Beyond Omakase: Designing Shared Control for Navigation Robots with Blind PeopleAutonomous navigation robots can increase the independence of blind people but often limit user control—following what is called in Japanese an "omakase" approach where decisions are left to the robot. This research investigates ways to enhance user control in social robot navigation, based on two studies conducted with blind participants. The first study, involving structured interviews (N=14), identified crowded spaces as key areas with significant social challenges. The second study (N=13) explored navigation tasks with an autonomous robot in these environments and identified design strategies across different modes of autonomy. Participants preferred an active role, termed the "boss" mode, where they managed crowd interactions, while the "monitor" mode helped them assess the environment, negotiate movements, and interact with the robot. These findings highlight the importance of shared control and user involvement for blind users, offering valuable insights for designing future social navigation robots.2025RKRie Kamikubo et al.University of Maryland, College of InformationReproductive & Women's HealthSocial Robot InteractionCHI
Tracking and its Potential for Older Adults with Memory ConcernsMuch research on older people with memory concerns is focused on tracking and informed by the priorities of others. In this paper, we seek to understand the potential that people with memory concerns see in tracking. We conducted interviews with 29 participants with concerns about their memory and engaged in an affective writing approach. We find a range of potentials that can be traced to how participants are already self-tracking. Emotions associated with these potentials vary: from acceptance to resistance, and positive anticipation to aversion. Participants are emotionally motivated to foreclose possibilities in some instances and keep them open in others. While individual and unique, potential is structured by forces that include individual routines, relationships with others, and macro-level institutions and cultural contexts. We reflect on these findings in the context of research on self-tracking with older adults, designing with ambiguity, and forces that structure the experience of living with memory concerns.2025ASAmelia Short et al.University of MarylandMental Health Apps & Online Support CommunitiesElderly Care & Dementia SupportSleep & Stress MonitoringCHI
Redefining Activity Tracking Through Older Adults' Reflections on Meaningful ActivitiesActivity tracking has the potential to promote active lifestyles among older adults. However, current activity tracking technologies may inadvertently perpetuate ageism by focusing on age-related health risks. Advocating for a personalized approach in activity tracking technology, we sought to understand what activities older adults find meaningful to track and the underlying values of those activities. We conducted a reflective interview study following a 7-day activity journaling with 13 participants. We identified various underlying values motivating participants to track activities they deemed meaningful. These values, whether competing or aligned, shape the desirability of activities. Older adults appreciate low-exertion activities, but they are difficult to track. We discuss how these activities can become central in designing activity tracking systems. Our research offers insights for creating value-driven, personalized activity trackers that resonate more fully with the meaningful activities of older adults.2024YWYiwen Wang et al.University of MarylandFitness Tracking & Physical Activity MonitoringElderly Care & Dementia SupportCHI
Exploring AI Problem Formulation with Children via Teachable MachinesEmphasizing problem formulation in AI literacy activities with children is vital, yet we lack empirical studies on their structure and affordances. We propose that participatory design involving teachable machines facilitates problem formulation activities. To test this, we integrated problem reduction heuristics into storyboarding and invited a university-based intergenerational design team of 10 children (ages 8-13) and 9 adults to co-design a teachable machine. We find that children draw from personal experiences when formulating AI problems; they assume voice and video capabilities, explore diverse machine learning approaches, and plan for error handling. Their ideas promote human involvement in AI, though some are drawn to more autonomous systems. Their designs prioritize values like capability, logic, helpfulness, responsibility, and obedience, and a preference for a comfortable life, family security, inner harmony, and excitement as end-states. We conclude by discussing how these results can inform the design of future participatory AI activities.2024UDUtkarsh Dwivedi et al.University of MarylandGenerative AI (Text, Image, Music, Video)Human-LLM CollaborationParticipatory DesignCHI
Contributing to Accessibility Datasets: Reflections on Sharing Study Data by Blind PeopleTo ensure that AI-infused systems work for disabled people, we need to bring accessibility datasets sourced from this community in the development lifecycle. However, there are many ethical and privacy concerns limiting greater data inclusion, making such datasets not readily available. We present a pair of studies where 13 blind participants engage in data capturing activities and reflect with and without probing on various factors that influence their decision to share their data via an AI dataset. We see how different factors influence blind participants' willingness to share study data as they assess risk-benefit tradeoffs. The majority support sharing of their data to improve technology but also express concerns over commercial use, associated metadata, and the lack of transparency about the impact of their data. These insights have implications for the development of responsible practices for stewarding accessibility datasets, and can contribute to broader discussions in this area.2023RKRie Kamikubo et al.University of Maryland, University of MarylandAI Ethics, Fairness & AccountabilityVisual Impairment Technologies (Screen Readers, Tactile Graphics, Braille)CHI
