As generative AI (GenAI) is integrated into everyday technologies, it offers new accessibility opportunities and risks for disabled people. However, little is known about how disabled people navigate GenAI in their everyday lives, particularly how trust, privacy, and intersectional identities affect these experiences. We present findings from seven cross-disability focus groups (N=20) that explore how disabled people navigate GenAI. Our findings reveal that while GenAI supports autonomy, efficiency, and communication, it also introduces accessibility taxes and ethical dilemmas. Although participants voiced skepticism, many continued using GenAI out of necessity. Finally, we found identity-based benefits and tensions, in which GenAI preserved and validated intersecting identities, but also misrepresented and erased those identities. We frame these negotiations as a constant balancing act between access and risk, urging research to further examine how "access" is conceptualized. We offer implications for creating GenAI tools that are transparent, trustworthy, and responsive to intersectional identities.

Blind and visually impaired (BVI) computer science students face systematic barriers when learning data structures: current accessibility approaches typically translate diagrams into alternative text, focusing on visual appearance rather than preserving the underlying structure essential for conceptual understanding. More accessible alternatives often do not scale in complexity, cost to produce, or both. Motivated by a recent shift to tools for creating visual diagrams from code, we propose a solution that automatically creates accessible representations from structural information about diagrams. Based on a Wizard-of-Oz study, we derive design requirements for an automated system, Arboretum, that compiles text-based diagram specifications into three synchronized nonvisual formats-tabular, navigable, and tactile. Our evaluation with BVI users highlights the strength of tactile graphics for complex tasks such as binary search; the benefits of offering multiple, complementary nonvisual representations; and limitations of existing digital navigation patterns for structural reasoning. This work reframes access to data structures by preserving their structural properties. The solution is a practical system to advance accessible CS education.

Disability culture celebrates the diversity disability brings.Its consideration of the positive aspects of the disability experience (community, solidarity, creativity) offers a contrast to many other framings of disability.Disability culture thus has the potential to deepen understandings of accessibility and inform approaches to the design and research of accessibility technologies.To explore this potential, we begin by presenting a preliminary synthesis of disability culture for the accessibility research community, based on works of disability studies scholars and activists.We highlight cultural processes of finding community and building solidarity, valuing disabled agency and knowledge, and rejecting ableist norms.To see how these cultural aspects might inform accessibility technology design, we studied accessibility technologies made by disabled people for disabled people -interviewing disabled innovators who had created and disseminated accessibility technologies.We asked these innovators to share their stories and reflect on goals and values they imbued in their innovations.We analyzed how cultural themes of belonging, knowledge, and creativity influenced their work.Our work highlights the potential of a cultural lens in aligning accessibility technology with disabled people's values as well as unearthing new directions for inquiry for the field.

In accessibility research, the choice to adopt or abandon assistive technologies (AT) is often treated as a stable proxy for functional fit: to adopt is to confirm a good fit between device features and individual needs, and to abandon is to signal poor fit. While useful for design, we argue that the framework is ill-equipped to account for the sociopolitical forces that shape AT use in historically underserved communities. In this paper, we propose a power-aware framework that recasts adoption not as a transparent expression of fit, but as situated negotiation of power. Drawing from an eight-month ethnographic study at a local nonprofit, we examine how low-income, racially-diverse, and disabled families navigate institutional cultures that reinforce normative expectations around disability and AT use. Building on postcolonial theories of power, we introduce the concept of minor resistance to describe the subtle, everyday tactics through which individuals lower the cost of access on their own terms. We argue that this shift in analytical lens reframes the goal of accessibility design from optimizing use to lowering the cost of choice. We conclude with implications for how designers can support community-driven responses to structural barriers by centering self-determination.

Accessibility research has a broad mandate: use technology to make the world more accessible to disabled people. Yet, as a field, accessibility research lacks a clear characterization of what "accessibility" is. Furthermore, it has been historically limited in who is designed for, focusing on specific types of disability and often failing to consider how disability intersects with other identities. We set out to explicate what it means to make something accessible, grounded in the lived experiences of a diverse group of 25 disabled people. From our empirical findings, we develop a process for modeling accessibility. First, an individual assesses their experience of inaccess, specifically, the type of barrier they face, the technology repertoire they possess, and the contextual factors that shape how they address accessibility barriers. Then, having assessed an access barrier, they perform consequence calculus, weighing all available options to achieve access and deciding upon the option that best matches their priorities. We highlight the situated nature of access; people's identities, contextual factors, repertoires, and priorities all dictate their experience of accessibility.

