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- Creators: Computer Science and Engineering Program
- Creators: Turaga, Pavan
Design and development of an immersive virtual reality team trainer for advance cardiac life support
Description
Technology in the modern day has ensured that learning of skills and behavior may be both widely disseminated and cheaply available. An example of this is the concept of virtual reality (VR) training. Virtual Reality training ensures that learning can be provided often, in a safe simulated setting, and it may be delivered in a manner that makes it engaging while negating the need to purchase special equipment. This thesis presents a case study in the form of a time critical, team based medical scenario known as Advanced Cardiac Life Support (ACLS). A framework and methodology associated with the design of a VR trainer for ACLS is detailed. In addition, in order to potentially provide an engaging experience, the simulator was designed to incorporate immersive elements and a multimodal interface (haptic, visual, and auditory). A study was conducted to test two primary hypotheses namely: a meaningful transfer of skill is achieved from virtual reality training to real world mock codes and the presence of immersive components in virtual reality leads to an increase in the performance gained. The participant pool consisted of 54 clinicians divided into 9 teams of 6 members each. The teams were categorized into three treatment groups: immersive VR (3 teams), minimally immersive VR (3 teams), and control (3 teams). The study was conducted in 4 phases from a real world mock code pretest to assess baselines to a 30 minute VR training session culminating in a final mock code to assess the performance change from the baseline. The minimally immersive team was treated as control for the immersive components. The teams were graded, in both VR and mock code sessions, using the evaluation metric used in real world mock codes. The study revealed that the immersive VR groups saw greater performance gain from pretest to posttest than the minimally immersive and control groups in case of the VFib/VTach scenario (~20% to ~5%). Also the immersive VR groups had a greater performance gain than the minimally immersive groups from the first to the final session of VFib/VTach (29% to -13%) and PEA (27% to 15%).
ContributorsVankipuram, Akshay (Author) / Li, Baoxin (Thesis advisor) / Burleson, Winslow (Committee member) / Kahol, Kanav (Committee member) / Arizona State University (Publisher)
Created2012
Description
Over the course of computing history there have been many ways for humans to pass information to computers. These different input types, at first, tended to be used one or two at a time for the users interfacing with computers. As time has progressed towards the present, however, many devices are beginning to make use of multiple different input types, and will likely continue to do so. With this happening, users need to be able to interact with single applications through a variety of ways without having to change the design or suffer a loss of functionality. This is important because having only one user interface, UI, across all input types is makes it easier for the user to learn and keeps all interactions consistent across the application. Some of the main input types in use today are touch screens, mice, microphones, and keyboards; all seen in Figure 1 below. Current design methods tend to focus on how well the users are able to learn and use a computing system. It is good to focus on those aspects, but it is important to address the issues that come along with using different input types, or in this case, multiple input types. UI design for touch screens, mice, microphones, and keyboards each requires satisfying a different set of needs. Due to this trend in single devices being used in many different input configurations, a "fully functional" UI design will need to address the needs of multiple input configurations. In this work, clashing concerns are described for the primary input sources for computers and suggests methodologies and techniques for designing a single UI that is reasonable for all of the input configurations.
ContributorsJohnson, David Bradley (Author) / Calliss, Debra (Thesis director) / Wilkerson, Kelly (Committee member) / Walker, Erin (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2013-05
Description
Collecting accurate collective decisions via crowdsourcing
is challenging due to cognitive biases, varying
worker expertise, and varying subjective scales. This
work investigates new ways to determine collective decisions
by prompting users to provide input in multiple
formats. A crowdsourced task is created that aims
to determine ground-truth by collecting information in
two different ways: rankings and numerical estimates.
Results indicate that accurate collective decisions can
be achieved with less people when ordinal and cardinal
information is collected and aggregated together
using consensus-based, multimodal models. We also
show that presenting users with larger problems produces
more valuable ordinal information, and is a more
efficient way to collect an aggregate ranking. As a result,
we suggest input-elicitation to be more widely considered
for future work in crowdsourcing and incorporated
into future platforms to improve accuracy and efficiency.
is challenging due to cognitive biases, varying
worker expertise, and varying subjective scales. This
work investigates new ways to determine collective decisions
by prompting users to provide input in multiple
formats. A crowdsourced task is created that aims
to determine ground-truth by collecting information in
two different ways: rankings and numerical estimates.
Results indicate that accurate collective decisions can
be achieved with less people when ordinal and cardinal
information is collected and aggregated together
using consensus-based, multimodal models. We also
show that presenting users with larger problems produces
more valuable ordinal information, and is a more
efficient way to collect an aggregate ranking. As a result,
we suggest input-elicitation to be more widely considered
for future work in crowdsourcing and incorporated
into future platforms to improve accuracy and efficiency.
