Despite the squat jump's intriguing dynamical properties and prevalence in athletics, there is a lack of information regarding the comprehensive functional role of muscles during the squat jump. To increase our understanding of the strategies the human body uses in accelerating joints and contributing energy to body segments, we incorporated experimental data from trained collegiate men and women into musculoskeletal computer simulations. We evaluated the simulations to determine fundamental coordination principles of the squat jump, and the effect of increased loading and gender on muscle strategies employed during the squat jump. Our results revealed that the plantar flexors and vasti were primarily involved in increasing the mechanical energy of the body, while the proximal muscles were primarily involved in redistributing energy throughout the body. The erector spinae muscles extended the lumbar spine, and contributed energy to the torso, while gluteus maximus and hamstrings extended the hip joint, and contributed energy to the pelvis. The vasti extended the knee joint, and contributed energy to the pelvis and torso. Our results suggested that the rectus femoris plays a critical role in converting rotational energy into vertical kinetic energy. Greater barbell loads reduced the rate of lumbar extension, and resulted in increased normalized energy contributions from soleus and vasti to the torso. When comparing the squat jumps between men and women, our results suggested that soleus and vasti are more active in men than women during the body-weight squat jump. / text

Low back pain (LBP) is a common condition that affects all age groups and sexes. Although the development of LBP is multifactorial, the performance of lifting-based manual material handling (MMH) tasks are recognized as a primary risk factor. Many occupations that involve MMH tasks are performed in hazardous environments, where personal protective equipment (PPE) must be worn. Among the most commonly prescribed forms of PPE in Canada are CSA Grade 1 steel-toed work boots. According to the hazards present on the jobsite, workers may need to wear steel-toed work boots with/without a metatarsal guard or be able to wear steel-toed shoes (no upper). However, the amount of research on the interaction between protective footwear and human motion is limited. Therefore, the purpose of this thesis was to assess the effects of steel-toed shoes (unlaced), steel-toed boots (work boot), and steel-toed boots with a metatarsal guard (MET) on lifting mechanics. Specifically, three-dimensional kinematics of the lower limbs and trunk, sagittal net reaction moments of the low back, and local dynamic stability (LDS) of the lower limbs, lower back, and upper back were analyzed. Twelve males and 12 females were recruited to participate in this research project. Participants performed a repetitive lifting task at 10% of their maximum back strength, under three block-randomized footwear conditions. Ankle dorsiflexion was negatively affected by footwear type, where dorsiflexion was reduced the most in the MET condition compared to the unlaced condition (p < 0.01). However, there were no other main effects of footwear type on any other variable tested, and both male and female participants were able to maintain similar lifting mechanics and LDS values when moving up the kinematic chain. It is possible that participants were able to preserve their kinematics and stability through the appropriate recruitment of muscles, which may have implications for an increase in compressive and shear force on the spine and should be explored further in the future.

The goal of this research work was to introduce the whole process of developing and validating a 3D lower extremity musculoskeletal model and to test the ability of the model to predict the muscles recruitment of the different muscles involved in human locomotion as well as determining the corresponding forces and moments generated around the different joints in the lower extremity. Therefore the model can be applied in one of the important fields of orthopaedics which is joint replacement; the case study used in such application is the total knee replacement. The knee reaction forces were compared to the pattern obtained by Harrington (1992), where the hip moment components (Flexion/extension, internal/external, and abduction/adduction) were all compared to the patterns obtained from the Hip98 data base. It was shown in the different graphs of joints forces and moments that the model was able to produce very close results when comparing pattern and magnitude to the literature data. Thus, this 3D biomechanical model is sophisticated enough to be used for surgery evaluation such as in total knee replacement, where the damaged cartilage and bone are removed from the surface of the knee joint and replaced with a man-made. The case study of the second part of the research work presented involved the comparison of the gait pattern between two main knee joint types, Metallic and Allograft knee joints against normal subjects (Control group). A total of fifteen subjects participated in this study, five subjects in each group. It was concluded that based on the study conducted and the statistical evidence obtained that the introduced model can be used for applications that involves joint surgeries such as knee replacement that ultimately can be utilized in surgery evaluation.

