Thesis (Ph.D)--Civil and Environmental Engineering, Georgia Institute of Technology, 2006. / Committe Chair: Terry W. Sturm; Committe Members: Dr. Fotis Sotiropoulos; Committee Members: Dr. Philip J. Roberts; Committee Members: Dr. Donald R. Webster; Committee Members: Dr. Anthony Hayter. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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This thesis examines the statistical properties of the Poisson AR(1) model of Al-Osh and Alzaid (1987) and McKenzie (1988). The analysis includes forecasting, estimation, testing for independence and specification and the addition of regressors to the model. The Poisson AR(1) model is an infinite server queue, and as such is well suited for modeling short-term disability claimants who are waiting to recover from an injury or illness. One of the goals of the thesis is to develop statistical methods for analyzing series of monthly counts of claimants collecting short-term disability benefits from the Workers' Compensation Board (WCB) of British Columbia. We consider four types of forecasts, which are the k-step ahead conditional mean, median, mode and distribution. For low count series the k-step ahead conditional distribution is practical and much more informative than the other forecasts. We consider three estimation methods: conditional least squares (CLS), generalized least squares (GLS) and maximum likelihood (ML). In the case of CLS estimation we find an analytic expression for the information and in the GLS case we find an approximation for the information. We find neat expressions for the score function and the observed Fisher information matrix. The score expressions leads to new definitions of residuals. Special care is taken to test for independence since the test is on the boundary of the parameter space. The score test is asymptotically equivalent to testing whether the CLS estimate of the correlation coefficient is zero. Further we define a Wald and likelihood ratio test. Then we use the general specification test of McCabe and Leybourne (1996) to test whether the model is sufficient to explain the variation found in the data. Next we add regressors to the model and update our earlier forecasting, estimation and testing results. We also show the model is identifiable. We conclude with a detailed application to monthly WCB claims counts. The preliminary analysis includes plots of the series, autocorrelation function and partial autocorrelation function. Model selection is based on the preliminary analysis, t-tests for the parameters, the general specification test and residuals. We also include forecasts for the first six months of 1995.

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The Ergonomics Research Group at Queen’s University, supported by the Workplace and Safety Insurance Board, has been developing an on-line system to estimate peak and cumulative joint loading in the workplace. This study will aid the project by examining the muscle activation levels (MALs) in upper extremity and trunk muscles during a manual lifting task using both hands. It was hypothesized that MAL’s are correlated with the magnitude of the load in the hands and thus could be used to predict the load which in turn will be used to predict the lower back moments. Alterations in the muscle activation patterns due to lifting different loads were examined. Electromyographic signals (EMG) and kinematic data were recorded from different sites on the trunk and upper limb as subjects lifted a load from the floor to a shelf using squat, stoop and freestyle lift techniques. All raw EMG data were processed to obtain the linear envelopes (LE) which provides estimates of the MAL’s. The peak, mean and area of the linear envelopes were calculated. Using regression analysis, a relationship between the parameters and load lifted was found to exist. A non-linear parallel cascade type architecture was used to develop a model to predict the load in the hands. The model uses the EMG parameters as inputs and fits the data via linear and non-linear cascades to the output, i.e. the load in the hands. A model was successfully developed for the squat lift posture using the area, peak and mean of the zero-normalized EMG LE recorded from the erector spinae (L4 level), with a prediction error of ± 1.03kg and for the stoop posture, a prediction error of ± 2.34kg. Given the predicted loads, moments in the lower back were computed using the method of Hof (1992). / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2007-09-28 16:15:23.077

The ability to accurately predict object weight is essential for skilled manipulation and recent studies suggest that such prediction is based, in part, on learned size-weight maps associated with families of objects. Weight prediction based on size-weight maps is also involved when judging weights; there is strong evidence that weight judgments are biased by expected weight, based on size. This bias is revealed by the size-weight illusion (SWI) whereby the smaller of two equally weighted and otherwise similar objects is judged to be heavier because it is heavier than expected based on its size. The overall aim of the current set of studies was to examine how size-weight maps for different families of objects are organized and represented at the perceptual and sensorimotor levels. We found that distinct and independent size-weight maps, used to predict weight, were used when lifting objects and judging their weights. At the perceptual level, interference between size-weight maps for the different sets of cubes was observed; participants could learn the inverted size-weight relationship for the green cubes when experienced alone but not when experienced along with the black cubes with a normal size-weight relationship. However, about half of participants learned to scale lift forces accurately for both sets of cubes indicating that the sensorimotor system can learn, without interference, opposite size-weight maps. We further investigated why not all participants learned to accurately scale their lift forces and found that learning to lift objects with different and arbitrary size weight maps involves visuomotor working memory resources. Moreover, an outside task that steals attentional resources can interfere even after previous learning of the size-weight maps. / Thesis (Master, Psychology) -- Queen's University, 2011-09-30 12:51:49.413

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