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Physical-Virtual Patient Simulators: Bringing Tangible Humanity to Simulated Patients
Title
Physical-Virtual Patient Simulators: Bringing Tangible Humanity to Simulated Patients.
Creator
Daher, Salam, Welch, Gregory, Gonzalez, Laura, Cendan, Juan, Proctor, Michael, University of Central Florida
Abstract / Description
In lieu of real patients, healthcare educators frequently use simulated patients. Simulated patients can be realized in physical form, such as mannequins and trained human actors, or virtual form, such as via computer graphics presented on two-dimensional screens or head-mounted displays. Each of these alone has its strengths and weaknesses. I introduce a new class of physical-virtual patient (PVP) simulators that combine strengths of both forms by combining the flexibility and richness of...
Show moreIn lieu of real patients, healthcare educators frequently use simulated patients. Simulated patients can be realized in physical form, such as mannequins and trained human actors, or virtual form, such as via computer graphics presented on two-dimensional screens or head-mounted displays. Each of these alone has its strengths and weaknesses. I introduce a new class of physical-virtual patient (PVP) simulators that combine strengths of both forms by combining the flexibility and richness of virtual patients with tangible characteristics of a human-shaped physical form that can also exhibit a range of multi-sensory cues, including visual cues (e.g., capillary refill and facial expressions), auditory cues (e.g., verbal responses and heart sounds), and tactile cues (e.g., localized temperature and pulse). This novel combination of integrated capabilities can improve patient simulation outcomes. In my Ph.D. work I focus on three primary areas of related research. First, I describe the realization of the technology for PVPs and results from two user-studies to evaluate the importance of dynamic visuals and human-shaped physical form in terms of perception, behavior, cognition, emotions, and learning.Second, I present a general method to numerically evaluate the compatibility of any simulator-scenario pair in terms of importance and fidelity of cues. This method has the potential to make logistical, economic, and educational impacts on the choices of utilizing existing simulators.Finally, I describe a method for increasing human perception of simulated humans by exposing participants to the simulated human taking part in a short, engaging conversation prior to the simulation.
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Date Issued
2018
Identifier
CFE0007750, ucf:52402
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007750
Mediated Physicality: Inducing Illusory Physicality of Virtual Humans via Their Interactions with Physical Objects
Title
Mediated Physicality: Inducing Illusory Physicality of Virtual Humans via Their Interactions with Physical Objects.
Creator
Lee, Myungho, Welch, Gregory, Wisniewski, Pamela, Hughes, Charles, Bruder, Gerd, Wiegand, Rudolf, University of Central Florida
Abstract / Description
The term virtual human (VH) generally refers to a human-like entity comprised of computer graphics and/or physical body. In the associated research literature, a VH can be further classified as an avatar(-)a human-controlled VH, or an agent(-)a computer-controlled VH. Because of the resemblance with humans, people naturally distinguish them from non-human objects, and often treat them in ways similar to real humans. Sometimes people develop a sense of co-presence or social presence with the...
Show moreThe term virtual human (VH) generally refers to a human-like entity comprised of computer graphics and/or physical body. In the associated research literature, a VH can be further classified as an avatar(-)a human-controlled VH, or an agent(-)a computer-controlled VH. Because of the resemblance with humans, people naturally distinguish them from non-human objects, and often treat them in ways similar to real humans. Sometimes people develop a sense of co-presence or social presence with the VH(-)a phenomenon that is often exploited for training simulations where the VH assumes the role of a human. Prior research associated with VHs has primarily focused on the realism of various visual traits, e.g., appearance, shape, and gestures. However, our sense of the presence of other humans is also affected by other physical sensations conveyed through nearby space or physical objects. For example, we humans can perceive the presence of other individuals via the sound or tactile sensation of approaching footsteps, or by the presence of complementary or opposing forces when carrying a physical box with another person. In my research, I exploit the fact that these sensations, when correlated with events in the shared space, affect one's feeling of social/co-presence with another person. In this dissertation, I introduce novel methods for utilizing direct and indirect physical-virtual interactions with VHs to increase the sense of social/co-presence with the VHs(-)an approach I refer to as mediated physicality. I present results from controlled user studies, in various virtual environment settings, that support the idea that mediated physicality can increase a user's sense of social/co-presence with the VH, and/or induced realistic social behavior. I discuss relationships to prior research, possible explanations for my findings, and areas for future research.
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Date Issued
2019
Identifier
CFE0007485, ucf:52687
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007485
Environmental Physical(-)Virtual Interaction to Improve Social Presence with a Virtual Human in Mixed Reality
Title
Environmental Physical(-)Virtual Interaction to Improve Social Presence with a Virtual Human in Mixed Reality.