Tremor affects millions of people and many patients desire alternative treatment options to medication or neural surgery. Peripheral suppression techniques are gaining greater use, but are currently applied in a trial-and-error method. To optimize these techniques, the muscles most responsible for an individual patient's tremor need to be identified. In this dissertation, I explored two parallel paths that both could aid in identifying muscles responsible for tremor. The first method utilizies measured data and a technique (coherence) that quantifies the frequency dependent correlation between two signals. Using coherence to identify muscles contributing to tremor requires at least two parts: an analysis of how tremor content is shared between muscles, and an analyis between muscle activity and joint/hand motion. The interpretation of the second analysis depends on the results of the first. The second method of identifying responsible muscles uses a mathematical model of the upper limb. With a validated model established techniques can be used to quantify the contribution to the output from each input. However, the accuracy of the model that has been previously used in the Neuromechanics Research Group had not been quantified. To evaluate the accuracy of this model, I used measured muscle activity as the input to generate simulated tremor and compared that to the measured tremor. From the first method, I found that synergistic muscles tend to share tremor content and do so in phase with each other. Therefore, tremor is likely due to a group of muscles rather than a single muscle. Additionally, I observed that the elbow flexor and wrist extensor muscles tended to be most correlated with tremor and should therefore be considered in peripheral suppression techniques. The second method revealed that while this upper-limb model shows potential to predict cases of severe tremor, improved model parameters must be identified through measurement or estimation techniques before the model should be used as it currently over-predicts the tremor.

People with osteoarthritis (OA) suffer from joint degeneration and pain as well as difficulty performing daily activities. Joint contact forces (JCF) are important for understanding individual joint loading, however, these contact force cannot be directly measured without instrumented implants. Musculoskeletal modeling is a tool for estimating JCF without the need for surgery. The results from these models can be very different due to different approaches used in the development of a model that was used for simulation. Therefore, the first purpose of this study was to develop and validate a musculoskeletal model in which lower extremity JCF were calculated at the hip, knee, and ankle in 10 participants with hip OA (H-OA) and 10 healthy control participants using OpenSim 4.0 [simtk.org, 23]. The generic gait2392 model was scaled to participant demographics, then the inverse kinematics (IK) solution and kinetic data were input into the Residual Reduction Algorithm (RRA) to reduce modeling errors. Kinematic solutions from RRA were used in the Computed Muscle Control (CMC) tool to compute muscle forces, then JCF were estimated using the Joint Reaction Analysis tool. Validation included JCF comparisons to published data of similar participant samples during level walking, and movement simulation quality was assessed with residual forces and moments applied at the pelvis, joint reserve actuators, and kinematic tracking errors. The computed JCFs were similar to the overall trends of published JCF results from similar participant samples, however the values of the computed JCFs were anywhere from 0.5 times body weight (BW) to 3BW larger than those in published studies. Simulation quality assessment resulted in low residual forces and moments, and low tracking errors. Most of the reserve actuators were small as well, besides pelvis rotation and hip rotation. The computed JCF were then used in the second portion of this study to determine the effect of group and side on JCF during both the weight acceptance and push-off phases of level walking. It was determined that there was a significant difference in the knee and ankle JCF during the weight acceptance portion of stance phase and at all joints during the push-off phase when comparing the H-OA and control groups on the affected limb. A significant interaction between group and limb was found for the peak hip JCF timing (% stance) during the push-off portion of the stance phase (p=0.009). These results demonstrate that H-OA participants experience an earlier peak hip JCF during propulsion on their affected limb. Based on previous research in OA that has examined spatiotemporal measures, this finding suggests that H-OA participants may use step or stride length changes as a strategy to decrease or limit pain and loading on the affected limb. Knowledge of potential JCF differences in H-OA participants, such as timing of the peaks in either portion of the stance phase, could provide useful insight to clinicians and therapists to make decisions on how to proceed with treatment or rehabilitation programs. / Master of Science / People with osteoarthritis suffer from joint degeneration and pain as well as difficulty performing daily activities, like walking. It is important to understand the forces and loading within individual joints. Musculoskeletal modeling is one way that researchers can estimate these joint contact forces (JCF) without needing a joint replacement implant that can measure these forces. When it comes to modeling simulations, there is a wide variety of results. Therefore, the first purpose of this study was to develop and validate a musculoskeletal model in which JCFs were calculated at the hip, knee, and ankle in 10 participants with hip osteoarthritis and 10 healthy adults. Validation of the model was completed through a comparison between computed results and published data of similar participant samples during level walking. The computed results were similar to the overall trends of published JCF results, however the numerical values themselves were larger than those in published studies. The computed JCFs were then used in the second portion of this study to determine how the two groups and limbs differ during level walking. There was a significant difference in the knee and ankle JCF during the first half of the stance phase and in all joints during the second half of stance when comparing the two groups. The hip osteoarthritis participants also experience an earlier peak hip JCF during the second half of stance phase on their affected limb. This finding suggests that hip osteoarthritis participants may change the way they take a step as a strategy to decrease or limit pain and loading on the affected limb. Knowledge of potential JCF differences, such as timing of the peaks in either portion of the stance phase, could provide useful insight to clinicians and therapists to make decisions on how to proceed with treatment or rehabilitation programs.

