An assessment of unmanned aircraft pilot discomfort and fatigue

• Main Author: DeBusk, John Hunter
• Other Authors: Kari Babski-Reeves
• Format: Others
• Language: en
• Published: MSSTATE 2018
• Subjects: Industrial and Systems Engineering
• Online Access: http://sun.library.msstate.edu/ETD-db/theses/available/etd-06252018-153404/

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 Captains (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.