ContributorsKemmer, Ryan Wyeth (Author) / Escobedo, Adolfo (Thesis director) / Maciejewski, Ross (Committee member) / Computing and Informatics Program (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
The software element of home and small business networking solutions has failed to keep pace with annual development of newer and faster hardware. The software running on these devices is an afterthought, oftentimes equipped with minimal features, an obtuse user interface, or both. At the same time, this past year has seen the rise of smart home assistants that represent the next step in human-computer interaction with their advanced use of natural language processing. This project seeks to quell the issues with the former by exploring a possible fusion of a powerful, feature-rich software-defined networking stack and the incredible natural language processing tools of smart home assistants. To accomplish these ends, a piece of software was developed to leverage the powerful natural language processing capabilities of one such smart home assistant, the Amazon Echo. On one end, this software interacts with Amazon Web Services to retrieve information about a user's speech patterns and key information contained in their speech. On the other end, the software joins that information with its previous session state to intelligently translate speech into a series of commands for the separate components of a networking stack. The software developed for this project empowers a user to quickly make changes to several facets of their networking gear or acquire information about it with just their language \u2014 no terminals, java applets, or web configuration interfaces needed, thus circumventing clunky UI's or jumping from shell to shell. It is the author's hope that showing how networking equipment can be configured in this innovative way will draw more attention to the current failings of networking equipment and inspire a new series of intuitive user interfaces.
ContributorsHermens, Ryan Joseph (Author) / Meuth, Ryan (Thesis director) / Burger, Kevin (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Engaging users is essential for designers of any exhibit, such as the human-computer interface, the visual effects, or the informational content. The need to understand users’ experiences and learning gains has motivated a focus on user engagement across computer science. However, there has been limited review of how human-computer interaction research interprets and employs the concepts in museum and exhibit settings, specifically their joint effects. The purpose of this study is to assess users’ experience and learning outcome, while interacting with a web application part of an exhibit that showcases the NASA Psyche spacecraft model. This web application provides an interactive menu that allows the user to navigate on the touch panel installed within the Psyche Spacecraft Exhibit. The user can press the button on the menu which will light up the corresponding parts of the model with a detailed description displayed on the panel. For this study, participants were required to take a questionnaire, a pretest, and a posttest. They were also required to interact with the web application while wearing an Emotiv EPOC+ EEG headset that measures their emotions while they were visiting the exhibit. During the study, data such as questionnaire results, sensed emotions from the EEG headset, and pretest and posttest scores were collected. Using the information gathered, the study explores user experience and learning gains through both biometrics and traditional tools. The findings show that users felt engaged and frustrated the most and that users gained more knowledge but at varying degrees from the interaction. Future work can be done to lower the levels of frustration and keep learning gains at a more consistent rate by improving the exhibit design to better meet various learning needs and visitor profiles.
ContributorsMa, Yumeng (Author) / Chavez-Echeagaray, Maria Elena (Thesis director) / Gonzalez Sanchez, Javier (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05
Description
The tradition of building musical robots and automata is thousands of years old. Despite this rich history, even today musical robots do not play with as much nuance and subtlety as human musicians. In particular, most instruments allow the player to manipulate timbre while playing; if a violinist is told to sustain an E, they will select which string to play it on, how much bow pressure and velocity to use, whether to use the entire bow or only the portion near the tip or the frog, how close to the bridge or fingerboard to contact the string, whether or not to use a mute, and so forth. Each one of these choices affects the resulting timbre, and navigating this timbre space is part of the art of playing the instrument. Nonetheless, this type of timbral nuance has been largely ignored in the design of musical robots. Therefore, this dissertation introduces a suite of techniques that deal with timbral nuance in musical robots. Chapter 1 provides the motivating ideas and introduces Kiki, a robot designed by the author to explore timbral nuance. Chapter 2 provides a long history of musical robots, establishing the under-researched nature of timbral nuance. Chapter 3 is a comprehensive treatment of dynamic timbre production in percussion robots and, using Kiki as a case-study, provides a variety of techniques for designing striking mechanisms that produce a range of timbres similar to those produced by human players. Chapter 4 introduces a machine-learning algorithm for recognizing timbres, so that a robot can transcribe timbres played by a human during live performance. Chapter 5 introduces a technique that allows a robot to learn how to produce isolated instances of particular timbres by listening to a human play an examples of those timbres. The 6th and final chapter introduces a method that allows a robot to learn the musical context of different timbres; this is done in realtime during interactive improvisation between a human and robot, wherein the robot builds a statistical model of which timbres the human plays in which contexts, and uses this to inform its own playing.
ContributorsKrzyzaniak, Michael Joseph (Author) / Coleman, Grisha (Thesis advisor) / Turaga, Pavan (Committee member) / Artemiadis, Panagiotis (Committee member) / Arizona State University (Publisher)
Created2016
Description
Audio signals, such as speech and ambient sounds convey rich information pertaining to a user’s activity, mood or intent. Enabling machines to understand this contextual information is necessary to bridge the gap in human-machine interaction. This is challenging due to its subjective nature, hence, requiring sophisticated techniques. This dissertation presents a set of computational methods, that generalize well across different conditions, for speech-based applications involving emotion recognition and keyword detection, and ambient sounds-based applications such as lifelogging.
