Investigating Contextual Notifications to Drive Self-Monitoring in mHealth Apps for Weight MaintenanceMobile health applications for weight maintenance offer self-monitoring as a tool to empower users to achieve health goals (e.g., losing weight); yet maintaining consistent self-monitoring over time proves challenging for users. These apps use push notifications to help increase users’ app engagement and reduce long-term attrition, but they are often ignored by users due to appearing at inopportune moments. Therefore, we analyzed whether delivering push notifications based on time alone or also considering user context (e.g., current activity) affected users’ engagement in a weight maintenance app, in a 4-week in-the-wild study with 30 participants. We found no difference in participants’ overall (across the day) self-monitoring frequency between the two conditions, but in the context-based condition, participants responded faster and more frequently to notifications, and logged their data more timely (as eating/exercising occurs). Our work informs the design of notifications in weight maintenance apps to improve their efficacy in promoting self-monitoring.2024YCYu-Peng Chen et al.University of FloridaSleep & Stress MonitoringNotification & Interruption ManagementCHI
Do You See What I See? A Qualitative Study Eliciting High-Level Visualization ComprehensionDesigners often create visualizations to achieve specific high-level analytical or communication goals. These goals require people to naturally extract complex, contextualized, and interconnected patterns in data. While limited prior work has studied general high-level interpretation, prevailing perceptual studies of visualization effectiveness primarily focus on isolated, predefined, low-level tasks, such as estimating statistical quantities. This study more holistically explores visualization interpretation to examine the alignment between designers' communicative goals and what their audience sees in a visualization, which we refer to as their comprehension. We found that statistics people effectively estimate from visualizations in classical graphical perception studies may differ from the patterns people intuitively comprehend in a visualization. We conducted a qualitative study on three types of visualizations---line graphs, bar graphs, and scatterplots---to investigate the high-level patterns people naturally draw from a visualization. Participants described a series of graphs using natural language and think-aloud protocols. We found that comprehension varies with a range of factors, including graph complexity and data distribution. Specifically, 1) a visualization's stated objective often does not align with people's comprehension, 2) results from traditional experiments may not predict the knowledge people build with a graph, and 3) chart type alone is insufficient to predict the information people extract from a graph. Our study confirms the importance of defining visualization effectiveness from multiple perspectives to assess and inform visualization practices.2024GQGhulam Jilani Quadri et al.University of North CarolinaInteractive Data VisualizationVisualization Perception & CognitionCHI
Reactive or Proactive? How Robots Should Explain FailuresAs robots tackle increasingly complex tasks, the need for explanations becomes essential for gaining trust and acceptance. Explainable robotic systems should not only elucidate failures when they occur but also predict and preemptively explain potential issues. This paper compares explanations from Reactive Systems, which detect and explain failures after they occur, to Proactive Systems, which predict and explain issues in advance. Our study reveals that the Proactive System fosters higher perceived intelligence and trust and its explanations were rated more understandable and timely. Our findings aim to advance the design of effective robot explanation systems, allowing people to diagnose and provide assistance for problems that may prevent a robot from finishing its task.2024GLGregory LeMasurier et al.Explainable AI (XAI)Human-Robot Collaboration (HRC)HRI
(Gestures Vaguely): The Effects of Robots' Use of Abstract Pointing Gestures in Large-Scale EnvironmentsAs robots are deployed into large-scale human environments, they will need to engage in task-oriented dialogues about objects and locations beyond those that can currently be seen. In these contexts, speakers use a wide range of referring gestures beyond those used in the small-scale interaction contexts that HRI research typically investigates. In this work, we thus seek to understand how robots can better generate gestures to accompany their referring language in large-scale interaction contexts. In service of this goal, we present the results of two human-subject studies: (1) a human-human study exploring how human gestures change in large-scale interaction contexts, and to identify human-like gestures suitable to such contexts yet readily implemented on robot hardware; and (2) a human-robot study conducted in a tightly controlled Virtual Reality environment, to evaluate robots' use of those identified gestures. Our results show that robot use of Precise Deictic and Abstract Pointing gestures afford different types of benefits when used to refer to visible vs. non-visible referents, leading us to formulate three concrete design guidelines. These results highlight both the opportunities for robot use of more humanlike gestures in large-scale interaction contexts, as well as the need for future work exploring their use as part of multi-modal communication.2024AHAnnie Huang et al.Hand Gesture RecognitionDomestic RobotsSocial Robot InteractionHRI
