ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- Creators: Cooke, Nancy J.
patient recovery time. It is a surgical procedure done by using long reached tools and an
endoscopic camera to operate on the body though small incisions made near the point of
operation while viewing the live camera feed on a nearby display screen. Multiple camera
views are used in various industries such as surveillance and professional gaming to
allow users a spatial awareness advantage as to what is happening in the 3D space that is
presented to them on 2D displays. The concept has not effectively broken into the
medical industry yet. This thesis tests a multi-view camera system in which three cameras
are inserted into a laparoscopic surgical training box along with two surgical instruments,
to determine the system impact on spatial cognition, perceived cognitive workload, and
the overall time needed to complete the task, compared to one camera viewing the
traditional set up. The task is a non-medical task and is one of five typically used to train
surgeons’ motor skills when initially learning minimally invasive surgical procedures.
The task is a peg transfer and will be conducted by 30 people who are randomly assigned
to one of two conditions; one display and three displays. The results indicated that when
three displays were present the overall time initially using them to complete a task was
slower; the task was perceived to be completed more easily and with less strain; and
participants had a slightly higher performance rate.
exponential rate, expected to alter the very foundations of our transportation system within the next 10-25 years. A crucial interaction has been born out this new technology: Human and automated drivers operating within the same environment. Despite the well- known dangers of automobiles and driving, autonomous vehicles and their consequences on driving environments are not well understood by the population who will soon be interacting with them every day. Will an improvement in the understanding of autonomous vehicles have an effect on how humans behave when driving around them? And furthermore, will this improvement in the understanding of autonomous vehicles lead to higher levels of trust in them? This study addressed these questions by conducting a survey to measure participant’s driving behavior and trust when in the presence of autonomous vehicles. Participants were given several pre-tests to measure existing knowledge and trust of autonomous vehicles, as well as to see their driving behavior when in close proximity to autonomous vehicles. Then participants were presented with an educational intervention, detailing how autonomous vehicles work, including their decision processes. After examining the intervention, participants were asked to repeat post-tests identical to the ones administered before the intervention. Though a significant difference in self-reported driving behavior was measure between the pre-test and post- test, there was no significant relation found between improvement in scores on the education intervention knowledge check and driving behavior. There was also no significant relation found between improvement in scores on the education intervention knowledge check and the change in trust scores. These findings can be used to inform autonomous vehicle and infrastructure design as well as future studies of the effects of autonomous vehicles on human drivers in experimental settings.