The expression and perception of emotions varies across speakers and cultures, thus, determining features and classification methods that generalize well to different conditions is strongly desired. A latent topic models-based method is proposed to learn supra-segmental features from low-level acoustic descriptors. The derived features outperform state-of-the-art approaches over multiple databases. Cross-corpus studies are conducted to determine the ability of these features to generalize well across different databases. The proposed method is also applied to derive features from facial expressions; a multi-modal fusion overcomes the deficiencies of a speech only approach and further improves the recognition performance.
Besides affecting the acoustic properties of speech, emotions have a strong influence over speech articulation kinematics. A learning approach, which constrains a classifier trained over acoustic descriptors, to also model articulatory data is proposed here. This method requires articulatory information only during the training stage, thus overcoming the challenges inherent to large-scale data collection, while simultaneously exploiting the correlations between articulation kinematics and acoustic descriptors to improve the accuracy of emotion recognition systems.
Identifying context from ambient sounds in a lifelogging scenario requires feature extraction, segmentation and annotation techniques capable of efficiently handling long duration audio recordings; a complete framework for such applications is presented. The performance is evaluated on real world data and accompanied by a prototypical Android-based user interface.
The proposed methods are also assessed in terms of computation and implementation complexity. Software and field programmable gate array based implementations are considered for emotion recognition, while virtual platforms are used to model the complexities of lifelogging. The derived metrics are used to determine the feasibility of these methods for applications requiring real-time capabilities and low power consumption.
The expression and perception of emotions varies across speakers and cultures, thus, determining features and classification methods that generalize well to different conditions is strongly desired. A latent topic models-based method is proposed to learn supra-segmental features from low-level acoustic descriptors. The derived features outperform state-of-the-art approaches over multiple databases. Cross-corpus studies are conducted to determine the ability of these features to generalize well across different databases. The proposed method is also applied to derive features from facial expressions; a multi-modal fusion overcomes the deficiencies of a speech only approach and further improves the recognition performance.
Besides affecting the acoustic properties of speech, emotions have a strong influence over speech articulation kinematics. A learning approach, which constrains a classifier trained over acoustic descriptors, to also model articulatory data is proposed here. This method requires articulatory information only during the training stage, thus overcoming the challenges inherent to large-scale data collection, while simultaneously exploiting the correlations between articulation kinematics and acoustic descriptors to improve the accuracy of emotion recognition systems.
Identifying context from ambient sounds in a lifelogging scenario requires feature extraction, segmentation and annotation techniques capable of efficiently handling long duration audio recordings; a complete framework for such applications is presented. The performance is evaluated on real world data and accompanied by a prototypical Android-based user interface.
The proposed methods are also assessed in terms of computation and implementation complexity. Software and field programmable gate array based implementations are considered for emotion recognition, while virtual platforms are used to model the complexities of lifelogging. The derived metrics are used to determine the feasibility of these methods for applications requiring real-time capabilities and low power consumption.
ContributorsShah, Mohit (Author) / Spanias, Andreas (Thesis advisor) / Chakrabarti, Chaitali (Thesis advisor) / Berisha, Visar (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2015
Description
Palliative care is a field that serves to benefit enormously from the introduction of mobile medical applications. Doctors at the Mayo Clinic intend to address a reoccurring dilemma, in which palliative care patients visit the emergency room during situations that are not urgent or life-threatening. Doing so unnecessarily drains the hospital’s resources, and it prevents the patient’s physician from applying specialized care that would better suit the patient’s individual needs. This scenario is detrimental to all involved. A mobile medical application seeks to foster doctor-patient communication while simultaneously decreasing the frequency of these excessive E.R. visits. In order to provide a sufficient standard of usefulness and convenience, the design of such a mobile application must be tailored to accommodate the needs of palliative care patients. Palliative care is focused on establishing long-term comfort for people who are often terminally-ill, elderly, handicapped, or otherwise severely disadvantaged. Therefore, a UI intended for palliative care patients must be devoted to simplicity and ease of use. The application must also be robust enough that the user feels that they have been provided with enough capabilities. The majority of this paper is dedicated to overhauling an existing palliative care application, the product of a previous honors thesis project, and implementing a user interface that establishes a simple, positive, and advantageous environment. This is accomplished through techniques such as color-coding, optimizing page layout, increasing customization capabilities, and more. Above all else, this user interface is intended to make the patient’s experience satisfying and trouble-free. They should be able to log in, navigate the application’s features with a few taps of their finger, and log out — all without undergoing any frustration or difficulties.
ContributorsWilkes, Jarrett Matthew (Co-author) / Ganey, David (Co-author) / Dao, Lelan (Co-author) / Balasooriya, Janaka (Thesis director) / Faucon, Christophe (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12