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- All Subjects: Computer Science
- Creators: Li, Baoxin
- Creators: Xue, Guoliang
- Member of: ASU Electronic Theses and Dissertations
- Resource Type: Text
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
Social situational awareness, or the attentiveness to one's social surroundings, including the people, their interactions and their behaviors is a complex sensory-cognitive-motor task that requires one to be engaged thoroughly in understanding their social interactions. These interactions are formed out of the elements of human interpersonal communication including both verbal and non-verbal cues. While the verbal cues are instructive and delivered through speech, the non-verbal cues are mostly interpretive and requires the full attention of the participants to understand, comprehend and respond to them appropriately. Unfortunately certain situations are not conducive for a person to have complete access to their social surroundings, especially the non-verbal cues. For example, a person is who is blind or visually impaired may find that the non-verbal cues like smiling, head nod, eye contact, body gestures and facial expressions of their interaction partners are not accessible due to their sensory deprivation. The same could be said of people who are remotely engaged in a conversation and physically separated to have a visual access to one's body and facial mannerisms. This dissertation describes novel multimedia technologies to aid situations where it is necessary to mediate social situational information between interacting participants. As an example of the proposed system, an evidence-based model for understanding the accessibility problem faced by people who are blind or visually impaired is described in detail. From the derived model, a sleuth of sensing and delivery technologies that use state-of-the-art computer vision algorithms in combination with novel haptic interfaces are developed towards a) A Dyadic Interaction Assistant, capable of helping individuals who are blind to access important head and face based non-verbal communicative cues during one-on-one dyadic interactions, and b) A Group Interaction Assistant, capable of provide situational awareness about the interaction partners and their dynamics to a user who is blind, while also providing important social feedback about their own body mannerisms. The goal is to increase the effective social situational information that one has access to, with the conjuncture that a good awareness of one's social surroundings gives them the ability to understand and empathize with their interaction partners better. Extending the work from an important social interaction assistive technology, the need for enriched social situational awareness is everyday professional situations are also discussed, including, a) enriched remote interactions between physically separated interaction partners, and b) enriched communication between medical professionals during critical care procedures, towards enhanced patient safety. In the concluding remarks, this dissertation engages the readers into a science and technology policy discussion on the potential effect of a new technology like the social interaction assistant on the society. Discussing along the policy lines, social disability is highlighted as an important area that requires special attention from researchers and policy makers. Given that the proposed technology relies on wearable inconspicuous cameras, the discussion of privacy policies is extended to encompass newly evolving interpersonal interaction recorders, like the one presented in this dissertation.
ContributorsKrishna, Sreekar (Author) / Panchanathan, Sethuraman (Thesis advisor) / Black, John A. (Committee member) / Qian, Gang (Committee member) / Li, Baoxin (Committee member) / Shiota, Michelle (Committee member) / Arizona State University (Publisher)
Created2011
Description
Continuous advancements in biomedical research have resulted in the production of vast amounts of scientific data and literature discussing them. The ultimate goal of computational biology is to translate these large amounts of data into actual knowledge of the complex biological processes and accurate life science models. The ability to rapidly and effectively survey the literature is necessary for the creation of large scale models of the relationships among biomedical entities as well as hypothesis generation to guide biomedical research. To reduce the effort and time spent in performing these activities, an intelligent search system is required. Even though many systems aid in navigating through this wide collection of documents, the vastness and depth of this information overload can be overwhelming. An automated extraction system coupled with a cognitive search and navigation service over these document collections would not only save time and effort, but also facilitate discovery of the unknown information implicitly conveyed in the texts. This thesis presents the different approaches used for large scale biomedical named entity recognition, and the challenges faced in each. It also proposes BioEve: an integrative framework to fuse a faceted search with information extraction to provide a search service that addresses the user's desire for "completeness" of the query results, not just the top-ranked ones. This information extraction system enables discovery of important semantic relationships between entities such as genes, diseases, drugs, and cell lines and events from biomedical text on MEDLINE, which is the largest publicly available database of the world's biomedical journal literature. It is an innovative search and discovery service that makes it easier to search
avigate and discover knowledge hidden in life sciences literature. To demonstrate the utility of this system, this thesis also details a prototype enterprise quality search and discovery service that helps researchers with a guided step-by-step query refinement, by suggesting concepts enriched in intermediate results, and thereby facilitating the "discover more as you search" paradigm.
avigate and discover knowledge hidden in life sciences literature. To demonstrate the utility of this system, this thesis also details a prototype enterprise quality search and discovery service that helps researchers with a guided step-by-step query refinement, by suggesting concepts enriched in intermediate results, and thereby facilitating the "discover more as you search" paradigm.
