Text Entry in Immersive Head-Mounted Display-Based Virtual Reality Using Standard Keyboards

We study the performance and user experience of two popular mainstream text entry devices, desktop keyboards and touchscreen keyboards, for use in Virtual Reality (VR) applications. We discuss the limitations arising from limited visual feedback, and examine the efficiency of different strategies of use. We analyze a total of 24 hours of typing data in VR from 24 participants and find that novice users are able to retain about 60% of their typing speed on a desktop keyboard and about 40-45\% of their typing speed on a touchscreen keyboard. We also find no significant learning effects, indicating that users can transfer their typing skills fast into VR. Besides investigating baseline performances, we study the position in which keyboards and hands are rendered in space. We find that this does not adversely affect performance for desktop keyboard typing and results in a performance trade-off for touchscreen keyboard typing

Making Spatial Information Accessible on Touchscreens for Users who are Blind and Visually Impaired

Touchscreens have become a de facto standard of input for mobile devices as they most optimally use the limited input and output space that is imposed by their form factor. In recent years, people who are blind and visually impaired have been increasing their usage of smartphones and touchscreens. Although basic access is available, there are still many accessibility issues left to deal with in order to bring full inclusion to this population. One of the important challenges lies in accessing and creating of spatial information on touchscreens. The work presented here provides three new techniques, using three different modalities, for accessing spatial information on touchscreens. The first system makes geometry and diagram creation accessible on a touchscreen through the use of text-to-speech and gestural input. This first study is informed by a qualitative study of how people who are blind and visually impaired currently access and create graphs and diagrams. The second system makes directions through maps accessible using multiple vibration sensors without any sound or visual output. The third system investigates the use of binaural sound on a touchscreen to make various types of applications accessible such as physics simulations, astronomy, and video games

WearPut : Designing Dexterous Wearable Input based on the Characteristics of Human Finger Motions

Department of Biomedical Engineering (Human Factors Engineering)Powerful microchips for computing and networking allow a wide range of wearable devices to be miniaturized with high fidelity and availability. In particular, the commercially successful smartwatches placed on the wrist drive market growth by sharing the role of smartphones and health management. The emerging Head Mounted Displays (HMDs) for Augmented Reality (AR) and Virtual Reality (VR) also impact various application areas in video games, education, simulation, and productivity tools. However, these powerful wearables have challenges in interaction with the inevitably limited space for input and output due to the specialized form factors for fitting the body parts. To complement the constrained interaction experience, many wearable devices still rely on other large form factor devices (e.g., smartphones or hand-held controllers). Despite their usefulness, the additional devices for interaction can constrain the viability of wearable devices in many usage scenarios by tethering users' hands to the physical devices. This thesis argues that developing novel Human-Computer interaction techniques for the specialized wearable form factors is vital for wearables to be reliable standalone products. This thesis seeks to address the issue of constrained interaction experience with novel interaction techniques by exploring finger motions during input for the specialized form factors of wearable devices. The several characteristics of the finger input motions are promising to enable increases in the expressiveness of input on the physically limited input space of wearable devices. First, the input techniques with fingers are prevalent on many large form factor devices (e.g., touchscreen or physical keyboard) due to fast and accurate performance and high familiarity. Second, many commercial wearable products provide built-in sensors (e.g., touchscreen or hand tracking system) to detect finger motions. This enables the implementation of novel interaction systems without any additional sensors or devices. Third, the specialized form factors of wearable devices can create unique input contexts while the fingers approach their locations, shapes, and components. Finally, the dexterity of fingers with a distinctive appearance, high degrees of freedom, and high sensitivity of joint angle perception have the potential to widen the range of input available with various movement features on the surface and in the air. Accordingly, the general claim of this thesis is that understanding how users move their fingers during input will enable increases in the expressiveness of the interaction techniques we can create for resource-limited wearable devices. This thesis demonstrates the general claim by providing evidence in various wearable scenarios with smartwatches and HMDs. First, this thesis explored the comfort range of static and dynamic touch input with angles on the touchscreen of smartwatches. The results showed the specific comfort ranges on variations in fingers, finger regions, and poses due to the unique input context that the touching hand approaches a small and fixed touchscreen with a limited range of angles. Then, finger region-aware systems that recognize the flat and side of the finger were constructed based on the contact areas on the touchscreen to enhance the expressiveness of angle-based touch input. In the second scenario, this thesis revealed distinctive touch profiles of different fingers caused by the unique input context for the touchscreen of smartwatches. The results led to the implementation of finger identification systems for distinguishing two or three fingers. Two virtual keyboards with 12 and 16 keys showed the feasibility of touch-based finger identification that enables increases in the expressiveness of touch input techniques. In addition, this thesis supports the general claim with a range of wearable scenarios by exploring the finger input motions in the air. In the third scenario, this thesis investigated the motions of in-air finger stroking during unconstrained in-air typing for HMDs. The results of the observation study revealed details of in-air finger motions during fast sequential input, such as strategies, kinematics, correlated movements, inter-fingerstroke relationship, and individual in-air keys. The in-depth analysis led to a practical guideline for developing robust in-air typing systems with finger stroking. Lastly, this thesis examined the viable locations of in-air thumb touch input to the virtual targets above the palm. It was confirmed that fast and accurate sequential thumb touch can be achieved at a total of 8 key locations with the built-in hand tracking system in a commercial HMD. Final typing studies with a novel in-air thumb typing system verified increases in the expressiveness of virtual target selection on HMDs. This thesis argues that the objective and subjective results and novel interaction techniques in various wearable scenarios support the general claim that understanding how users move their fingers during input will enable increases in the expressiveness of the interaction techniques we can create for resource-limited wearable devices. Finally, this thesis concludes with thesis contributions, design considerations, and the scope of future research works, for future researchers and developers to implement robust finger-based interaction systems on various types of wearable devices.ope