Supporting Novices Author Audio Descriptions via Automatic FeedbackAudio descriptions (AD) make videos accessible to those who cannot see them. But many videos lack AD and remain inaccessible as traditional approaches involve expensive professional production. We aim to lower production costs by involving novices in this process. We present an AD authoring system that supports novices to write scene descriptions (SD)—textual descriptions of video scenes—and convert them into AD via text-to-speech. The system combines video scene recognition and natural language processing to review novice-written SD and feeds back what to mention automatically. To assess the effectiveness of this automatic feedback in supporting novices, we recruited 60 participants to author SD with no feedback, human feedback, and automatic feedback. Our study shows that automatic feedback improves SD's descriptiveness, objectiveness, and learning quality, without affecting qualities like sufficiency and clarity. Though human feedback remains more effective, automatic feedback can reduce production costs by 45%.2023RNRosiana Natalie et al.Singapore Management UniversityVisual Impairment Technologies (Screen Readers, Tactile Graphics, Braille)Universal & Inclusive DesignGame AccessibilityCHI
MyMove: Facilitating Older Adults to Collect In-Situ Activity Labels on a Smartwatch with SpeechCurrent activity tracking technologies are largely trained on younger adults, which can lead to solutions that are not well-suited for older adults. To build activity trackers for older adults, it is crucial to collect training data with them. To this end, we examine the feasibility and challenges with older adults in collecting activity labels by leveraging speech. Specifically, we built MyMove, a speech-based smartwatch app to facilitate the in-situ labeling with a low capture burden. We conducted a 7-day deployment study, where 13 older adults collected their activity labels and smartwatch sensor data, while wearing a thigh-worn activity monitor. Participants were highly engaged, capturing 1,224 verbal reports in total. We extracted 1,885 activities with corresponding effort level and timespan, and examined the usefulness of these reports as activity labels. We discuss the implications of our approach and the collected dataset in supporting older adults through personalized activity tracking technologies.2022YKYoung-Ho Kim et al.University of MarylandFitness Tracking & Physical Activity MonitoringSmartwatches & Fitness BandsBiosensors & Physiological MonitoringCHI
How Content Authored by People with Dementia Affects Attitudes towards DementiaNegative attitudes shape experiences with stigmatized conditions such as dementia, from affecting social relationships to influencing willingness to adopt technology. Consequently, attitudinal change has been identified as one lever to improve life for people with stigmatized conditions. Though recognized as a scaleable approach, social media has not been studied in terms of how it should best be designed or deployed to target attitudes and understanding of dementia. Through a mixed methods design with 123 undergraduate college students, we study the effect of being exposed to dementia-related media, including content produced by people with dementia. We selected undergraduate college students as the target of our intervention, as they represent the next generation that will work and interact with individuals with dementia. Our analysis describes changes over the period of two weeks in attitudes and understanding of the condition. The shifts in understanding of dementia that we found in our qualitative analysis were not captured by the instrument we selected to assess understanding of dementia. While small improvements in positive and overall attitudes were seen across all interventions and the control, we observe a different pattern with negative attitudes, where transcriptions of content produced by people with dementia significantly reduced negative attitudes. The discussion presents implications for supporting people with dementia as content producers, doing so in ways that best affect attitudes and understanding by drawing on research on cues and interactive media, and supporting students in changing their perspectives towards people with dementia.2021ALAmanda Lazar et al.Support and InclusionCSCW