ContributorsKanwar, Pradeep (Author) / Davulcu, Hasan (Thesis advisor) / Dinu, Valentin (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2010
Description
The fields of pattern recognition and machine learning are on a fundamental quest to design systems that can learn the way humans do. One important aspect of human intelligence that has so far not been given sufficient attention is the capability of humans to express when they are certain about a decision, or when they are not. Machine learning techniques today are not yet fully equipped to be trusted with this critical task. This work seeks to address this fundamental knowledge gap. Existing approaches that provide a measure of confidence on a prediction such as learning algorithms based on the Bayesian theory or the Probably Approximately Correct theory require strong assumptions or often produce results that are not practical or reliable. The recently developed Conformal Predictions (CP) framework - which is based on the principles of hypothesis testing, transductive inference and algorithmic randomness - provides a game-theoretic approach to the estimation of confidence with several desirable properties such as online calibration and generalizability to all classification and regression methods. This dissertation builds on the CP theory to compute reliable confidence measures that aid decision-making in real-world problems through: (i) Development of a methodology for learning a kernel function (or distance metric) for optimal and accurate conformal predictors; (ii) Validation of the calibration properties of the CP framework when applied to multi-classifier (or multi-regressor) fusion; and (iii) Development of a methodology to extend the CP framework to continuous learning, by using the framework for online active learning. These contributions are validated on four real-world problems from the domains of healthcare and assistive technologies: two classification-based applications (risk prediction in cardiac decision support and multimodal person recognition), and two regression-based applications (head pose estimation and saliency prediction in images). The results obtained show that: (i) multiple kernel learning can effectively increase efficiency in the CP framework; (ii) quantile p-value combination methods provide a viable solution for fusion in the CP framework; and (iii) eigendecomposition of p-value difference matrices can serve as effective measures for online active learning; demonstrating promise and potential in using these contributions in multimedia pattern recognition problems in real-world settings.
ContributorsNallure Balasubramanian, Vineeth (Author) / Panchanathan, Sethuraman (Thesis advisor) / Ye, Jieping (Committee member) / Li, Baoxin (Committee member) / Vovk, Vladimir (Committee member) / Arizona State University (Publisher)
Created2010
Description
Peer-to-peer systems are known to be vulnerable to the Sybil attack. The lack of a central authority allows a malicious user to create many fake identities (called Sybil nodes) pretending to be independent honest nodes. The goal of the malicious user is to influence the system on his/her behalf. In order to detect the Sybil nodes and prevent the attack, a reputation system is used for the nodes, built through observing its interactions with its peers. The construction makes every node a part of a distributed authority that keeps records on the reputation and behavior of the nodes. Records of interactions between nodes are broadcast by the interacting nodes and honest reporting proves to be a Nash Equilibrium for correct (non-Sybil) nodes. In this research is argued that in realistic communication schedule scenarios, simple graph-theoretic queries such as the computation of Strongly Connected Components and Densest Subgraphs, help in exposing those nodes most likely to be Sybil, which are then proved to be Sybil or not through a direct test executed by some peers.
