Browsing by Author "Hu, Donghan"

Now showing 1 - 8 of 8
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    Context-Aware Sit-Stand Desk for Promoting Healthy and Productive Behaviors
    Hu, Donghan; Bae, Joseph; Lim, Sol; Lee, Sang Won (ACM, 2023-10-29)
    To mitigate the risk of chronic diseases caused by prolonged sitting, sit-stand desks are promoted as an effective intervention to foster healthy behaviors among knowledge workers by allowing periodic posture switching between sitting and standing. However, conventional systems either let users manually switch the mode, and some research visited automated notification systems with pre-set time intervals. While this regular notification can promote healthy behaviors, such notification can act as external interruptions that hinder individuals’ working productivity. Notably, knowledge workers are known to be reluctant to change their physical postures when concentrating. To address these issues, we propose considering work context based on their screen activities to encourage computer users to alternate their postures when it can minimize disruption, promoting healthy and productive behaviors. To that end, we are in the process of building a context-aware sit-stand desk that can promote healthy and productive behaviors. To that end, we have completed two modules: an application that monitors users’ computer’s ongoing activities and a sensor module that can measure the height of sit-stand desks for data collection. The collected data includes computer activities, measured desk height, and their willingness to switch to standing modes and will be used to build an LSTM prediction model to suggest optimal time points for posture changes, accompanied by appropriate desk height. In this work, we acknowledge previous relevant research, outline ongoing deployment efforts, and present our plan to validate the effectiveness of our approach via user studies.
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    The Effects of Incorrect Occlusion Cues on the Understanding of Barehanded Referencing in Collaborative Augmented Reality
    Li, Yuan; Hu, Donghan; Wang, Boyuan; Bowman, Douglas A.; Lee, Sang Won (Frontiers, 2021-07-01)
    In many collaborative tasks, the need for joint attention arises when one of the users wants to guide others to a specific location or target in space. If the collaborators are co-located and the target position is in close range, it is almost instinctual for users to refer to the target location by pointing with their bare hands. While such pointing gestures can be efficient and effective in real life, performance will be impacted if the target is in augmented reality (AR), where depth cues like occlusion may be missing if the pointer’s hand is not tracked and modeled in 3D. In this paper, we present a study utilizing head-worn AR displays to examine the effects of incorrect occlusion cues on spatial target identification in a collaborative barehanded referencing task. We found that participants’ performance in AR was reduced compared to a real-world condition, but also that they developed new strategies to cope with the limitations of AR. Our work also identified mixed results of the effect of spatial relationships between users.
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    Exploring the Effectiveness of Time-lapse Screen Recording for Self-Reflection in Work Context
    Hu, Donghan; Lee, Sang Won (ACM, 2024-05-11)
    Effective self-tracking in working contexts empowers individuals to explore and reflect on past activities. Recordings of computer activities contain rich metadata that can offer valuable insight into users’ previous tasks and endeavors. However, presenting a simple summary of time usage may not effectively engage users with data because it is not contextualized, and users may not understand what to do with the data. This work explores time-lapse videos as a visual-temporal medium to facilitate self-refection among workers in productivity contexts. To explore this space, we conducted a four-week study (n = 15) to investigate how a computer screen’s history of states can help workers recall previous undertakings and gain comprehensive insights via self-refection. Our results support that watching time-lapse videos can enhance self-refection more effectively than traditional self-tracking tools by providing contextual clues about users’ past activities. The experience with both traditional tools and time-lapse videos resulted in increased productivity. Additionally, time-lapse videos assist users in cultivating a positive understanding of their work. We discuss how multimodal cues, such as time-lapse videos, can complement personal informatics tools.
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    Investigating Characteristics of Media Recommendation Solicitation in r/ifyoulikeblank
    Bhuiyan, Md Momen; Hu, Donghan; Jelson, Andrew; Mitra, Tanushree; Lee, Sang Won (ACM, 2024-11-08)
    Despite the existence of search-based recommender systems like Google, Netflix, and Spotify, online users sometimes may turn to crowdsourced recommendations in places like the r/ifyoulikeblank subreddit. In this exploratory study, we probe why users go to r/ifyoulikeblank, how they look for recommendation, and how the subreddit users respond to recommendation requests. To answer, we collected sample posts from r/ifyoulikeblank and analyzed them using a qualitative approach. Our analysis reveals that users come to this subreddit for various reasons, such as exhausting popular search systems, not knowing what or how to search for an item, and thinking crowd have better knowledge than search systems. Examining users query and their description, we found novel information users provide during recommendation seeking using r/ifyoulikeblank. For example, sometimes they ask for artifacts recommendation based on the tools used to create them. Or, sometimes indicating a recommendation seeker's time constraints can help better suit recommendations to their needs. Finally, recommendation responses and interactions revealed patterns of how requesters and responders refine queries and recommendations. Our work informs future intelligent recommender systems design.