Creator
Kim, Kangsoo, Welch, Gregory, Gonzalez, Avelino, Sukthankar, Gita, Bruder, Gerd, Fiore, Stephen, University of Central Florida
Abstract / Description
Interactive Virtual Humans (VHs) are increasingly used to replace or assist real humans in various applications, e.g., military and medical training, education, or entertainment. In most VH research, the perceived social presence with a VH, which denotes the user's sense of being socially connected or co-located with the VH, is the decisive factor in evaluating the social influence of the VH(-)a phenomenon where human users' emotions, opinions, or behaviors are affected by the VH. The purpose...
Show moreInteractive Virtual Humans (VHs) are increasingly used to replace or assist real humans in various applications, e.g., military and medical training, education, or entertainment. In most VH research, the perceived social presence with a VH, which denotes the user's sense of being socially connected or co-located with the VH, is the decisive factor in evaluating the social influence of the VH(-)a phenomenon where human users' emotions, opinions, or behaviors are affected by the VH. The purpose of this dissertation is to develop new knowledge about how characteristics and behaviors of a VH in a Mixed Reality (MR) environment can affect the perception of and resulting behavior with the VH, and to find effective and efficient ways to improve the quality and performance of social interactions with VHs. Important issues and challenges in real(-)virtual human interactions in MR, e.g., lack of physical(-)virtual interaction, are identified and discussed through several user studies incorporating interactions with VH systems. In the studies, different features of VHs are prototyped and evaluated, such as a VH's ability to be aware of and influence the surrounding physical environment, while measuring objective behavioral data as well as collecting subjective responses from the participants. The results from the studies support the idea that the VH's awareness and influence of the physical environment can improve not only the perceived social presence with the VH, but also the trustworthiness of the VH within a social context. The findings will contribute towards designing more influential VHs that can benefit a wide range of simulation and training applications for which a high level of social realism is important, and that can be more easily incorporated into our daily lives as social companions, providing reliable relationships and convenience in assisting with daily tasks.
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Date Issued
2018
Identifier
CFE0007340, ucf:52115
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007340
Improving Efficiency in Deep Learning for Large Scale Visual Recognition
Title
Improving Efficiency in Deep Learning for Large Scale Visual Recognition.
Creator
Liu, Baoyuan, Foroosh, Hassan, Qi, GuoJun, Welch, Gregory, Sukthankar, Rahul, Pensky, Marianna, University of Central Florida
Abstract / Description
The emerging recent large scale visual recognition methods, and in particular the deep Convolutional Neural Networks (CNN), are promising to revolutionize many computer vision based artificial intelligent applications, such as autonomous driving and online image retrieval systems. One of the main challenges in large scale visual recognition is the complexity of the corresponding algorithms. This is further exacerbated by the fact that in most real-world scenarios they need to run in real time...
Show moreThe emerging recent large scale visual recognition methods, and in particular the deep Convolutional Neural Networks (CNN), are promising to revolutionize many computer vision based artificial intelligent applications, such as autonomous driving and online image retrieval systems. One of the main challenges in large scale visual recognition is the complexity of the corresponding algorithms. This is further exacerbated by the fact that in most real-world scenarios they need to run in real time and on platforms that have limited computational resources. This dissertation focuses on improving the efficiency of such large scale visual recognition algorithms from several perspectives. First, to reduce the complexity of large scale classification to sub-linear with the number of classes, a probabilistic label tree framework is proposed. A test sample is classified by traversing the label tree from the root node. Each node in the tree is associated with a probabilistic estimation of all the labels. The tree is learned recursively with iterative maximum likelihood optimization. Comparing to the hard label partition proposed previously, the probabilistic framework performs classification more accurately with similar efficiency. Second, we explore the redundancy of parameters in Convolutional Neural Networks (CNN) and employ sparse decomposition to significantly reduce both the amount of parameters and computational complexity. Both inter-channel and inner-channel redundancy is exploit to achieve more than 90\% sparsity with approximately 1\% drop of classification accuracy. We also propose a CPU based efficient sparse matrix multiplication algorithm to reduce the actual running time of CNN models with sparse convolutional kernels. Third, we propose a multi-stage framework based on CNN to achieve better efficiency than a single traditional CNN model. With a combination of cascade model and the label tree framework, the proposed method divides the input images in both the image space and the label space, and processes each image with CNN models that are most suitable and efficient. The average complexity of the framework is significantly reduced, while the overall accuracy remains the same as in the single complex model.
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Date Issued
2016
Identifier
CFE0006472, ucf:51436
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006472