Press-n-Paste : Copy-and-Paste Operations with Pressure-sensitive Caret Navigation for Miniaturized Surface in Mobile Augmented Reality

Publisher Copyright: © 2021 ACM.Copy-and-paste operations are the most popular features on computing devices such as desktop computers, smartphones and tablets. However, the copy-and-paste operations are not sufficiently addressed on the Augmented Reality (AR) smartglasses designated for real-time interaction with texts in physical environments. This paper proposes two system solutions, namely Granularity Scrolling (GS) and Two Ends (TE), for the copy-and-paste operations on AR smartglasses. By leveraging a thumb-size button on a touch-sensitive and pressure-sensitive surface, both the multi-step solutions can capture the target texts through indirect manipulation and subsequently enables the copy-and-paste operations. Based on the system solutions, we implemented an experimental prototype named Press-n-Paste (PnP). After the eight-session evaluation capturing 1,296 copy-and-paste operations, 18 participants with GS and TE achieve the peak performance of 17,574 ms and 13,951 ms per copy-and-paste operation, with 93.21% and 98.15% accuracy rates respectively, which are as good as the commercial solutions using direct manipulation on touchscreen devices. The user footprints also show that PnP has a distinctive feature of miniaturized interaction area within 12.65 mm∗14.48 mm. PnP not only proves the feasibility of copy-and-paste operations with the flexibility of various granularities on AR smartglasses, but also gives significant implications to the design space of pressure widgets as well as the input design on smart wearables.Peer reviewe

Exploring user-defined gestures for alternate interaction space for smartphones and smartwatches

2016 Spring.Includes bibliographical references.In smartphones and smartwatches, the input space is limited due to their small form factor. Although many studies have highlighted the possibility of expanding the interaction space for these devices, limited work has been conducted on exploring end-user preferences for gestures in the proposed interaction spaces. In this dissertation, I present the results of two elicitation studies that explore end-user preferences for creating gestures in the proposed alternate interaction spaces for smartphones and smartwatches. Using the data collected from the two elicitation studies, I present gestures preferred by end-users for common tasks that can be performed using smartphones and smartwatches. I also present the end-user mental models for interaction in proposed interaction spaces for these devices, and highlight common user motivations and preferences for suggested gestures. Based on the findings, I present design implications for incorporating the proposed alternate interaction spaces for smartphones and smartwatches

AUGMENTED TOUCH INTERACTIONS WITH FINGER CONTACT SHAPE AND ORIENTATION

Touchscreen interactions are far less expressive than the range of touch that human hands are capable of - even considering technologies such as multi-touch and force-sensitive surfaces. Recently, some touchscreens have added the capability to sense the actual contact area of a finger on the touch surface, which provides additional degrees of freedom - the size and shape of the touch, and the finger's orientation. These additional sensory capabilities hold promise for increasing the expressiveness of touch interactions - but little is known about whether users can successfully use the new degrees of freedom. To provide this baseline information, we carried out a study with a finger-contact-sensing touchscreen, and asked participants to produce a range of touches and gestures with different shapes and orientations, with both one and two fingers. We found that people are able to reliably produce two touch shapes and three orientations across a wide range of touches and gestures - a result that was confirmed in another study that used the augmented touches for a screen lock application

Multi-user Gaze-based Interaction Techniques on Collaborative Touchscreens

Eye-gaze is a technology for implicit, fast, and hands-free input for a variety of use cases, with the majority of techniques focusing on single-user contexts. In this work, we present an exploration into gaze techniques of users interacting together on the same surface. We explore interaction concepts that exploit two states in an interactive system: 1) users visually attending to the same object in the UI, or 2) users focusing on separate targets. Interfaces can exploit these states with increasing availability of eye-tracking. For example, to dynamically personalise content on the UI to each user, and to provide a merged or compromised view on an object when both users' gaze are falling upon it. These concepts are explored with a prototype horizontal interface that tracks gaze of two users facing each other. We build three applications that illustrate different mappings of gaze to multi-user support: an indoor map with gaze-highlighted information, an interactive tree-of-life visualisation that dynamically expands on users' gaze, and a worldmap application with gaze-aware fisheye zooming. We conclude with insights from a public deployment of this system, pointing toward the engaging and seamless ways how eye based input integrates into collaborative interaction

The Graphical Access Challenge for People with Visual Impairments: Positions and Pathways Forward