We describe a semi-automatic technique for modeling and animating complex musculoskeletal systems using a strand based muscle model. Using our interactive tools, we are able to generate the motion of tendons and muscles under the skin of a traditionally animated character. This is achieved by integrating the traditional animation pipeline with a biomechanical simulator capable of dynamic simulation with complex routing constraints on muscles and tendons. We integrate our musculoskeletal modeling and animation toolkit into a professional 3D production environment, thereby enabling artists and scientists to create complex musculoskeletal systems that were previously inaccessible to them. We demonstrate the applications of our tools to the visual effects industry with several animations of the human hand and applications to the biomechanics community with a novel model of the human shoulder.

We describe a semi-automatic technique for modeling and animating complex musculoskeletal systems using a strand based muscle model. Using our interactive tools, we are able to generate the motion of tendons and muscles under the skin of a traditionally animated character. This is achieved by integrating the traditional animation pipeline with a biomechanical simulator capable of dynamic simulation with complex routing constraints on muscles and tendons. We integrate our musculoskeletal modeling and animation toolkit into a professional 3D production environment, thereby enabling artists and scientists to create complex musculoskeletal systems that were previously inaccessible to them. We demonstrate the applications of our tools to the visual effects industry with several animations of the human hand and applications to the biomechanics community with a novel model of the human shoulder.

We describe a semi-automatic technique for modeling and animating complex musculoskeletal systems using a strand based muscle model. Using our interactive tools, we are able to generate the motion of tendons and muscles under the skin of a traditionally animated character. This is achieved by integrating the traditional animation pipeline with a biomechanical simulator capable of dynamic simulation with complex routing constraints on muscles and tendons. We integrate our musculoskeletal modeling and animation toolkit into a professional 3D production environment, thereby enabling artists and scientists to create complex musculoskeletal systems that were previously inaccessible to them. We demonstrate the applications of our tools to the visual effects industry with several animations of the human hand and applications to the biomechanics community with a novel model of the human shoulder. / Science, Faculty of / Computer Science, Department of / Graduate