ContributorsCárdenas-Haro, José Antonio (Author) / Konjevod, Goran (Thesis advisor) / Richa, Andréa W. (Thesis advisor) / Sen, Arunabha (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2010
Description
The exponential rise in unmanned aerial vehicles has necessitated the need for accurate pose estimation under any extreme conditions. Visual Odometry (VO) is the estimation of position and orientation of a vehicle based on analysis of a sequence of images captured from a camera mounted on it. VO offers a cheap and relatively accurate alternative to conventional odometry techniques like wheel odometry, inertial measurement systems and global positioning system (GPS). This thesis implements and analyzes the performance of a two camera based VO called Stereo based visual odometry (SVO) in presence of various deterrent factors like shadows, extremely bright outdoors, wet conditions etc... To allow the implementation of VO on any generic vehicle, a discussion on porting of the VO algorithm to android handsets is presented too. The SVO is implemented in three steps. In the first step, a dense disparity map for a scene is computed. To achieve this we utilize sum of absolute differences technique for stereo matching on rectified and pre-filtered stereo frames. Epipolar geometry is used to simplify the matching problem. The second step involves feature detection and temporal matching. Feature detection is carried out by Harris corner detector. These features are matched between two consecutive frames using the Lucas-Kanade feature tracker. The 3D co-ordinates of these matched set of features are computed from the disparity map obtained from the first step and are mapped into each other by a translation and a rotation. The rotation and translation is computed using least squares minimization with the aid of Singular Value Decomposition. Random Sample Consensus (RANSAC) is used for outlier detection. This comprises the third step. The accuracy of the algorithm is quantified based on the final position error, which is the difference between the final position computed by the SVO algorithm and the final ground truth position as obtained from the GPS. The SVO showed an error of around 1% under normal conditions for a path length of 60 m and around 3% in bright conditions for a path length of 130 m. The algorithm suffered in presence of shadows and vibrations, with errors of around 15% and path lengths of 20 m and 100 m respectively.
ContributorsDhar, Anchit (Author) / Saripalli, Srikanth (Thesis advisor) / Li, Baoxin (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2010
Description
Debugging is a hard task. Debugging multi-threaded applications with their inherit non-determinism is all the more difficult. Non-determinism of any kind adds to the difficulty of cyclic debugging. In Android applications which are written in Java, threads and concurrency constructs introduce non-determinism to the program execution. Even with the same input, consecutive runs may not be the same and reproducing the same bug is a challenging task. This makes it difficult to understand and analyze the execution behavior or to understand the source of a failing execution. This thesis introduces a replay mechanism for Android applications written in Java and is based on the Lamport Clock. This tool provides the user with a controlled debugging environment, where the program execution follows the identical partially ordered happened-before dependency among threads, as during the recorded execution. In this, certain significant events like thread creation, synchronization etc. are recorded during run-time. They can later be replayed off-line, as many times as needed to pinpoint and fix an error in the application. It is software based approach and has been implemented by modifying the Dalvik Virtual Machine in the Android platform. The method of replay described in this thesis is independent of the underlying operating system scheduler.
ContributorsGirme, Rohit (Author) / Lee, Yann-Hang (Thesis advisor) / Chatha, Karamvir (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2011
Description
Advances in the area of ubiquitous, pervasive and wearable computing have resulted in the development of low band-width, data rich environmental and body sensor networks, providing a reliable and non-intrusive methodology for capturing activity data from humans and the environments they inhabit. Assistive technologies that promote independent living amongst elderly and individuals with cognitive impairment are a major motivating factor for sensor-based activity recognition systems. However, the process of discerning relevant activity information from these sensor streams such as accelerometers is a non-trivial task and is an on-going research area. The difficulty stems from factors such as spatio-temporal variations in movement patterns induced by different individuals and contexts, sparse occurrence of relevant activity gestures in a continuous stream of irrelevant movements and the lack of real-world data for training learning algorithms. This work addresses these challenges in the context of wearable accelerometer-based simple activity and gesture recognition. The proposed computational framework utilizes discriminative classifiers for learning the spatio-temporal variations in movement patterns and demonstrates its effectiveness through a real-time simple activity recognition system and short duration, non- repetitive activity gesture recognition. Furthermore, it proposes adaptive discriminative threshold models trained only on relevant activity gestures for filtering irrelevant movement patterns in a continuous stream. These models are integrated into a gesture spotting network for detecting activity gestures involved in complex activities of daily living. The framework addresses the lack of real world data for training, by using auxiliary, yet related data samples for training in a transfer learning setting. Finally the problem of predicting activity tasks involved in the execution of a complex activity of daily living is described and a solution based on hierarchical Markov models is discussed and evaluated.
