開始時刻
前のセッションの直後
この勉強会は終了しました。ご参加ありがとうございました。
Space-adaptive algorithms aim to effectively align the virtual with the real to provide immersive user experiences for Augmented Reality(AR) content across various physical spaces. While such measures are reliant on real spatial features, efforts to understand those features from the user’s perspective and reflect them in designing adaptive augmented spaces have been lacking. For this, we compared factors of narrative experience in six spatial conditions during the gameplay of Fragments, a space-adaptive AR detective game. Configured by size and furniture layout, each condition afforded disparate degrees of traversability and visibility. Results show that whereas centered furniture clusters are suitable for higher presence in sufficiently large rooms, the same layout leads to lower narrative engagement. Based on our findings, we suggest guidelines that can enhance the effects of space adaptivity by considering how users perceive and navigate augmented space generated from different physical environments.
Older adults can benefit from technologies that help them to complete everyday tasks. However, they are an often-under-represented population in augmented reality (AR) research. We present the results of a study in which people aged 50 years or older were asked to perform actions by interpreting visual AR prompts in a lab setting. Our results show that users were less successful at completing actions when using ARROW and HIGHLIGHT augmentations than when using ghosted OBJECT or GHOSTHAND augmentations. We found that user confidence in performing actions varied according to action and augmentation type. Users preferred combined AUDIO+TEXT prompts (our control condition) overall, but the GHOSTHAND was the most preferred visual prompt. We discuss reasons for these differences and provide insight for developers of AR content for older adults. Our work provides the first comparative study of AR with older adults in a non-industrial context.
In email interfaces, providing users with reply suggestions may simplify or accelerate correspondence.
While the "success" of such systems is typically quantified using the number of suggestions selected by users, this ignores the impact of social context, which can change how suggestions are perceived.
To address this, we developed a mixed-methods framework involving qualitative interviews and crowdsourced experiments to characterize problematic email reply suggestions.
Our interviews revealed issues with over-positive, dissonant, cultural, and gender-assuming replies, as well as contextual politeness.
In our experiments, crowdworkers assessed email scenarios that we generated and systematically controlled, showing that contextual factors like social ties and the presence of salutations impacts users' perceptions of email correspondence.
These assessments created a novel dataset of human-authored corrections for problematic email replies.
Our study highlights the social complexity of providing suggestions for email correspondence, raising issues that may apply to all social messaging systems.
Participants in text entry studies usually copy phrases or compose novel messages. A composition task mimics actual user behavior and can allow researchers to better understand how a system might perform in reality. A problem with composition is that participants may gravitate towards writing simple text, that is, text containing only common words. Such simple text is insufficient to explore all factors governing a text entry method, such as its error correction features. We contribute to enhancing composition tasks in two ways. First, we show participants can modulate the difficulty of their compositions based on simple instructions. While it took more time to compose difficult messages, they were longer, had more difficult words, and resulted in more use of error correction features. Second, we compare two methods for obtaining a participant's intended text, comparing both methods with a previously proposed crowdsourced judging procedure. We found participant-supplied references were more accurate.
When exploring a new domain through web search, people often struggle to articulate queries because they lack domain-specific language and well-defined informational goals. Perhaps search tools rely too much on the query to understand what a searcher wants. Towards expanding this contextual understanding of a user during exploratory search, we introduce a novel system, CoNotate, which offers query suggestions based on analyzing the searcher's notes and previous searches for patterns and gaps in information. To evaluate this approach, we conducted a within-subjects study where participants (n=38) conducted exploratory searches using a baseline system (standard web search) and the CoNotate system. The CoNotate approach helped searchers issue significantly more queries, and discover more terminology than standard web search. This work demonstrates how search can leverage user-generated content to help people get started when exploring complex, multi-faceted information spaces.
Computational notebooks help data analysts analyze and visualize datasets, and share analysis procedures and outputs. However, notebooks typically combine code (e.g., Python scripts), notes, and outputs (e.g., tables, graphs). The combination of disparate materials is known to hinder the comprehension of notebooks, making it difficult for analysts to collaborate with other analysts unfamiliar with the dataset. To mitigate this problem, we introduce ToonNote, a JupyterLab extension that enables the conversion of notebooks into "data comics.'' ToonNote provides a simplified view of a Jupyter notebook, highlighting the most important results while supporting interactive and free exploration of the dataset. This paper presents the results of a formative study that motivated the system, its implementation, and an evaluation with 12 users, demonstrating the effectiveness of the produced comics. We discuss how our findings inform the future design of interfaces for computational notebooks and features to support diverse collaborators.
