyses and preserving accuracy, as well as contributions enabling an easy subject-specific model calibration. Firstly, in order to control the whole analysis process, the thesis is developed in a global approach of all the analysis steps: kinematics, dynamics and muscle forces estimation. For all of these steps, quick analysis methods have been proposed. Particularly, a quick muscle force sharing problem resolution method has been proposed, based on interpolated data. Moreover, a complete calibration process, based on classical motion analysis tools available in a biomechanical lab has been developed, based on motion capture and force platform data.

<p>Musculoskeletal degradation and musculoskeletal injuries place a substantial burden on the healthcare system. Advancing the understanding and prevention of the injury potential associated with these injuries in various demographics as well as advancing performance optimization requires knowledge of the loading distribution among the various musculoskeletal tissues at the joints. Accurate muscle force estimates are needed for characterizing these distributions due to their influence on the loading of the system. This dissertation discusses</p> <p>the development and validation of a physiologically-driven skeletal muscle force model that is suitable for application on an individualized level. The derivation of the skeletal muscle force model began with dimensional analysis and a selection of critical parameters that define muscle force generation. One of the key parameters included was measured muscle voltage using electromyography sensors. This provided the model with the ability to be easily used</p> <p>in application-based studies. It also incorporated the muscle force-length, force-velocity, and force-frequency curves, providing an even stronger physiological basis to the model. Validation was performed by multiple studies using experimental data from subjects conducting exercises chosen to target specific muscles of interest. Data was collected from a Vicon Vero motion capture system, an instrumented Bertec treadmill, and Delsys Trigno electromyography sensors. The first study analyzed the ankle joint of seventeen subjects using the two Newton-Euler equations of rigid body motion and the skeletal muscle force model. The average percent error across all subjects was 8.2% and ranged from 4.2% to 15.5%. The second study analyzed the sensitivity of two sets of parameters within the model. The first was conducted on a set of observed and fitted constants from the dimensionless pi terms and aimed to identify which, if any, could be excluded from an optimization routine. Results indicated that only two of the nine constant parameters needed to be optimized. The second sensitivity analysis focused on the anatomical kinematic parameters in order to identify the impact that the incorporation of MRI scans for subject-specific anatomical models would have on the accuracy of the model’s output. Results demonstrated sensitivity to the muscle insertion points, suggesting that the use of MRI scans could increase the accuracy of the model. The third study was a case study focused on evaluating the assumption of a constant within the skeletal muscle force model remaining constant over time. Results indicated that the collection of maximum EMG recordings for these studies may not have been controlled to a desirable level and that the inclusion of specialized equipment for maximum EMG recordings would likely validate this assumption. The final study analyzed the</p> <p>knee joint of ten subjects in a similar fashion to that of the ankle joint. The goal was to observe the model’s performance on a more anatomically complex joint. The average percent error across all subjects was 20.6%, approximately two times higher than the ankle joint.</p> <p>However, the majority of the error associated with this study came from the deviation in calculated moments about an axis of much smaller importance and magnitude than the primary flexion/extension axis. When errors were excluded from this axis, the average percent error for all subjects was 8.8%, almost identical to that of the ankle joint application. These findings as a whole indicate that the model has predictive ability and is capable of providing reasonable estimates of both muscle forces and surrounding musculoskeletal tissue loading. Therefore, the model could be used in various biomechanical advancements and applications in injury prevention, performance optimization, tissue engineering, prosthetic design, and more.</p>