Two-thirds of repetitive strain injuries affect the wrist joint. Although force is believed to be one of the major factors, the forces involved in wrist movements have not been thoroughly characterized in vivo. Computer simulations with a musculoskeletal model of the wrist have been used to estimate wrist muscle forces, but only at maximum voluntary contraction and only involving a single degree of freedom (DOF). In this study we present a method for creating a subject-specific model that can be used to estimate muscle forces and joint torques in both degrees of freedom of the wrist over a range of torques applicable to activities of daily living. Ten young, healthy subjects applied three levels of isometric wrist torque (about 7, 15, and 25% of maximum torque) in combinations of wrist flexion-extension and radial-ulnar deviation while joint torque in both DOF and surface electromyograms (sEMG) in the five major wrist muscles were measured. To find subject-specific parameters, we followed a two-step process. First, a pre-existing, generic musculoskeletal model of the wrist was scaled to individual subjects' height. Second, we compared joint torques predicted from measured sEMG using forward simulations of muscular dynamics to measured torques and minimized this error to optimize for subject-specific model parameter values. The model parameters optimized were the maximum isometric force and tendon slack length of each muscle. Optimization constraints were added to ensure physiologically plausible combinations of parameter values. The optimization produced model parameters that 1) were in a reasonable physiological range for each test subject and 2) significantly improved the accuracy of the model’s torque estimation. Scaling the generic model reduced the root mean squared (RMS) error between predicted and measured joint torques by 2.8±4.6% (mean±SD), whereas optimizing the scaled model further reduced the RMS error by 51.4±18.9% for the torque level at which the model was optimized. Testing the optimized model at other torque levels still significantly reduced the error between predicted and measured torques compared to the scaled model (43.7±28.0% and 25.0±24.0% for lower and higher torque levels, respectively). The mean error between predicted and measured torque was 0.23±0.04, 0.30±0.04, and 1.17±0.26 Nm at the low-, mid-, and high-torque levels, respectively. The method generally reduced the error in flexion-extension (FE) more than radial-ulnar deviation (RUD), likely in part because sEMG and torque were larger in FE than in RUD. Optimizing for subject-specific model parameters significantly improved prediction over both the generic and scaled models, in both degrees of freedom of the wrist, and at all three torque levels. The presented method for creating subject-specific models can be used in future studies to quantify muscle forces and joint torques of natural wrist movements in vivo.

L'étude de la biomécanique du geste sport a pour double objectif d'améliorer la performance et de minimiser le risque de blessures. De multiples études ont appliqué les principes de la biomécanique au mouvement du swing de golf. Toutefois, des biais méthodologiques et un manque de consensus ont conduit à des résultats parfois contradictoires. Ainsi, l'objectif premier de cette thèse a été d'effectuer une analyse exhaustive de l'état de l'art de la biomécanique appliquée au swing de golf. Le second objectif a été de concevoir et de mettre en place un protocole expérimental qui permette l'analyse du swing de golf. Et le troisième objectif a été de développer les modèles et analyses permettant l'étude du swing de golf. Cette partie a été effectuée à l'aide du développement d'un modèle musculo-squelettique via le logiciel OpenSim.34 joueurs ont été analysés dans le cadre de cette thèse. Les analyses ont porté sur de multiples éléments : géométriques, cinématiques, dynamiques et énergétiques. Différents critères ont été pris en compte, dont certains couramment pris en compte dans l'analyse du swing de golf (e.g. X-factor), dans l'analyse du mouvement (e.g. dynamique articulaire), mais aussi de nouveaux critères tels que le moment moteur ou le moment cinétique moteur.Des perspectives en termes de transferts vers les équipes médicales et les entraîneurs ont été proposées. / Studying biomechanics aspects of sport movements is aiming at improving performance and reducing injury risk. Several studies have used biomechanics concepts to study golf swing. However, several methodological biases and the lack of consensus have driven to contradictions. Thus, the first objective of this thesis was to do an exhaustive literature review of the biomechanics applied to golf swing. The second objective was to establish an experimental protocol for studying golf swing. And the third objective was to develop models and analysis to study golf swing. This part was done by a musculoskeletal model developed with OpenSim software.34 golf players have been analyzed for this thesis. Those analyses tackled multiples aspects : geometries, kinematics, kinetics and energetics. Several criteria have been taken into account, some from golf swing analysis (e.g. X-factor), from movement analysis (e.g. articular kinetics) and some new ones such as motor moment and motor kinetic moment.Perspectives for transferring results to medical staff and coaches were proposed.