Understanding user interactions in stereoscopic head-mounted displays

2022 Spring.Includes bibliographical references.Interacting in stereoscopic head mounted displays can be difficult. There are not yet clear standards for how interactions in these environments should be performed. In virtual reality there are a number of well designed interaction techniques; however, augmented reality interaction techniques still need to be improved before they can be easily used. This dissertation covers work done towards understanding how users navigate and interact with virtual environments that are displayed in stereoscopic head-mounted displays. With this understanding, existing techniques from virtual reality devices can be transferred to augmented reality where appropriate, and where that is not the case, new interaction techniques can be developed. This work begins by observing how participants interact with virtual content using gesture alone, speech alone, and the combination of gesture+speech during a basic object manipulation task in augmented reality. Later, a complex 3-dimensional data-exploration environment is developed and refined. That environment is capable of being used in both augmented reality (AR) and virtual reality (VR), either asynchronously or simultaneously. The process of iteratively designing that system and the design choices made during its implementation are provided for future researchers working on complex systems. This dissertation concludes with a comparison of user interactions and navigation in that complex environment when using either an augmented or virtual reality display. That comparison contributes new knowledge on how people perform object manipulations between the two devices. When viewing 3D visualizations, users will need to feel able to navigate the environment. Without careful attention to proper interaction technique design, people may struggle to use the developed system. These struggles may range from a system that is uncomfortable and not fit for long-term use, or they could be as major as causing new users to not being able to interact in these environments at all. Getting the interactions right for AR and VR environments is a step towards facilitating their widespread acceptance. This dissertation provides the groundwork needed to start designing interaction techniques around how people utilize their personal space, virtual space, body, tools, and feedback systems

Semi-aural Interfaces: Investigating Voice-controlled Aural Flows

To support mobile, eyes-free web browsing, users can listen to ‘playlists’ of web content— aural flows . Interacting with aural flows, however, requires users to select interface buttons, tethering visual attention to the mobile device even when it is unsafe (e.g. while walking). This research extends the interaction with aural flows through simulated voice commands as a way to reduce visual interaction. This paper presents the findings of a study with 20 participants who browsed aural flows either through a visual interface only or by augmenting it with voice commands. Results suggest that using voice commands reduced the time spent looking at the device by half but yielded similar system usability and cognitive effort ratings as using buttons. Overall, the low-cognitive effort engendered by aural flows, regardless of the interaction modality, allowed participants to do more non-instructed (e.g. looking at the surrounding environment) than instructed activities (e.g. focusing on the user interface)

Designing multimodal interaction for the visually impaired

Although multimodal computer input is believed to have advantages over unimodal input, little has been done to understand how to design a multimodal input mechanism to facilitate visually impaired users\u27 information access. This research investigates sighted and visually impaired users\u27 multimodal interaction choices when given an interaction grammar that supports speech and touch input modalities. It investigates whether task type, working memory load, or prevalence of errors in a given modality impact a user\u27s choice. Theories in human memory and attention are used to explain the users\u27 speech and touch input coordination. Among the abundant findings from this research, the following are the most important in guiding system design: (1) Multimodal input is likely to be used when it is available. (2) Users select input modalities based on the type of task undertaken. Users prefer touch input for navigation operations, but speech input for non-navigation operations. (3) When errors occur, users prefer to stay in the failing modality, instead of switching to another modality for error correction. (4) Despite the common multimodal usage patterns, there is still a high degree of individual differences in modality choices. Additional findings include: (I) Modality switching becomes more prevalent when lower working memory and attentional resources are required for the performance of other concurrent tasks. (2) Higher error rates increases modality switching but only under duress. (3) Training order affects modality usage. Teaching a modality first versus second increases the use of this modality in users\u27 task performance. In addition to discovering multimodal interaction patterns above, this research contributes to the field of human computer interaction design by: (1) presenting a design of an eyes-free multimodal information browser, (2) presenting a Wizard of Oz method for working with visually impaired users in order to observe their multimodal interaction. The overall contribution of this work is that of one of the early investigations into how speech and touch might be combined into a non-visual multimodal system that can effectively be used for eyes-free tasks

Application of Machine Learning within Visual Content Production

We are living in an era where digital content is being produced at a dazzling pace. The heterogeneity of contents and contexts is so varied that a numerous amount of applications have been created to respond to people and market demands. The visual content production pipeline is the generalisation of the process that allows a content editor to create and evaluate their product, such as a video, an image, a 3D model, etc. Such data is then displayed on one or more devices such as TVs, PC monitors, virtual reality head-mounted displays, tablets, mobiles, or even smartwatches. Content creation can be simple as clicking a button to film a video and then share it into a social network, or complex as managing a dense user interface full of parameters by using keyboard and mouse to generate a realistic 3D model for a VR game. In this second example, such sophistication results in a steep learning curve for beginner-level users. In contrast, expert users regularly need to refine their skills via expensive lessons, time-consuming tutorials, or experience. Thus, user interaction plays an essential role in the diffusion of content creation software, primarily when it is targeted to untrained people. In particular, with the fast spread of virtual reality devices into the consumer market, new opportunities for designing reliable and intuitive interfaces have been created. Such new interactions need to take a step beyond the point and click interaction typical of the 2D desktop environment. The interactions need to be smart, intuitive and reliable, to interpret 3D gestures and therefore, more accurate algorithms are needed to recognise patterns. In recent years, machine learning and in particular deep learning have achieved outstanding results in many branches of computer science, such as computer graphics and human-computer interface, outperforming algorithms that were considered state of the art, however, there are only fleeting efforts to translate this into virtual reality. In this thesis, we seek to apply and take advantage of deep learning models to two different content production pipeline areas embracing the following subjects of interest: advanced methods for user interaction and visual quality assessment. First, we focus on 3D sketching to retrieve models from an extensive database of complex geometries and textures, while the user is immersed in a virtual environment. We explore both 2D and 3D strokes as tools for model retrieval in VR. Therefore, we implement a novel system for improving accuracy in searching for a 3D model. We contribute an efficient method to describe models through 3D sketch via an iterative descriptor generation, focusing both on accuracy and user experience. To evaluate it, we design a user study to compare different interactions for sketch generation. Second, we explore the combination of sketch input and vocal description to correct and fine-tune the search for 3D models in a database containing fine-grained variation. We analyse sketch and speech queries, identifying a way to incorporate both of them into our system's interaction loop. Third, in the context of the visual content production pipeline, we present a detailed study of visual metrics. We propose a novel method for detecting rendering-based artefacts in images. It exploits analogous deep learning algorithms used when extracting features from sketches

Gestural product interaction : development and evaluation of an emotional vocabulary

This research explores emotional response to gesture in order to inform future product interaction design. After describing the emergence and likely role of full-body interfaces with devices and systems, the importance of emotional reaction to the necessary movements and gestures is outlined. A gestural vocabulary for the control of a web page is then presented, along with a semantic differential questionnaire for its evaluation. An experiment is described where users undertook a series of web navigation tasks using the gestural vocabulary, then recorded their reaction to the experience. A number of insights were drawn on the context, precision, distinction, repetition and scale of gestures when used to control or activate a product. These insights will be of help in interaction design, and provide a basis for further development of the gestural vocabulary