Teleoperating industrial manipulators in co-located spaces can be challenging. Facilitating robot teleoperation by providing additional visual information about the environment and the robot affordances using augmented reality (AR), can improve task performance in manipulation and grasping. In this paper, we present two designs of augmented visual cues, that aim to enhance the visual space of the robot operator through hints about the position of the robot gripper in the workspace and in relation to the target. These visual cues aim to improve the distance perception and thus, the task performance. We evaluate both designs against a baseline in an experiment where participants teleoperate a robotic arm to perform pick-and-place tasks. Our results show performance improvements in different levels, reflecting in objective and subjective measures with trade-offs in terms of time, accuracy, and participants' views of teleoperation. These findings show the potential of AR not only in teleoperation, but in understanding the human-robot workspace.
People with low vision experience reduced mobility that affects their physical and mental wellbeing. With augmented reality (AR) glasses, there are new opportunities to provide visual and auditory information that can improve mobility for this vulnerable group. Current research into AR-based mobility aids has focused mainly on the technical aspects, and less emphasis has been placed on understanding the usability and suitability of these aids in people with various levels of visual impairment. In this paper, we present the results of qualitative interviews with 18 participants using HoloLens v1 and eight prototype augmentations to understand how these enhancements are perceived by people with low vision and how these aids should be adjusted to suit their needs. Our results suggested that participants with moderate vision loss could potentially perceive the most benefit from glasses and underlined the importance of extensive customizability to accommodate the needs of a highly varied low vision population.
Augmented Reality (AR) glasses equip users with the tools to modify the visual appearance of their surrounding environment. This might severely impact interpersonal communication, as the conversational partners will no longer share the same visual perception of reality. Grounded in color-in-context theory, we present a potential AR application scenario in which users can modify the color of the environment to achieve subconscious benefits. In a consecutive online survey (N=64), we measured the user's comfort, acceptance of altering and being altered, and how it is impacted by being able to perceive or not perceive the alteration. We identified significant differences depending on (1) who or what is the target of the alteration, (2) which body part is altered, and (3) which relationship the conversational partners share. In light of our quantitative and qualitative findings, we discuss ethical and practical implications for future devices and applications that employ visual alterations.
Augmented Reality (AR) can deliver engaging user experiences that seamlessly meld virtual content with the physical environment. However, building such experiences is challenging due to the developer’s inability to assess how uncontrolled deployment contexts may influence the user experience. To address this issue, we demonstrate a method for rapidly conducting AR experiments and real-world data collection in the user's own physical environment using a privacy-conscious mobile web application. The approach leverages the large number of distinct user contexts accessible through crowdsourcing to efficiently source diverse context and perceptual preference data. The insights gathered through this method complement emerging design guidance and sample-limited lab-based studies. The utility of the method is illustrated by re-examining the design challenge of adapting AR text content to the user's environment. Finally, we demonstrate how gathered design insight can be operationalized to provide adaptive text content functionality in an AR headset.
We present an in-depth analysis of the impact of multi-word suggestion choices from a neural language model on user behaviour regarding input and text composition in email writing. Our study for the first time compares different numbers of parallel suggestions, and use by native and non-native English writers, to explore a trade-off of ``efficiency vs ideation'', emerging from recent literature. We built a text editor prototype with a neural language model (GPT-2), refined in a prestudy with 30 people. In an online study (N=156), people composed emails in four conditions (0/1/3/6 parallel suggestions). Our results reveal (1) benefits for ideation, and costs for efficiency, when suggesting multiple phrases; (2) that non-native speakers benefit more from more suggestions; and (3) further insights into behaviour patterns. We discuss implications for research, the design of interactive suggestion systems, and the vision of supporting writers with AI instead of replacing them.
Subtitles can help improve the understanding of media content. People enable subtitles based on individual characteristics (e.g., language or hearing ability), viewing environment, or media context (e.g., drama, quiz show). However, some people find that subtitles can be distracting and that they negatively impact their viewing experience. We explore the challenges and opportunities surrounding interaction with real-time personalisation of subtitled content. To understand how people currently interact with subtitles, we first conducted an online questionnaire with 102 participants. We used our findings to elicit requirements for a new approach called Adaptive Subtitles that allows the viewer to alter which speakers have subtitles displayed in real-time. We evaluated our approach with 19 participants to understand the interaction trade-offs and challenges within real-time adaptations of subtitled media. Our evaluation findings suggest that granular controls and structured onboarding allow viewers to make informed trade-offs when adapting media content, leading to improved viewing experiences.