Graphical access is one of the most pressing challenges for individuals who are blind or visually impaired. This chapter discusses some of the factors underlying the graphics access challenge, reviews prior approaches to addressing this long-standing information access barrier, and describes some promising new solutions. We specifically focus on touchscreen-based smart devices, a relatively new class of information access technologies, which our group believes represent an exemplary model of user-centered, needs-based design. We highlight both the challenges and the vast potential of these technologies for alleviating the graphics accessibility gap and share the latest results in this line of research. We close with recommendations on ideological shifts in mindset about how we approach solving this vexing access problem, which will complement both technological and perceptual advancements that are rapidly being uncovered through a growing research community in this domain

Multi-user Gaze-based Interaction Techniques on Collaborative Touchscreens

Eye-gaze is a technology for implicit, fast, and hands-free input for a variety of use cases, with the majority of techniques focusing on single-user contexts. In this work, we present an exploration into gaze techniques of users interacting together on the same surface. We explore interaction concepts that exploit two states in an interactive system: 1) users visually attending to the same object in the UI, or 2) users focusing on separate targets. Interfaces can exploit these states with increasing availability of eye-tracking. For example, to dynamically personalise content on the UI to each user, and to provide a merged or compromised view on an object when both users’ gaze are falling upon it. These concepts are explored with a prototype horizontal interface that tracks gaze of two users facing each other. We build three applications that illustrate different mappings of gaze to multi-user support: an indoor map with gaze-highlighted information, an interactive tree-of-life visualisation that dynamically expands on users’ gaze, and a worldmap application with gaze-aware fisheye zooming. We conclude with insights from a public deployment of this system, pointing toward the engaging and seamless ways how eye based input integrates into collaborative interaction

Content-aware kinetic scrolling for supporting web page navigation

Long documents are abundant on the web today, and are accessed in increasing numbers from touchscreen devices such as mobile phones and tablets. Navigating long documents with small screens can be challenging both physically and cognitively because they compel the user to scroll a great deal and to mentally filter for important content. To support navigation of long documents on touchscreen devices, we introduce content-aware kinetic scrolling, a novel scrolling technique that dynamically applies pseudo-haptic feedback in the form of friction around points of high interest within the page. This allows users to quickly find interesting content while exploring without further cluttering the limited visual space. To model degrees of interest (DOI) for a variety of existing web pages, we introduce social wear, a method for capturing DOI based on social signals that indicate collective user interest. Our preliminary evaluation shows that users pay attention to items with kinetic scrolling feedback during search, recognition, and skimming tasks.Quanta Computer (Firm)Samsung (Firm) (Fellowship

Computational Modeling and Experimental Research on Touchscreen Gestures, Audio/Speech Interaction, and Driving

As humans are exposed to rapidly evolving complex systems, there are growing needs for humans and systems to use multiple communication modalities such as auditory, vocal (or speech), gesture, or visual channels; thus, it is important to evaluate multimodal human-machine interactions in multitasking conditions so as to improve human performance and safety. However, traditional methods of evaluating human performance and safety rely on experimental settings using human subjects which require costly and time-consuming efforts to conduct. To minimize the limitations from the use of traditional usability tests, digital human models are often developed and used, and they also help us better understand underlying human mental processes to effectively improve safety and avoid mental overload. In this regard, I have combined computational cognitive modeling and experimental methods to study mental processes and identify differences in human performance/workload in various conditions, through this dissertation research. The computational cognitive models were implemented by extending the Queuing Network-Model Human Processor (QN-MHP) Architecture that enables simulation of human multi-task behaviors and multimodal interactions in human-machine systems. Three experiments were conducted to investigate human behaviors in multimodal and multitasking scenarios, combining the following three specific research aims that are to understand: (1) how humans use their finger movements to input information on touchscreen devices (i.e., touchscreen gestures), (2) how humans use auditory/vocal signals to interact with the machines (i.e., audio/speech interaction), and (3) how humans drive vehicles (i.e., driving controls). Future research applications of computational modeling and experimental research are also discussed. Scientifically, the results of this dissertation research make significant contributions to our better understanding of the nature of touchscreen gestures, audio/speech interaction, and driving controls in human-machine systems and whether they benefit or jeopardize human performance and safety in the multimodal and concurrent task environments. Moreover, in contrast to the previous models for multitasking scenarios mainly focusing on the visual processes, this study develops quantitative models of the combined effects of auditory, tactile, and visual factors on multitasking performance. From the practical impact perspective, the modeling work conducted in this research may help multimodal interface designers minimize the limitations of traditional usability tests and make quick design comparisons, less constrained by other time-consuming factors, such as developing prototypes and running human subjects. Furthermore, the research conducted in this dissertation may help identify which elements in the multimodal and multitasking scenarios increase workload and completion time, which can be used to reduce the number of accidents and injuries caused by distraction.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143903/1/heejinj_1.pd