Theses and Dissertations

Issuing Body

Mississippi State University

Advisor

Babski-Reeves, Kari

Committee Member

Chander, Harish

Committee Member

Burch, Reuben

Committee Member

Moorhead, Robert

Date of Degree

8-10-2018

Original embargo terms

Visible to MSU only for 3 years

Document Type

Dissertation - Open Access

Major

Industrial and Systems Engineering

Degree Name

Doctor of Philosophy

College

James Worth Bagley College of Engineering

Department

Department of Industrial and Systems Engineering

Abstract

The rapid growth of unmanned aircraft system (UAS) use in both the military and civil sectors has uncovered an array of challenges within the field. In terms of human factors and ergonomics, the influence of the unique physical design of the control stations used to pilot the unmanned aircraft on local muscular fatigue and discomfort are of great concern. This study was conducted to assess the influence of two display configurations, Side-by-Side (SS) and Stacked (ST), and two chairs, Ergonomic (EC) and Captain’s (CC), on mean and median power frequencies, root mean square amplitude, posture, discomfort, workload, and seat pressure. Sixteen participants [age: 24.75 ± 2.96 years; gender: 4 female/ 12 male; height: 177.56 ± 9.09 cm; weight: 81.37 ± 16.43 kg] completed four, 2-hour simulated UAS flights for all chair/display combinations. Eight participants piloted one, 6-hour simulated UAS flight in the display/chair combination which best minimized discomfort and fatigue in the two-hour flights, EC/SS. During the two-hour flights, muscle activity, discomfort, posture, workload, and seat pressure findings indicated increased muscular fatigue and discomfort over time. Generally, the EC/SS condition appeared to best mitigate muscular fatigue and postures associated with increased risk for the development of musculoskeletal disorders. Six-hour flight data failed to provide additional insights on the influence of extended duration flights on the dependent variables of this study. Finally, linear regression analysis revealed muscle activity can likely be predicted during UAS piloting tasks using the dependent variables in this study; however, the study failed to provide evidence that models built from two-hour data can accurately predict muscle activity out to six hours.

URI

https://hdl.handle.net/11668/21229

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