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Biomechanical models are used in many situations to help understand the risks associated with performing different work tasks. A necessary input to most biomechanical models is the body posture of the worker. Measuring posture has proven to be a difficult and time-consuming process. The research reported in this thesis investigated if a posture can be predicted instead of measured. The posture prediction model employs a whole-body sagittal plane representation of the worker with five links using inverse kinematic procedures to calculate the postures. The model chooses a posture by optimizing an objective function using a nonlinear programming search. Three separate models have been formulated to predict the postures of 16 subjects humans performing four static sagittal lifting tasks. Each model uses a different objective function or criterion defined relative to the torques on the human joints. These criteria are labeled as Total Torque, Percent Strength, and Balance. The influence of gender, hand position, and criteria on the prediction accuracy were investigated. The results showed that there was less postural variability for higher hand positions compared to lower hand positions. For lower hand positions there were two distinct types of postures chosen by subjects which implies that there are two different types of criteria being used by subjects at these hand positions. Student t tests, which investigated the accuracy of the predictions, showed that all of the prediction errors were significantly greater than zero at α=0.05. A mixed factor, repeated measures ANOVA investigating the prediction error showed that the Total Torque criterion was more accurate than the two other criteria. / Master of Science

Biomechanical models have been used in designing human work environments to evaluate potential risks to workers before a work environment is constructed. In order for work environments to be modeled correctly, most biomechanical models require as input, an accurate body posture of the worker. This information can be obtained by, either measuring the posture of workers for the task of interest, or estimating the posture. This research explores methods to estimate working postures by developing a model that can predict a worker's posture. The model in this thesis represents the body of the worker with ten links: neck, left and right forearms, left and right upper arms, body, left and right thighs, and left and right calves. The work task inputs consist of the magnitude and direction of the force applied to the hands, and the distances between the hands and the floor. By using these inputs, the model can predict a posture by optimizing an objective function of two criteria: Total Squared Moment and Balance. Model constraints also ensure that a predicted posture is feasible for human. The output of the model is the predicted posture in terms of ten body joint angles: neck, left and right elbows, left and right shoulders, hip, left and right knees, left and right ankles. These joint angles are defined as angles relative to horizontal. The prediction posture can be used as a base reference when inputting into other biomechanical models. By predicting posture from the model, one can obtain postures of the workers without direct measurement of postures from the workers, which can be expensive and time consuming. / Graduation date: 2002