Browsing by Subject "Musculoskeletal system--Computer simulation"
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Item A computational musculoskeletal model of the human elbow and forearm in the analysis of ballistic movements(1994) Gonzalez, Roger Valles, 1963-; Barr, Ronald E., 1946-; Abraham, Lawrence D.This dissertation describes the development and evaluation of a musculoskeletal model of the elbow joint complex (EJC) that represents human elbow flexion-extension and forearm pronation-supination. This study showed that EJC movements can be accurately modeled when the kinematics, kinetics, musculotendon characteristics, and muscle excitation patterns are precisely represented. Eight musculotendon actuators were represented at the EJC. The length, velocity, and moment arm for each of the eight musculotendon actuators were based on skeletal anatomy and joint position. Previously determined musculotendon parameters and skeletal geometry were utilized in the musculoskeletal model for the analysis of ballistic (rapid-directed) elbow joint complex movements. The key objective was to develop a computational model, guided by parameterized optimal control, to investigate the relationship among patterns of muscle excitation and kinematics, and to determine the effects of forearm and elbow position on the recruitment of individual muscles during a variety of ballistic movements. An extension of this model also characterized the function and movement of the elbow and forearm system by using processed electromyography (EMG) as the model driver. The model was verified using data from two volunteer subjects performing sixteen tasks that involved combinations of ballistic elbow flexion-extension and forearm pronation-supination. The testing and evaluation stage included electrogoniometer recordings of arm kinematics in conjunction with calibrated EMG recordings for numerical analysis. The modeling results showed (1) muscles which cross the EJC are affected in their recruitment by the orientation of the joint, (2) no fixed synergistic muscle recruitment was observed, (3) recruitment of the flexor muscles are most affected by the orientation of the EJC while recruitment of the extensor muscles are least affected, (4) Anconeus muscle activation likely represents its role in stabilization of the EJC, (5) for particular movements, the model results showed how it could be advantageous to activate a muscle that is antagonist to the movement desired in order to further minimize the final movement time, (6) triphasic patterns were demonstrated in the solutions predicted, and (7) an EMG signal processing scheme provided not only the trend but also the general magnitude of EJC movements when used to drive the modelItem A neuromusculoskeletal tracking method for estimating muscle forces in human gait from experimental movement data(2007) Seth, Ajay; Pandy, Marcus G.; Diller, K. R. (Kenneth R.)The research results contained in this dissertation relate to a novel approach to estimating individual muscle forces in human movement by exploiting typical experimental observations acquired in movement laboratories. A neuromusculoskeletal model is made to move as observed and exert the same forces on the environment as recorded in the laboratory. Electrical activity of muscles can also be used to guide the solution process such that in the end, the muscle activity of the model is in better agreement with these recordings while still producing the desired movement. The innovation of this process is the efficient combination of inverse and forward analysis techniques. These classical techniques combined with nonlinear control theory form the basis of a neuromusculoskeletal tracking methodology for systematically replicating human performance in a computer model. The purpose is to capitalize on the non-invasive nature of this methodology to extract internal information about muscle forces and subsequent bone and soft-tissue loads during human movement. This information is sought by orthopedic surgeons and movement scientists alike in order to determine the function of individual muscles and to understand what interventions/treatments may be the most effective at restoring function and comfort to their patients. This treatise has accomplished three primary objectives: 1) it provides the detailed development of a non-invasive method for estimating muscle forces that includes complete system dynamics and is computationally tractable; 2) performs a benchmark analysis to validate the increased accuracy and computational advantages of the tracking approach, and 3) applies neuromusculoskeletal tracking to one of the most challenging problems in biomechanics, which is human gait simulation and analysis. In reaching these objectives four principle findings were made. 1) Tracking has provided results that are superior to previous dynamic optimization methods and at 3 to 4 orders of magnitude savings in computational costs, with the relative savings increasing with model complexity. 2) When random and systematic error/noise is present in kinematic data (due to skin movement, sampling, environmental interference, and data processing techniques), then ground reaction forces are better predictors of the true movement of the system. Under these circumstances, closely tracking experimentally estimated model kinematics is insu cient to demonstrate movement accuracy and ground reaction forces must be closely duplicated to indicate accuracy. 3) Because of its relative speed, neuromusculoskeletal tracking has proven to be a powerful validation tool since poor results or even tracking failure occurs if the model is not adequately representative of the subject data. Therefore, models must be evolved until the desired accuracy is obtained. 4) Controller weightings can further improve simulation accuracy by tracking certain reference data (such as ground reaction forces) more closely than others (i.e. motion of the toes). However, obtaining the set of weightings that balance tracking accuracy across multiple references is not a trivial task especially when there are a large number of reference signals to consider. Although improvements in tracking accuracy can be obtained by the optimization of weightings, they may not justify the high computational cost.