DOI

10.17077/etd.8hlm-4l3p

Document Type

Thesis

Date of Degree

Spring 2019

Access Restrictions

Access restricted until 07/29/2021

Degree Name

MS (Master of Science)

Degree In

Biomedical Engineering

First Advisor

Wilder, David G.

Second Advisor

Fethke, Nathan B.

First Committee Member

Grosland, Nicole M.

Abstract

Exposure to whole body vibration has been identified as a risk factor for the development of low back problems and exposure to frequent vertical impacts has been identified as a risk factor for both acute and chronic back injury. While there have been many prior studies into human response to both vibration and impacts, these studies have only examined them in isolation from one another, i.e. only sinusoidal vibration or impacts with no concurrent vibration. This does not reflect the environments in which occupational exposures take place and limits our ability to generalize these findings to the real world. The first obstacle in examining the interaction between vibration and impacts is the lack of any quantitative definition of what constitutes an impact within a vibrating environment.

To take the first steps toward creating this quantitative definition of an impact, we examined acceleration, posture, and erector spinae electromyography (EMG) data from farm vehicle operators as they completed routine farm tasks. We created several novel impact detection methods based upon our current understanding of human muscle response to impacts that analyze acceleration data and return the locations in the data at which an impact is believed to have occurred. These novel impact detection methods are the Thump, Womp, and Wiggle Methods. We compared their relative successes in predicting a substantial change in EMG activity immediately following an identified impact to that of a method that randomly selected points in the data, as determined by a novel locally-normalized muscle response evaluation method. We then created a series of generalized linear mixed models that included posture and subject-specific data to compare how the odds ratios between the quartiles of each predictor align with we would expect these predictors to affect the likelihood of an impact to trigger a muscle response.

We found that none of our novel impact detection methods predicted muscle response at an appreciably different rate than the random method. However, when posture and subject-specific predictors are introduced into generalized linear mixed models, we see statistical significance in how increases in the chest and lumbar angles affect the likelihood of a muscle response in impacts identified by the Thump Method (p<0.001). We also see statistical significance in how increases in the magnitude of the impact metric in impacts identified by the Wiggle Method increase the likelihood of an observed muscle response being observed (p<0.05).

We believe that the Thump and Wiggle Methods of impact identification described within this thesis together provide a foundation for the development of an ideal impact identification method for future studies into impacts within vibrating environments.

Keywords

agriculture, impact, safety, vibration

Pages

xiii, 108 pages

Bibliography

Includes bibliographical references (pages 102-108).

Copyright

Copyright © 2019 Shamus Kirkwood Roeder

Available for download on Thursday, July 29, 2021

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