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- Genre: Academic theses
Description
As people begin to live longer and the population shifts to having more olderadults on Earth than young children, radical solutions will be needed to ease the
burden on society. It will be essential to develop technology that can age with the
individual. One solution is to keep older adults in their homes longer through smart
home and smart living technology, allowing them to age in place. People have many
choices when choosing where to age in place, including their own homes, assisted
living facilities, nursing homes, or family members. No matter where people choose to
age, they may face isolation and financial hardships. It is crucial to keep finances in
mind when developing Smart Home technology.
Smart home technologies seek to allow individuals to stay inside their homes for
as long as possible, yet little work looks at how we can use technology in different
life stages. Robots are poised to impact society and ease burns at home and in the
workforce. Special attention has been given to social robots to ease isolation. As
social robots become accepted into society, researchers need to understand how these
robots should mimic natural conversation. My work attempts to answer this question
within social robotics by investigating how to make conversational robots natural and
reciprocal.
I investigated this through a 2x2 Wizard of Oz between-subjects user study. The
study lasted four months, testing four different levels of interactivity with the robot.
None of the levels were significantly different from the others, an unexpected result. I
then investigated the robot’s personality, the participant’s trust, and the participant’s
acceptance of the robot and how that influenced the study.
ContributorsMiller, Jordan (Author) / McDaniel, Troy (Thesis advisor) / Michael, Katina (Committee member) / Cooke, Nancy (Committee member) / Bryan, Chris (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2022
Description
Visual Odometry is one of the key aspects of robotic localization and mapping. Visual Odometry consists of many geometric-based approaches that convert visual data (images) into pose estimates of where the robot is in space. The classical geometric methods have shown promising results; they are carefully crafted and built explicitly for these tasks. However, such geometric methods require extreme fine-tuning and extensive prior knowledge to set up these systems for different scenarios. Classical Geometric approaches also require significant post-processing and optimization to minimize the error between the estimated pose and the global truth. In this body of work, the deep learning model was formed by combining SuperPoint and SuperGlue. The resulting model does not require any prior fine-tuning. It has been trained to enable both outdoor and indoor settings. The proposed deep learning model is applied to the Karlsruhe Institute of Technology and Toyota Technological Institute dataset along with other classical geometric visual odometry models. The proposed deep learning model has not been trained on the Karlsruhe Institute of Technology and Toyota Technological Institute dataset. It is only during experimentation that the deep learning model is first introduced to the Karlsruhe Institute of Technology and Toyota Technological Institute dataset. Using the monocular grayscale images from the visual odometer files of the Karlsruhe Institute of Technology and Toyota Technological Institute dataset, through the experiment to test the viability of the models for different sequences. The experiment has been performed on eight different sequences and has obtained the Absolute Trajectory Error and the time taken for each sequence to finish the computation. From the obtained results, there are inferences drawn from the classical and deep learning approaches.
ContributorsVaidyanathan, Venkatesh (Author) / Venkateswara, Hemanth (Thesis advisor) / McDaniel, Troy (Thesis advisor) / Michael, Katina (Committee member) / Arizona State University (Publisher)
Created2022
Description
Ultraviolet (UV) radiation is the most well-known cause of skin cancer, and skin cancer is the most common type of cancer in the United States. People are exposed to UV rays when they engage in outdoor activities, particularly exercise, which is an important health behavior. Thus, researchers and the general public have shown increasing interest in measuring UV exposures during outdoor physical activity using wearable sensors. However, minimal research exists at the intersection of UV sensors, personal exposure, adaptive behavior due to exposures, and risk of skin damage. Three studies are presented in this dissertation: (1) a state-of-the-art review that synthesizes the current academic and grey literature surrounding personal UV sensing technologies; (2) the first study to investigate the effects of specific physical activity types, skin type, and solar angle on personal exposure in different outdoor environmental contexts; and (3) a study that develops recommendations for future UV-sensing wearables based on follow-up interviews with participants from the second study, who used a wrist-worn UV sensor while exercising outdoors. The first study provides recommendations for 13 commercially available sensors that are most suitable for various types of research or personal use. The review findings will help guide researchers in future studies assessing UV exposure with wearables during physical activity. The second study outlines the development of predictive models for individual-level UV exposure, which are also provided. These models recommend the inclusion of sky view factor, solar angle, activity type, urban environment type, and the directions traveled during physical activity. Finally, based on user feedback, the third study recommends that future UV-sensing wearables should be multi-functional watches where users can toggle between showing their UV exposure results in cumulative and countdown formats, which is intuitive and aesthetically pleasing to users.
ContributorsHenning, Alyssa Leigh (Author) / Vanos, Jennifer (Thesis advisor) / Johnston, Erik (Thesis advisor) / Frow, Emma (Committee member) / Michael, Katina (Committee member) / Downs, Nathan (Committee member) / Arizona State University (Publisher)
Created2021
Description
With the growing popularity of 3d printing in recreational, research, and commercial enterprises new techniques and processes are being developed to improve the quality of parts created. Even so, the anisotropic properties is still a major hindrance of parts manufactured in this method. The goal is to produce parts that mimic the strength characteristics of a comparable part of the same design and materials created using injection molding. In achieving this goal the production cost can be reduced by eliminating the initial investment needed for the creation of expensive tooling. This initial investment reduction will allow for a wider variant of products in smaller batch runs to be made available. This thesis implements the use of ultraviolet (UV) illumination for an in-process laser local pre-deposition heating (LLPH). By comparing samples with and without the LLPH process it is determined that applied energy that is absorbed by the polymer is converted to an increase in the interlayer temperature, and resulting in an observed increase in tensile strength over the baseline test samples. The increase in interlayer bonding thus can be considered the dominating factor over polymer degradation.
ContributorsKusel, Scott Daniel (Author) / Hsu, Keng (Thesis advisor) / Sodemann, Angela (Committee member) / Kannan, Arunachala M (Committee member) / Arizona State University (Publisher)
Created2017