The purpose of this study was to evaluate the effects of same-day strength training on velocity and accuracy of a tennis serve among five female Division I intercollegiate athletes at the second and sixth week of a strength training program. Velocity and accuracy of 20 tennis serves were measured approximately four hours after a morning split-body (lower legs and trunk) strength training session termed the lift-day (LD) and were compared to measures taken on days that no lifting took place, termed the non-lift day (NLD). For each test day, velocity was multiplied by accuracy to provide an overall serve performance score for each NLD and LD. An ANOVA revealed that there was no significant difference in any of the measured variables between the NLD and the LD at any time period throughout the study. A mean serve performance score difference between NLD (77.56) and LD (78.05) of 0.49 was not statistically different [p = 0.84]. The results of this study suggest that female collegiate tennis players may strength-train with no significant effect on same-day serve performance following adequate recovery.

Two hypotheses were formulated to examine the additivity of perceived exertion in repetitive, symmetric, mid-sagittal lifting. "Additivity" has been defined as the means by which a whole-body rating of perceived exertion is composed of a weighted combination of component ratings of perceived exertion. The "task additivity" hypothesis asserts that a perceived exertion rating for the whole body in a floor-to-overhead lifting task can be modelled by the perceived exertion ratings of the component motions, i.e., floor-to-knuckle height lifting and knuckle height-to-overhead lifting. This is an inter-task (subtask) additivity paradigm. The "body-segment additivity" hypothesis asserts that the perceived exertion rating for the whole body in a floor-to-overhead lifting task can be modelled by a combination of the ratings of perceived effort from the arms, legs, torso, and central (cardio-respiratory) body functions. This is an intra-task (regional) additivity paradigm. / Master of Science

This study examined an existing industrial workstation at an automobile assembly plant using computer aided ergonomics and digital human models. The purpose of this evaluation was the development of a methodology useful for evaluating workstations to identify potential design issues that could result in musculoskeletal injury in a real work environment. An ergonomic risk assessment was conducted on a lifting task while being performed both manually and using an assist device. JACK digital human modeling and ergonomics software were used to conduct a computer-based ergonomic analysis. Four analysis tools in JACK (static strength analysis, rapid upper limb assessment, metabolic energy expenditure analysis and NIOSH lift analysis) were used to evaluate the potential injury risk of the current method of task performance and there is any difference between using and not using the assist device. Muscle activity was measured by electromyography (EMG) to identify physiological indicators of fatigue. Also, Borg¡¯s Rate of Perceived Exertion (RPE) scale was administered to obtain psychophysical data. Results of this study revealed that there were relative stresses on the trunk and arm areas when the task was performed manually. The results also suggest although using the assist device decreased injury risk potentially, use of the assist device had an adverse impact on the productivity of the assembly line. Based on the findings of this study, the methodology used appears to be an appropriate ergonomic analysis tool for assessing and predicting potential risks associated with the design of industrial workstations. Furthermore this methodology can be extended to designing and redesigning industrial workstations.

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Musculoskeletal pain and injury continue to be prevalent sources of disability for thousands of workers in the U.S. every year. Proactive approaches to the reduction of this incidence attempt to prevent the injury by effecting task design so that human capabilities and limitations are driving factors in the task design and analysis process. Knowledge about the posture and kinematics that might be employed by an individual in performing a task is an important element of these proactive approaches to task design and analysis, especially for manual materials handling (i.e., lifting) exertions. In turn, accurate models that predict posture and kinematics can reduce the need for empirical postural and kinematic data in this task development process. Artificial neural networks were used in this investigation to achieve these predictions. As input, these networks received information about lift characteristics (e.g. target location, movement duration) and returned a predicted set of joint angles. Two types of networks were created, one to predict static posture based on target position, the second to predict the time histories of several joint angles (i.e., kinematics) as an object is lifted or lowered. Initial networks were created for sagittally symmetric lifts (two dimensions), but the final set of networks was expanded to make predictions for symmetric and asymmetric lifts in three dimensions. Networks were trained and verified with an empirical set of non-cyclic lifting motions. Notably, the within-subject variability in these motions was similar in magnitude to the associated between-subjects variability. In general, the networks were able to assimilate the data relatively well, especially in predicting kinematics, where root mean square errors were typically smaller than 20 degrees. These errors were similar in magnitude to the levels of within-subject variability observed in the dataset. Network performance also compared favorably to other existing models, typically resulting in smaller prediction errors than these other approaches. In addition, the internal connections of trained networks were examined to infer hypothetical motor control strategies. Results of this examination showed that feedback was an important component in providing kinematic predictions, whereas posture prediction benefited greatly from knowledge about individual anthropometry. Finally, potential improvements to increase prediction accuracy are discussed. Overall, these results support the use of artificial neural network models to predict posture and kinematics for lifting tasks. / Ph. D.

