Semester

Spring

Date of Graduation

2019

Document Type

Dissertation

Degree Type

PhD

College

Statler College of Engineering and Mineral Resources

Department

Industrial and Managements Systems Engineering

Committee Chair

Ashish Nimbarte

Committee Member

Gary Winn

Committee Member

Xiaopeng Ning

Committee Member

Xinjian He

Committee Member

Hongwei Hsiao

Abstract

Musculoskeletal disorders of the shoulder have a huge impact on the workplace and employees. Due to the complexity and high mobility of the shoulder, developing a task analysis tool with a focus on the shoulder is difficult, and while there are some ergonomic analysis tools that do consider the shoulder to varying degrees in the analysis, none focus purely on the shoulder. Therefore, this research was undertaken in an attempt to develop a shoulder-based task analysis tool for the shoulder. However, since the scope of work involving the shoulder is so vast, the tool in this research focuses on unobstructed, one-handed lifting tasks.

An initial evaluation utilizing correlation and root mean square error analysis was performed using motion capture and electromyography data from participants performing a lifting task with dynamic and static portions using 3 different weights. Each task was modeled in AnyBody Modeling System with each available muscle recruitment algorithm. Based on correlation and root mean square error analysis between the muscle activations from the model and the collected electromyography data, the Min/Max strategy was most appropriate for static tasks and Poly4 strategy for static exertions. These selected muscle recruitment algorithms were used to model the tasks performed by the participants through the other sections of this research.

Next, five participants performed static lifting tasks supporting a 15-pound weight throughout the reach zone of the right arm. These tasks were modeled in AnyBody using the Min/Max recruitment algorithm based on the results of the previous aim. Twelve potential composite index equations, designed to estimate shoulder strain based on AnyBody model outputs, were analyzed, three equations developed for this research and one previously-developed and validated equation. Correlation analysis between the results of each potential composite index and perceived exertions led toward the selection of one of the newly developed composite index equations, though the results were so close that the previously-developed equation was selected. Regression methods were used to develop a regression equation to predict the composite index values based on the distance to the load from the sternal notch.

Finally, the hypothesis that the strain of an unobstructed lifting task would be highest at either the origin or the destination of the lift was tested to determine if the strain on the shoulder for a lifting task could be estimated based on the origin and destination of the load. Five participants were recruited to perform lifting tasks between low-, medium-, and high-risk load locations, and the peak predicted strain throughout the trial was compared to the strain at the origin and destination. It was determined that, due to an initial lift from the origin accompanied by an arced lifting trajectory, the peak strain was often slightly higher than at the higher of the origin or destination.

Included in

Ergonomics Commons

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