ABSTRACT



The cold and the flu are two of the most prevalent diseases in the world. Many over the counter (OTC) medications have been created to combat the symptoms of these illnesses. Some medications take a holistic approach by claiming to alleviate a wide range of symptoms, while others target a specific symptom. As these medications become more ubiquitous within the United State of America (USA), consumers form associations and mental models about the cold/flu field. The goal of Study 1 was to build a Pathfinder network based on the associations consumers make between cold/flu symptoms and medications. 100 participants, 18 years or older, fluent in English, and residing in the USA, completed a survey about the relatedness of cold/flu symptoms to OTC medications. They rated the relatedness on a scale of 1 (highly unrelated) to 7 (highly related) and those rankings were used to build a Pathfinder network that represented the average of those associations. Study 2 was conducted to validate the Pathfinder network. A different set of 90 participants with the same restrictions as those in Study 1 completed a matching associations test. They were prompted to match symptoms and medications they associated closely with each other. Results showered a significant negative correlation between the geodetic distance (the number of links between objects in the Pathfinder network) separating symptoms and medications and frequency of pairing symptoms with medication. This provides evidence of the validity of the Pathfinder network. It was also seen that, higher the relatedness rating between symptoms and medications in Study 1, higher the frequency of pairing symptom to medication in Study 2, and the more directly linked those symptoms and medications were in the Pathfinder network. This network can inform pharmaceutical companies about which symptoms they most closely associate with, who their competitors are, what symptoms they can dominate, and how to market their medications more effectively.

Electronic Health Records (EHRs) began to be introduced in the 1960s. Government-run hospitals were the primary adopters of technology. The rate of adoption continually rose from there, doubling from 2007 to 2012 from 34.8% to about 71%. Most of the growth seen from 2007 to 2012 is a result of the passage of the Health Information Technology for Economic and Clinical Health (HITECH) Act as part of the American Reinvestment and Recovery (ARRA) Act. $19 billion dollars were made available as part of these two acts to increase the rate of Health Information Technology (HIT), of which EHRs are a large part. A national health information network is envisioned for the end stages of HITECH which will enable health information to be exchanged immediately from one health network to another. While the ability to exchange data quickly appears to be an achievable goal, it might come with the cost of loss of usability and functionality for providers who interact with the EHRs and often enter health data into an EHR. The loss of usability can be attributed to how the EHR was designed.

The purpose of this review is to determine how to measure and assess human trust in medical technology. A systematic literature review was selected as the path to understand the landscape for measuring trust up to this point. I started by creating a method of systematically reading through related studies in databases before summarizing results and concluding with a recommended design for the upcoming study. This required searching several databases and learning each advanced search methods for each in order to determine which databases provided the most relevant results. From there, the reader examined the results, keeping track in a spreadsheet. The first pass through filtered out the results which did not include detailed methods of measuring trust. The second pass took detailed notes on the remaining studies, keeping track of authors, participants, subjects, methods, instruments, issues, limitations, analytics, and validation. After summarizing the results, discussing trends in the results, and mentioning limitations a conclusion was devised. The recommendation is to use an uncompressed self-reported questionnaire with 4-10 questions on a six-point-Likert scale with reversing scales throughout. Though the studies analyzed were specific to medical settings, this method can work outside of the medical setting for measuring human trust.

The current study aims to explore factors affecting trust in human-drone collaboration. A current gap exists in research surrounding civilian drone use and the role of trust in human-drone interaction and collaboration. Specifically, existing research lacks an explanation of the relationship between drone pilot experience, trust, and trust-related behaviors as well as other factors. Using two dimensions of trust in human-automation team—purpose and performance—the effects of experience on drone design and trust is studied to explore factors that may contribute to such a model. An online survey was conducted to examine civilian drone operators’ experience, familiarity, expertise, and trust in commercially available drones. It was predicted that factors of prior experience (familiarity, self-reported expertise) would have a significant effect on trust in drones. The choice to use or exclude the drone propellers in a search-and-identify scenario, paired with the pilots’ experience with drones, would further confirm the relevance of the trust dimensions of purpose versus performance in the human-drone relationship. If the pilot has a positive sense of purpose and benevolence with the drone, the pilot trusts the drone has a positive intent towards them and the task. If the pilot has trust in the performance of the drone, they ascertain that the drone has the skill to do the task. The researcher found no significant differences between mean trust scores across levels of familiarity, but did find some interaction between self-report expertise, familiarity, and trust. Future research should further explore more concrete measures of situational participant factors such as self-confidence and expertise to understand their role in civilian pilots’ trust in their drone.