Falls in the workplace are a major cause of injuries and fatalities. Muscle fatigue is one important factor that has been linked to a decrement in postural control and a potential increased falling risk. However, potential interactive effects of muscle fatigue with other risk factors remain unclear, and practical interventions are needed to mitigate the adverse effects of muscle fatigue. The current work was conducted to address these research needs through three experimental studies. The first study investigated how muscle fatigue affects postural control during quiet standing on inclined surfaces. Inclined surfaces compromised postural control, with the most deleterious effects found while standing in a lateral direction. Fatigue did not result in further decrements in postural control during standing on inclined surfaces. The second study investigated the effects of muscle fatigue on postural control while lifting unexpected loads. Lifting an object with unexpected mass compromised postural control, with a more substantial effect found in the unexpectedly light load condition. Fatigue-related effects were not consistent, though there was evidence that lumbar muscle fatigue did not compound the adverse effects of lifting an object in unexpected mass conditions. The last study evaluated the efficacy of three interventions (two auditory stimulations and periodic rest breaks) at mitigating the adverse effects of muscle fatigue on postural control. Allowance of rest breaks did not improve postural control during the fatiguing work, though it was indicated that benefits may be present for some individuals. Both a static pure tone and moving conversation appeared to offset fatigue-induced postural instability. The current research provides a more comprehensive understanding of the contribution of muscle fatigue to fall risks during occupationally relevant tasks and assessed the efficacy of practical interventions to reduce the risk of falls. These findings may facilitate the development of strategies to prevent occupational falls related to muscle fatigue, inclined surfaces, and manual material handling tasks. / Ph. D.

Master of Science / Department of Industrial & Manufacturing Systems Engineering / Todd W. Easton / Integer programs (IPs) are mathematical models that can provide organizations with the ability to optimally obtain their goals through appropriate utilization and allocation of available resources. Unfortunately, IPs are NP-complete in the strong sense, and many integer programs cannot be solved. Introduced by Gomory, lifting is a technique that takes a valid inequality and strengthens it. Lifting can result in facet defining inequalities, which are the theoretically strongest inequalities; because of this, lifting techniques are commonly used in commercial IP software to reduce the time required to solve an IP. This thesis introduces two new algorithms for exact simultaneous up lifting multiple sets into binary knapsack problems and introduces sequential simultaneous lifting. The Dynamic Programming Multiple Lifting Set Algorithm (DPMLSA) is a pseudopolynomial time algorithm bounded by O(nb) effort that can exactly uplift an arbitrary number of sets. The Three Set Simultaneous Lifting Algorithm (TSSLA) is a polynomial time algorithm bounded by O(n2) and can exact simultaneously up lift three sets. The simultaneously lifted inequalities generated by the DPMLSA and the TSSLA can be facet defining, and neither algorithm requires starting with a minimal cover. A brief computational study shows that the DPMLSA is fast and required an average of only 0.070 seconds. The computational study also shows these sequential simultaneously lifted inequalities are useful, as the solution time decreased by an overall average of 18.4%. Therefore, implementing the DPMLSA should be beneficial for large IPs.

Comparison kinematic models of the traditional style deadlift are presented. Data was obtained through film and analyzed via computer and computer graphics. The comparison between the models revealed that the greater-skilled: 1. used less trunk flexion from the instant of initial trunk lean to the instant of maximum trunk lean, 2. used less knee extension (in same time interval as 1), and 3. demonstrated a smaller horizontal distance between the body center of mass (CM) and the CM of the bar at the instant the bar left the platform. A trend was also observed in which the greater-skilled subjects demonstrated less thoracic lean than the lesser-skilled group at the time the bar reached knee level.