ContributorsChatapuram Krishnan, Narayanan (Author) / Panchanathan, Sethuraman (Thesis advisor) / Sundaram, Hari (Committee member) / Ye, Jieping (Committee member) / Li, Baoxin (Committee member) / Cook, Diane (Committee member) / Arizona State University (Publisher)
Created2010
Description
Video object segmentation (VOS) is an important task in computer vision with a lot of applications, e.g., video editing, object tracking, and object based encoding. Different from image object segmentation, video object segmentation must consider both spatial and temporal coherence for the object. Despite extensive previous work, the problem is still challenging. Usually, foreground object in the video draws more attention from humans, i.e. it is salient. In this thesis we tackle the problem from the aspect of saliency, where saliency means a certain subset of visual information selected by a visual system (human or machine). We present a novel unsupervised method for video object segmentation that considers both low level vision cues and high level motion cues. In our model, video object segmentation can be formulated as a unified energy minimization problem and solved in polynomial time by employing the min-cut algorithm. Specifically, our energy function comprises the unary term and pair-wise interaction energy term respectively, where unary term measures region saliency and interaction term smooths the mutual effects between object saliency and motion saliency. Object saliency is computed in spatial domain from each discrete frame using multi-scale context features, e.g., color histogram, gradient, and graph based manifold ranking. Meanwhile, motion saliency is calculated in temporal domain by extracting phase information of the video. In the experimental section of this thesis, our proposed method has been evaluated on several benchmark datasets. In MSRA 1000 dataset the result demonstrates that our spatial object saliency detection is superior to the state-of-art methods. Moreover, our temporal motion saliency detector can achieve better performance than existing motion detection approaches in UCF sports action analysis dataset and Weizmann dataset respectively. Finally, we show the attractive empirical result and quantitative evaluation of our approach on two benchmark video object segmentation datasets.
ContributorsWang, Yilin (Author) / Li, Baoxin (Thesis advisor) / Wang, Yalin (Committee member) / Cleveau, David (Committee member) / Arizona State University (Publisher)
Created2013
Description
Feature embeddings differ from raw features in the sense that the former obey certain properties like notion of similarity/dissimilarity in it's embedding space. word2vec is a preeminent example in this direction, where the similarity in the embedding space is measured in terms of the cosine similarity. Such language embedding models have seen numerous applications in both language and vision community as they capture the information in the modality (English language) efficiently. Inspired by these language models, this work focuses on learning embedding spaces for two visual computing tasks, 1. Image Hashing 2. Zero Shot Learning. The training set was used to learn embedding spaces over which similarity/dissimilarity is measured using several distance metrics like hamming / euclidean / cosine distances. While the above-mentioned language models learn generic word embeddings, in this work task specific embeddings were learnt which can be used for Image Retrieval and Classification separately.
Image Hashing is the task of mapping images to binary codes such that some notion of user-defined similarity is preserved. The first part of this work focuses on designing a new framework that uses the hash-tags associated with web images to learn the binary codes. Such codes can be used in several applications like Image Retrieval and Image Classification. Further, this framework requires no labelled data, leaving it very inexpensive. Results show that the proposed approach surpasses the state-of-art approaches by a significant margin.
Zero-shot classification is the task of classifying the test sample into a new class which was not seen during training. This is possible by establishing a relationship between the training and the testing classes using auxiliary information. In the second part of this thesis, a framework is designed that trains using the handcrafted attribute vectors and word vectors but doesn’t require the expensive attribute vectors during test time. More specifically, an intermediate space is learnt between the word vector space and the image feature space using the hand-crafted attribute vectors. Preliminary results on two zero-shot classification datasets show that this is a promising direction to explore.
Image Hashing is the task of mapping images to binary codes such that some notion of user-defined similarity is preserved. The first part of this work focuses on designing a new framework that uses the hash-tags associated with web images to learn the binary codes. Such codes can be used in several applications like Image Retrieval and Image Classification. Further, this framework requires no labelled data, leaving it very inexpensive. Results show that the proposed approach surpasses the state-of-art approaches by a significant margin.
Zero-shot classification is the task of classifying the test sample into a new class which was not seen during training. This is possible by establishing a relationship between the training and the testing classes using auxiliary information. In the second part of this thesis, a framework is designed that trains using the handcrafted attribute vectors and word vectors but doesn’t require the expensive attribute vectors during test time. More specifically, an intermediate space is learnt between the word vector space and the image feature space using the hand-crafted attribute vectors. Preliminary results on two zero-shot classification datasets show that this is a promising direction to explore.
ContributorsGattupalli, Jaya Vijetha (Author) / Li, Baoxin (Thesis advisor) / Yang, Yezhou (Committee member) / Venkateswara, Hemanth (Committee member) / Arizona State University (Publisher)
Created2019