Vehicular automation and autonomy are emerging fields that are growing at an

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.

With the growth of autonomous vehicles’ prevalence, it is important to understand the relationship between autonomous vehicles and the other drivers around them. More specifically, how does one’s knowledge about autonomous vehicles (AV) affect positive and negative affect towards driving in their presence? Furthermore, how does trust of autonomous vehicles correlate with those emotions? These questions were addressed by conducting a survey to measure participant’s positive affect, negative affect, and trust when driving in the presence of autonomous vehicles. Participants’ were issued a pretest measuring existing knowledge of autonomous vehicles, followed by measures of affect and trust. After completing this pre-test portion of the study, participants were given information about how autonomous vehicles work, and were then presented with a posttest identical to the pretest. The educational intervention had no effect on positive or negative affect, though there was a positive relationship between positive affect and trust and a negative relationship between negative affect and trust. These findings will be used to inform future research endeavors researching trust and autonomous vehicles using a test bed developed at Arizona State University. This test bed allows for researchers to examine the behavior of multiple participants at the same time and include autonomous vehicles in studies.

The advent of commercial inexpensive sensors and the advances in information and communication technology (ICT) have brought forth the era of pervasive Quantified-Self. Automatic diet monitoring is one of the most important aspects for Quantified-Self because it is vital for ensuring the well-being of patients suffering from chronic diseases as well as for providing a low cost means for maintaining the health for everyone else. Automatic dietary monitoring consists of: a) Determining the type and amount of food intake, and b) Monitoring eating behavior, i.e., time, frequency, and speed of eating. Although there are some existing techniques towards these ends, they suffer from issues of low accuracy and low adherence. To overcome these issues, multiple sensors were utilized because the availability of affordable sensors that can capture the different aspect information has the potential for increasing the available knowledge for Quantified-Self. For a), I envision an intelligent dietary monitoring system that automatically identifies food items by using the knowledge obtained from visible spectrum camera and infrared spectrum camera. This system is able to outperform the state-of-the-art systems for cooked food recognition by 25% while also minimizing user intervention. For b), I propose a novel methodology, IDEA that performs accurate eating action identification within eating episodes with an average F1-score of 0.92. This is an improvement of 0.11 for precision and 0.15 for recall for the worst-case users as compared to the state-of-the-art. IDEA uses only a single wrist-band which includes four sensors and provides feedback on eating speed every 2 minutes without obtaining any manual input from the user.

There has been an ongoing debate between the relative deterrent power of certainty and severity on deceptive and criminal activity, certainty being the likelihood of capture and severity being the magnitude of the potential punishment. This paper is a review of the current body of research regarding risk assessment and deception in games, specifically regarding certainty and severity. The topics of game theoretical foundations, balance, and design were covered, as were heuristics and individual differences in deceptive behavior. Using this background knowledge, this study implemented a methodology through which the risk assessments of certainty and severity can be compared behaviorally in a repeated conflict context. It was found that certainty had a significant effect on a person’s likelihood to lie, while severity did not. Exploratory data was collected using the dark triad personality quiz, though it did not ultimately show a pattern.

Minimally invasive surgery is a surgical technique that is known for its reduced

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.

What if there is a way to integrate prosthetics seamlessly with the human body and robots could help improve the lives of children with disabilities? With physical human-robot interaction being seen in multiple aspects of life, including industry, medical, and social, how these robots are interacting with human becomes even more important. Therefore, how smoothly the robot can interact with a person will determine how safe and efficient this relationship will be. This thesis investigates adaptive control method that allows a robot to adapt to the human's actions based on the interaction force. Allowing the relationship to become more effortless and less strained when the robot has a different goal than the human, as seen in Game Theory, using multiple techniques that adapts the system. Few applications this could be used for include robots in physical therapy, manufacturing robots that can adapt to a changing environment, and robots teaching people something new like dancing or learning how to walk after surgery.

The experience gained is the understanding of how a cost function of a system works, including the tracking error, speed of the system, the robot’s effort, and the human’s effort. Also, this two-agent system, results into a two-agent adaptive impedance model with an input for each agent of the system. This leads to a nontraditional linear quadratic regulator (LQR), that must be separated and then added together. Thus, creating a traditional LQR. This new experience can be used in the future to help build better safety protocols on manufacturing robots. In the future the knowledge learned from this research could be used to develop technologies for a robot to allow to adapt to help counteract human error.