Pedestrian Detection with Wearable Cameras for the Blind: A Two-way PerspectiveBlind people have limited access to information about their surroundings, which is important for ensuring one's safety, managing social interactions, and identifying approaching pedestrians. With advances in computer vision, wearable cameras can provide equitable access to such information. However, the always-on nature of these assistive technologies poses privacy concerns for parties that may get recorded. We explore this tension from both perspectives, those of sighted passersby and blind users, taking into account camera visibility, in-person versus remote experience, and extracted visual information. We conduct two studies: an online survey with MTurkers (N=206) and an in-person experience study between pairs of blind (N=10) and sighted (N=40) participants, where blind participants wear a working prototype for pedestrian detection and pass by sighted participants. Our results suggest that both of the perspectives of users and bystanders and the several factors mentioned above need to be carefully considered to mitigate potential social tensions.2020KLKyungjun Lee et al.University of MarylandVisual Impairment Technologies (Screen Readers, Tactile Graphics, Braille)Privacy by Design & User ControlCHI
Crowdsourcing the Perception of Machine TeachingTeachable interfaces can empower end-users to attune machine learning systems to their idiosyncratic characteristics and environment by explicitly providing pertinent training examples. While facilitating control, their effectiveness can be hindered by the lack of expertise or misconceptions. We investigate how users may conceptualize, experience, and reflect on their engagement in machine teaching by deploying a mobile teachable testbed in Amazon Mechanical Turk. Using a performance-based payment scheme, Mechanical Turkers (N=100) are called to train, test, and re-train a robust recognition model in real-time with a few snapshots taken in their environment. We find that participants incorporate diversity in their examples drawing from parallels to how humans recognize objects independent of size, viewpoint, location, and illumination. Many of their misconceptions relate to consistency and model capabilities for reasoning. With limited variation and edge cases in testing, the majority of them do not change strategies on a second training attempt.2020JHJonggi Hong et al.University of MarylandGenerative AI (Text, Image, Music, Video)AI-Assisted Decision-Making & AutomationCrowdsourcing Task Design & Quality ControlCHI
#HandsOffMyADA: A Twitter Response to the ADA Education and Reform ActTwitter continues to be used increasingly for communication related advocacy, activism, and social change. This is also the case for the disability community. In light of the recently proposed ADA Education and Reform in the United States, we investigate factors for effectiveness of sharing or retweeting messages about topics affecting the rights of people with disabilities. We perform a multifaceted study of the #HandsOffMyADA campaign against the proposed H.R.620 bill to: (1) explore how communication via Twitter compares to previous disability rights movements; (2) characterize the campaign in terms of hashtags, user groups, and content such as accessible multimedia that contribute to dissemination of campaign messages; (3) identify major themes in tweets and responses, and their variation among user groups; and (4) understand how the disability community mobilized for this campaign compared to previous Twitter initiatives.2019BABrooke E. Auxier et al.University of Maryland, College ParkCyberbullying & Online HarassmentEmpowerment of Marginalized GroupsCHI
Hands Holding Clues for Object Recognition in Teachable MachinesCamera manipulation confounds the use of object recognition applications by blind people. This is exacerbated when photos from this population are also used to train models, as with teachable machines, where out-of-frame or partially included objects against cluttered backgrounds degrade performance. Leveraging prior evidence on the ability of blind people to coordinate hand movements using proprioception, we propose a deep learning system that jointly models hand segmentation and object localization for object classification. We investigate the utility of hands as a natural interface for including and indicating the object of interest in the camera frame. We confirm the potential of this approach by analyzing existing datasets from people with visual impairments for object recognition. With a new publicly available egocentric dataset and an extensive error analysis, we provide insights into this approach in the context of teachable recognizers.2019KLKyungjun Lee et al.University of Maryland, College ParkHuman Pose & Activity RecognitionVisual Impairment Technologies (Screen Readers, Tactile Graphics, Braille)Motor Impairment Assistive Input TechnologiesCHI
Environmental Factors in Indoor Navigation Based on Real-World Trajectories of Blind UsersIndoor localization technologies can enhance quality of life for blind people by enabling them to independently explore and navigate indoor environments. Researchers typically evaluate their systems in terms of localization accuracy and user behavior along planned routes. We propose two measures of path-following behavior: deviation from optimal route and trajectory variability. Through regression analysis of real-world trajectories from blind users, we identify relationships between a) these measures and b) elements of the environment, route characteristics, localization error, and instructional cues that users receive. Our results provide insights into path-following behavior for turn-by-turn indoor navigation and have implications for the design of future interactions. Moreover, our findings highlight the importance of reporting these environmental factors and route properties in similar studies. We present automated and scalable methods for their calculation and to encourage their reporting for better interpretation and comparison of results across future studies.2018HKHernisa Kacorri et al.University of MarylandVisual Impairment Technologies (Screen Readers, Tactile Graphics, Braille)Geospatial & Map VisualizationCHI