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    Scrapbook: Screenshot-Based Bookmarks for Effective Digital Resource Curation across Applications
    Hu, Donghan; Lee, Sang Won (ACM, 2022-10-29)
    Modern knowledge workers typically need to use multiple resources, such as documents, web pages, and applications, at the same time. This complexity in their computing environments forces workers to restore various resources in the course of their work. However, conventional curation methods like bookmarks, recent document histories, and file systems place limitations on effective retrieval. Such features typically work only for resources of one type within one application, ignoring the interdependency between resources needed for a single task. In addition, text-based handles do not provide rich cues for users to recognize their associated resources. Hence, the need to locate and reopen relevant resources can significantly hinder knowledge workers’ productivity. To address these issues, we designed and developed Scrapbook, a novel application for digital resource curation across applications that uses screenshot-based bookmarks. Scrapbook extracts and stores all the metadata (URL, file location, and application name) of windows visible in a captured screenshot to facilitate restoring them later. A week-long field study indicated that screenshot-based bookmarks helped participants curate digital resources. Additionally, participants reported that multimodal—visual and textual—data helped them recall past computer activities and reconstruct working contexts efficiently.
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    ScreenTrack: Using Visual History for Self-tracking Computer Activities and Retrieving Working Context
    Hu, Donghan (Virginia Tech, 2019-07-03)
    People spend a significant amount of time using computers at work, at home, or school. Given users switch tasks and are frequently interrupted or distracted while working, reconstructing working context is inevitable. For example, users sometimes need to revisit an arbitrary task from the past to retrieve necessary information (e.g., webpages, files). In this scenario, retrieving working context can be time-consuming or even impossible; users may rely on their memory and may not be able to retrieve the relevant documents that they used before. Sometimes application provides a chronological history of recently opened documents (files, websites). However, it can be challenging to find the right information they need as there are many and users may not recognize from the text-based data (e.g. web page titles, document file name). Therefore, helping them reconstruct mental context and retrieving relevant applications and files can enhance overall productivity. To that end, the concept of self-tracking, which is widely used in health and fitness, is applied to the context of computer usage. In particular, the idea of using a history of a computer screen can provide visuals that users can associate with existing meta-data (file location, web page URL, time). A user can reconstruct working context from the screen visual that they recognize. The idea of using a visual history of a computer screen activities is tested through the development of ScreenTrack, a program that captures a computer screen regularly and let a user watch a time-lapse video made of computer screenshots, and retrieve applications, files, and web pages from a snapshot of a screen. I hypothesize that the chronological history of computer screen activities can effectively help users navigate visual working context and retrieve information that is associated with a snapshot. Through a controlled user study, it was found that participants were able to retrieve information that they were asked more quickly with ScreenTrack than the control condition with statistical significance (p<0.005). Besides, participants gave positive feedback on the software that they would like to use such software in their computers in various context, but expressed potential concerns of using such software for privacy and computer storage problems. In this thesis, I motivate the need of such software, review the related work, share the design consideration, and introduce design and implementation process, validate the effects of ScreenTrack with a controlled user study.
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    Unpacking Task Management Tools, Values, and Worker Dynamics
    Hu, Donghan; Bhuiyan, Md Momen; Lim, Sol; Wiese, Jason; Lee, Sang Won (ACM, 2024-06-25)
    As the complexity of daily tasks grows, knowledge workers experience challenges in managing tasks and risk skipping over some. Fortunately, various task management tools have become available, ranging from traditional tools, such as sticky notes, to complex project management software. In this exploratory study, we aim to understand the landscape of task management tools that knowledge workers use and identify the value they seek from such tools. In addition, we investigate how such value relates to workers’ personality traits and job characteristics. For this purpose, we conducted a series of formative studies and an online survey (𝑁 = 248) to evaluate the perceived importance of various attributes of taskmanagement tools, followed by an exploratory factor analysis to identify the latent structure within that. This process revealed six underlying dimensions for task management tools: communicability, structure, portability, adaptability, physicality, and visualizability. Applying regression analysis, we found connections between latent dimensions and both personality traits and job characteristics. Our findings inform the design of future task management tools with guidance on choosing features and functionality that will meet the needs of their target populations.
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    Using Screenshots as a Medium to Support Knowledge Workers' Productivity
    Hu, Donghan (Virginia Tech, 2024-11-08)
    As computer users increasingly rely on digital tools for daily tasks, the complexity of their working environments continues to grow. Modern knowledge workers must navigate a diverse array of digital resources, including documents, websites, applications, and other information. This complexity presents challenges in managing multiple activities to maintain productivity, such as handling interruptions, resuming tasks, curating resources, recalling context, retrieving previously closed digital resources, and fostering self-reflection. Despite these challenges, there has been limited research on leveraging visual cues to help users reconstruct their previous mental contexts, retrieve digital resources, and enhance self-reflection for behavioral change. Therefore, this Ph.D. dissertation addresses these gaps by focusing on: (1) investigating the existing challenges users face in curating digital resources, (2) designing and implementing supportive applications for task resumption, (3) developing methods that utilize screenshots and metadata for reconstructing mental context and retrieving resources, and (4) enhancing the processes of self-reflection and behavioral change to improve overall productivity.