Dakter Bari: Introducing Intermediary to Ensure Healthcare Services to Extremely Impoverished PeopleExtremely impoverished people (known as Beggars or Homeless, depending on where they live), are a group of vulnerable citizens that are deprived of necessary healthcare support, consequences of which can be minor to severe, and in some cases, fatal. Bangladesh, having a significant number of them, is no different. One noticeable difference of these beggars compared to similar communities in other parts of the world (e.g., homeless people in the USA) is that technology penetration is near-to-zero for beggars in Bangladesh, which we confirm through our field study. Thus, technology-based (such as app-based, mHealth, etc.) solutions for providing healthcare support, which maybe possible in advanced countries, is not possible in lower-income countries like Bangladesh. However, there does exist multiple healthcare services in Bangladesh intended for beggars and similar communities, which mostly remain underused by the intended population. This scenario presents a unique challenge, where there is a geographical gap between healthcare services and their intended recipients (beggars in our context). We tackle this problem through a carefully-crafted solution Dakter Bari (meaning ``Home of a doctor" in English), that is tailored to the application ecosystem in this context. We extract critical insights from our field study with (N=70) beggars, and from findings, create a pathway for availing lower-cost healthcare solutions using intermediaries. We also conduct field studies with (N=71) possible intermediary partners and (N=10) hospitals to identify the challenges and possibilities of such intermediary based solutions. With insights gained through these field studies, we then design, iteratively develop, deploy, and user-test such a solution in real cases. To penetrate the system further, we design and deploy posters that are easy to understand for the beggar community and report the findings from the system usage data. The usage of the system for more than six months registers 255 service requests and demonstrates its efficacy in bridging the gap we identified through our study.2021MRMd. Aminur Rahman et al.Specialist and Collaborative Work // Algorithmic FairnessCSCW
Learning from Tweets: Opportunities and Challenges to Inform Policy Making During Dengue EpidemicSocial media platforms are now prevalent in almost all aspects of our daily lives. Existing work focusing on the intersection between disease surveillance and social media usually puts emphasis on early prediction of diseases. However, there is a dearth of research that explores the utility of social media in public health crises by framing the responses of general public. In this connection, here we study the responses of general public in response to dengue outbreaks in the scarce-resource settings of Bangladesh. Our purpose is to explore the utility of pervasive social media health data in health research in the limited-resource settings of Bangladesh, where the availability and accessibility to government health data is limited. On the other hand, the lower Internet adoption rate in such areas make it difficult to seamlessly integrate such social media platforms in effective disease planning and intervention programs. Therefore, through this study, we aspire to explore the scope of effective policy making approaches based on the social media health data.2020FSFarhana Shahid et al.Data and Social Media for HealthCSCW
Brain-Computer Interfaces for Artistic ExpressionArtists have been using BCIs for artistic expression since the 1960s. Their interest and creativity is now increasing because of the availability of affordable BCI devices and software that does not require them to invest extensive time in getting the BCI to work or tuning it to their application. Designers of artistic BCIs are often ahead of more traditional BCI researchers in ideas on using BCIs in multimodal and multiparty contexts, where multiple users are involved, and where robustness and efficiency are not the main matters of concern. The aim of this workshop is to look at current (research) activities in BCIs for artistic expression and to identify research areas that are of interest for both BCI and HCI researchers as well as artists/designers of BCI applications.2018ANAnton Nijholt et al.University of TwenteBrain-Computer Interface (BCI) & NeurofeedbackDigital Art Installations & Interactive PerformanceCHI
Computer-Human Interaction Mentoring (CHIMe) 2018HCI is a field where diversity should be considered in the systems we build and study. As such, it is important to cultivate a growing group of diverse researchers with a range of experiences to contribute to difficult design, research, and computational problems. Therefore, the CHIMe organizers invite graduate and undergraduate students to attend. CHIMe intends to provide a welcoming environment for mentoring and collaboration amongst peers, faculty, and industry experts in HCI.2018RBRobin Brewer et al.University of MichiganParticipatory DesignUser Research Methods (Interviews, Surveys, Observation)CHI
Computer-Human Interaction Mentoring (CHIMe) 2018HCI is a field where diversity should be considered in the systems we build and study. As such, it is important to cultivate a growing group of diverse researchers with a range of experiences to contribute to difficult design, research, and computational problems. Therefore, the CHIMe organizers invite graduate and undergraduate students to attend. CHIMe intends to provide a welcoming environment for mentoring and collaboration amongst peers, faculty, and industry experts in HCI.2018RBRobin Brewer et al.University of MichiganMental Health Apps & Online Support CommunitiesUser Research Methods (Interviews, Surveys, Observation)CHI