This thesis explores challenges for the design of Virtual Reality Exposure Therapy (VRET) systems. Exposure therapy is the established method for treatment of anxiety disorders and is typically delivered in-vivo, i.e. exposure to phobic stimulus in real environments. Virtual reality (VR), instead, offers the potential to conduct exposure therapy at the clinic. This approach has several benefits in terms of efficiency, customization and control, amount of exposure, and as an transition phase to real situations. However, currently many systems are limited in scope and are designed for research purposes without informing the design from therapist's practices.  My research aims to contribute towards the understanding of current practices in exposure therapy and investigates challenges for the design of these systems for the two main user groups, patients and therapists. Three different focus areas have been prevalent. First, we have studied therapist in real sessions to inform the design and development of VRET-systems. Second, we have evaluated two different VRET implementations supporting therapists to interact with patients. Third, on the patient's side, we have studied presence on healthy participants focusing on the influence of virtual bodies and patient movement in VR. This thesis summarises and discusses these studies. Overall, the studies emphasize the complexity of exposure therapy and the need for individualized patient conditions. This poses multiple challenges for the design of VRET-systems such as, first, the systems must offer flexibility to the therapists to orchestrate individualized therapy. Second, the systems must enable rich therapists-patient interaction. Third, the complexity of individualization of scenarios and sessions must be addressed in the design of the therapist's interface. Fourth, for patients, body avatars influences presence differently depending on the scenario and locomotion is challenging as offices are typically small. / <p>QC 20190214</p>

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The previous decade has seen breakthroughs in image analysis and computer vision, mainly due to machine learning methods known as deep learning. These methods have since spread to other fields. This thesis aims to survey the progress, highlight problems related to data and computations, and show techniques to mitigate them. In Paper I, we show how to modify the VGG16 classifier architecture to be equivariant to transformations in the p4 group, consisting of translations and specific rotations. We conduct experiments to investigate if baseline architectures, using data augmentation, can be replaced with these rotation-equivariant networks. We train and test on the Oral cancer dataset, used to automate cancer diagnostics. In Paper III, we use a similar methodology as in Paper I to modify the U-net architecture combined with a discriminative loss, for semantic instance segmentation. We test the method on the BBBC038 dataset consisting of highly varied images of cell nuclei. In Paper II, we look at the UCluster method, used to group sub- atomic particles in particle physics. We show how to distribute the training over multiple GPUs using distributed deep learning in a cloud environment. The papers show how to use limited training data more efficiently, using group-equivariant convolutions, to reduce the prob- lems of overfitting. They also demonstrate how to distribute training over multiple nodes in computational centers, which is needed to handle growing data sizes.

As an increasingly powerful, interactive, and dynamic medium for sharing information, the World Wide Web (Web) has found many educational applications. Because of educational benefits for both learners and educators, Web-based courses and programs have increasingly been developed and implemented by many academic institutions, organizations, and companies worldwide (Aggarwal & Brento, 2000). This study, however, asserts that many of the developmental approaches lack three important considerations to be used for implementing learning applications based on the Web: (1) integration of the human-computer interface design with instructional design, (2) development of the evaluation framework to improve the overall quality of Web-based learning support environments, and (3) development of an individualistic approach to the Web hosting of courses. This study explored the three Web-based learning environment weaknesses while developing a user-centered, Web-based learning support environment for Global Positioning System (GPS) education: Web-based distance and distributed learning (WD2L) environment. Research goals of the study were all concerned with the improvement of the design process and usability of the WD2L environment based on a theory-based Integrated Design Process (IDP) proposed in the study. Results indicated that the proposed IDP was effective in that the study showed (1) the WD2L environment's equivalence to traditional supplemental learning, especially as a Web-based supplemental learning program and (2) users' positive perceptions of WD2L environment resources. The study also confirmed that for an e-learning environment to be successful, various aspects of the learning environment as a Web-based supplemental learning program should be considered such as application domain knowledge (i.e., target subject field), conceptual learning theory, instructional design, human-computer interface design, and evaluation about the overall quality of the learning environment. / Ph. D.

Responding to the need within the human-computer interaction field to address ubiquitous and multitasking systems more scientifically, this research extends the usefulness of a new research framework for a particular class of systems. Notification systems are interfaces used in a divided-attention, multitasking situation, attempting to deliver current, valued information through a variety of platforms and modes in an efficient and effective manner. Through review of literature and experiences with empirical dual-task perceptual studies, we recognize a lack of unifying framework for understanding, classifying, analyzing, developing, evaluating, and discussing notification systems--fundamentally inhibiting scientific growth and knowledge reuse that should help designers advance the state-of-the-art. To this end, we developed a framework (referred to as the IRC framework) for notification systems research based on a core taxonomy of critical parameters describing user goals. Next, we extend the framework, focusing on three key aspects: 1) a system description process, allowing articulation of abstract design objectives that focus on critical user requirements; 2) interface usability evaluation tools, enabling comparison of the design and user's models, while supporting generalizability of research and early identification of usability concerns; and 3) design comparison and reuse mechanisms, saving time and effort in requirements analysis and early design stages by enabling design reuse and appreciation of design progress. Results from this research include the development of tools to express IRC design models (IRCspec) and user's models (IRCresults), and the extension of the critical parameters concept. Validation studies with novice designers show sufficient assessment accuracy and consistency. Leveraging these tools that help designers express abstract, yet critical, design intentions and effects as classification and retrieval indices, we develop a repository for reusable design knowledge (a claims library). Responding to challenges of design knowledge access that we observed through initial user testing, we introduce a vision for an integrated design environment (LINK-UP) to operationalize the IRC framework and notification systems claims library in a computer-aided design support system. Proof-of-concept testing results encourage the thought that when valuable design tools embody critical parameters and are coupled with readily accessible reusable design knowledge, interface development will improve as a scientific endeavor. / Ph. D.