Screen or No Screen? Lessons Learnt from a Real-World Deployment Study of Using Voice Assistants With and Without Touchscreen for Older Adults

While voice user interfaces offer increased accessibility due to hands-free and eyes-free interactions, older adults often have challenges such as constructing structured requests and perceiving how such devices operate. Voice-first user interfaces have the potential to address these challenges by enabling multimodal interactions. Standalone voice + touchscreen Voice Assistants (VAs), such as Echo Show, are specific types of devices that adopt such interfaces and are gaining popularity. However, the affordances of the additional touchscreen for older adults are unknown. Through a 40-day real-world deployment with older adults living independently, we present a within-subjects study (N = 16; age M = 82.5, SD = 7.77, min. = 70, max. = 97) to understand how a built-in touchscreen might benefit older adults during device setup, conducting self-report diary survey, and general uses. We found that while participants appreciated the visual outputs, they still preferred to respond via speech instead of touch. We identified six design implications that can inform future innovations of senior-friendly VAs for managing healthcare and improving quality of life

Towards Location-Independent Eyes-Free Text Entry

We propose an interface for eyes-free text entry using an ambiguous technique and conduct a preliminary user study. We find that user are able to enter text at 19.09 words per minute (WPM) with a 2.08% character error rate (CER) after eight hours of practice. We explore ways to optimize the ambiguous groupings to reduce the number of disambiguation errors, both with and without familiarity constraints. We find that it is feasible to reduce the number of ambiguous groups from six to four. Finally, we explore a technique for presenting word suggestions to users using simultaneous audio feedback. We find that accuracy is quite poor when the words are played fully simultaneously, but improves when a slight delay is added before each voice

Improving everyday computing tasks with head-mounted displays

The proliferation of consumer-affordable head-mounted displays (HMDs) has brought a rash of entertainment applications for this burgeoning technology, but relatively little research has been devoted to exploring its potential home and office productivity applications. Can the unique characteristics of HMDs be leveraged to improve users’ ability to perform everyday computing tasks? My work strives to explore this question. One significant obstacle to using HMDs for everyday tasks is the fact that the real world is occluded while wearing them. Physical keyboards remain the most performant devices for text input, yet using a physical keyboard is difficult when the user can’t see it. I developed a system for aiding users typing on physical keyboards while wearing HMDs and performed a user study demonstrating the efficacy of my system. Building on this foundation, I developed a window manager optimized for use with HMDs and conducted a user survey to gather feedback. This survey provided evidence that HMD-optimized window managers can provide advantages that are difficult or impossible to achieve with standard desktop monitors. Participants also provided suggestions for improvements and extensions to future versions of this window manager. I explored the issue of distance compression, wherein users tend to underestimate distances in virtual environments relative to the real world, which could be problematic for window managers or other productivity applications seeking to leverage the depth dimension through stereoscopy. I also investigated a mitigation technique for distance compression called minification. I conducted multiple user studies, providing evidence that minification makes users’ distance judgments in HMDs more accurate without causing detrimental perceptual side effects. This work also provided some valuable insight into the human perceptual system. Taken together, this work represents valuable steps toward leveraging HMDs for everyday home and office productivity applications. I developed functioning software for this purpose, demonstrated its efficacy through multiple user studies, and also gathered feedback for future directions by having participants use this software in simulated productivity tasks

An Ambiguous Technique for Nonvisual Text Entry

Text entry is a common daily task for many people, but it can be a challenge for people with visual impairments when using virtual touchscreen keyboards that lack physical key boundaries. In this thesis, we investigate using a small number of gestures to select from groups of characters to remove most or all dependence on touch locations. We leverage a predictive language model to select the most likely characters from the selected groups once a user completes each word. Using a preliminary interface with six groups of characters based on a Qwerty keyboard, we find that users are able to enter text with no visual feedback at 19.1 words per minute (WPM) with a 2.1% character error rate (CER) after five hours of practice. We explore ways to optimize the ambiguous groups to reduce the number of disambiguation errors. We develop a novel interface named FlexType with four character groups instead of six in order to remove all remaining location dependence and enable one-handed input. We compare optimized groups with and without constraining the group assignments to alphabetical order in a user study. We find that users enter text with no visual feedback at 12.0 WPM with a 2.0% CER using the constrained groups after four hours of practice. There was no significant difference from the unconstrained groups. We improve FlexType based on user feedback and tune the recognition algorithm parameters based on the study data. We conduct an interview study with 12 blind users to assess the challenges they encounter while entering text and solicit feedback on FlexType, and we further incorporate this feedback into the interface. We evaluate the improved interface in a longitudinal study with 12 blind participants. On average, participants entered text at 8.2 words per minute using FlexType, 7.5 words per minute using a Qwerty keyboard with VoiceOver, and at 26.9 words per minute using Braille Screen Input