This dissertation focuses on taking a human-centric approach to utilize human intelligence best to inform machine learning models. More specifically, the complex relationship between the changes in movement functionality to movement quality. I designed and evaluated the Tacit Computable Empowering methodology across two domains: in-home rehabilitation and clinical assessment. My methodology has three main objectives: first, to transform tacit expert knowledge into explicit knowledge. Second, to transform explicit knowledge into a computable framework that machine learning can understand and replicate. Third, synergize human intelligence with computational machine learning to empower, not replace, the human. Finally, my methodology uses assistive interfaces to allow clinicians and machine learning models to draw parallels between movement functionality and movement quality. The results from my dissertation inform researchers and clinicians on how best to create a standardized framework to capture and assess human movement data for embodied learning scenarios / Doctor of Philosophy / Artificial intelligence (AI) is increasingly considered an important computational design material in the development of innovative products, systems, and services. Recent research emphasizes the potential for computational designers to create new tools, methods, and design processes to more adeptly handle AI and machine learning as fundamental but not exclusive materials within the design process. This talk adopts a human-centric approach to utilize human intelligence to inform machine learning models within a healthcare context. I describe the novel tacit computable empowering (TCE) methodology used and evaluated across two healthcare domains: in-home rehabilitation and clinic-based assessment. The TCE methodology comprises three main objectives: 1) to transform tacit expert knowledge into explicit knowledge; 2) to transform explicit knowledge into a computable framework that machine learning can understand and replicate and 3) to synergize human intelligence with computational machine learning to empower (and not replace) the human. This methodology uses assistive interfaces to allow clinicians and machine learning models to draw parallels between movement functionality and movement quality. Outcomes from this work inform researchers and clinicians as to how to best create a standardized framework to capture and assess human movement data for embodied learning scenarios.

This thesis presents a comprehensive research study aimed at designing an information dashboard to address the specific information needs of dairy farmers in relation to animal health and reproduction data. The research focused on answering two key research questions: (1) How can we determine which factors are most relevant in terms of farms productivity and wellbeing of its animals? and (2) How can we visualize the data and farms history in a meaningful way so that the owner can make sense of it and therefore make better decisions for future planning?To answer these questions, extensive user research was conducted within the dairy farming community, involving interviews, literature review and surveys. The findings revealed that dairy farmers required quick access to critical data related to animal health and reproduction to make informed decisions. Applying a user-centered design approach, iterative prototyping and usability testing sessions were conducted to refine the dashboard design based on feedback from farmers. The goal was to create a user-friendly tool that addressed the specific needs of dairy farmers, including clear differentiation between data pertaining to individual animals versus the entire herd.The outcome of this research was the development of an information dashboard that successfully met the information needs of dairy farmers. The dashboard provided easy access to essential data, empowering farmers to make informed decisions regarding animal health and reproduction.Further testing and refinement of the dashboard design are recommended to ensure its effectiveness and usability in real-world farming scenarios. Additionally, future investigations could explore the inclusion of breeding value information in the dashboard. This research contributes to the transformation of the dairy farming landscape, offering farmers enhanced information management capabilities and improved decision-making processes.

Current video conferencing tools lack a sense of presence. Telepresence can improve the current video conferencing by providing feeling of presence at a different location from remote location. Most recent telepresence systems are built with the devices that are not accessible and uncomfortable for the daily meeting purpose. This work proposes a framework for embodied telepresence system that suits a daily meeting case the best. Based on our new telepresence framework, a new system architecture and design requirements are constructed. The system architecture shows how the telepresence system needs to be structured, and a design requirement helps to understand the needs of the system. With this framework we were able to implement a user friendly and accessible telepresence system. Our telepresence system enables users to control the telepresence robot with smartphone controller. The controller has four features: (1) Smartphone orientation control, (2) Position save and playback, (3) Local smart light bulb control, and (4) visual cue. At the end, our work evaluates the developed telepresence system by measuring the performances of given tasks to the participants. The evaluation shows that our system provides a sense of presence to both remote and local users. However, the proposed telepresence framework and system requires farther improvements to provide better usability. / Master of Science / During the pandemic, video conferencing tools like Zoom, Microsoft Teams, or Google Meet showed the advantages of having meetings and working remotely. However, these tools do not provide a sense of presence and the necessary level of control of what can be seen from a remote user's point of view. Therefore, researchers investigated and developed various tools that can give remote users a sense of presence at a location where a face-to-face meeting is taking place. We call this a telepresence tool. Our systematic review of the current telepresence tool results that most of the telepresence tools use devices that are not familiar and hard to access for general users. Additionally, they do not consider the local users feeling about remote user's presence at the face-to-face meeting (local site). Therefore, in this paper, we propose a general guideline or framework to help build a telepresence tool that overcomes the current telepresence tools' problems. Our telepresence tool, developed based on our proposed framework, uses a smartphone to control the telepresence robot that represents a remote user at the local site. A remote user can control the local site light bulb, save the telepresence robot's position and place it back, and show the user is away or present at the meeting. The evaluation of our telepresence system shows that our system provides a sense of presence to both remote and local users