Tactile graphics present visual information to blind and visually-impaired individuals in an accessible way, through touch. Current methods for producing tactile graphics, such as embossing or swell-paper printing, have limitations such as durability - and the tools required to produce them are limited in expressiveness. In this project, we explore embroidery as a medium for producing tactile graphics. Embroidery, traditionally known for its variety and visual beauty, offers not just improved durability and ease of production - but the ability to convey information through a broad range of stitch types. Following an exploration of the design space of embroidered tactile graphics, we identify key perceptual properties that impact how embroidered textures are differentiated. Based on these differences, we introduce an optimization algorithm for assigning textures to regions of tactile graphics in a way that makes them diverse and legible. We implement an end-to-end pipeline for producing embroidered tactile graphics and evaluate the comprehensibility and legibility of our design with 6 blind participants. Our findings showed that embroidered tactile graphics present information accurately and comprehensively, and that measurable properties, such as the use of spacing and distinctiveness, were an important factor of expressive and legible design.

A growing body of research investigates how to make captioning experiences more accessible and enjoyable to disabled people. However, prior work has focused largely on English captioning, neglecting the majority of people who are multilingual (i.e., understand or express themselves in more than one language). To address this gap, we conducted semi-structured interviews and diary logs with 13 participants who used multilingual captions for accessibility. Our findings highlight the linguistic and cultural dimensions of captioning, detailing how language features (scripts and orthography) and the inclusion/negation of cultural context shape the accessibility of captions. Despite lack of quality and availability, participants emphasized the importance of multilingual captioning to learn a new language, build community, and preserve cultural heritage. Moving toward a future where all ways of communicating are celebrated, we present ways to orient captioning research to a language justice agenda that decenters English and engages with varied levels of fluency.

Generative AI (AI) has become ubiquitous in both daily and professional life, with emerging research demonstrating its potential as a tool for accessibility. Neurodivergent people, often left out by existing accessibility technologies, develop their own ways of navigating normative expectations. GAI offers new opportunities for access, but it is important to understand how neurodivergent "power users"-successful early adopters-engage with it and the challenges they face. Further, we must understand how marginalization and intersectional identities influence their interactions with GAI. Our autoethnography, enhanced by privacy-preserving GAI-based diaries and interviews, reveals the intricacies of using GAI to navigate normative environments and expectations. Our findings demonstrate how GAI can both support and complicate tasks like code-switching, emotional regulation, and accessing information. We show that GAI can help neurodivergent users to reclaim their agency in systems that diminish their autonomy and self-determination. However, challenges such as balancing authentic self-expression with societal conformity, alongside other risks, create barriers to realizing GAI's full potential for accessibility.

We present MatplotAlt, an open-source Python package for easily adding alternative text to Matplotlib figures. MatplotAlt equips Jupyter notebook authors to automatically generate and surface chart descriptions with a single line of code or command, and supports a range of options that allow users to customize the generation and display of captions based on their preferences and accessibility needs. Our evaluation indicates that MatplotAlt's heuristic and LLM-based methods to generate alt text can create accurate long-form descriptions of both simple univariate and complex Matplotlib figures. We find that state-of-the-art LLMs still struggle with factual errors when describing charts, and improve the accuracy of our descriptions by prompting GPT4-turbo with heuristic-based alt text or data tables parsed from the Matplotlib figure.

Supporting student success requires collaboration among multiple stakeholders. Researchers have explored machine learning models for academic performance prediction; yet key challenges remain in ensuring these models are interpretable, equitable, and actionable within real-world educational support systems. First, many models prioritize predictive accuracy but overlook human-centered principles, limiting trust among students and reducing their usefulness for educators and institutional decision-makers. Second, most models require at least a month of data before making reliable predictions, delaying opportunities for early intervention. Third, current models primarily rely on sporadically collected, classroom-derived data, missing broader behavioral patterns that could provide more continuous and actionable insights. To address these gaps, we present three modeling approaches-LR, 1D-CNN, and MTL-1D-CNN-to classify students as low or high academic performers. We evaluate them based on explainability , fairness , and generalizability to assess their alignment with key social values. Using behavioral and self-reported data collected within the first week of two Spring terms, we demonstrate that these models can identify at-risk students as early as week one. However, trade-offs across human-centered principles highlight the complexity of designing predictive models that effectively support multi-stakeholder decision-making and intervention strategies. We discuss these trade-offs and their implications for different stakeholders, outlining how predictive models can be integrated into student support systems. Finally, we examine broader socio-technical challenges in deploying these models and propose future directions for advancing human-centered, collaborative academic prediction systems.

Slide deck accessibility is often studied for people who are blind or visually impaired, but rarely for other people with access needs. We first conducted focus groups with 17 people with slide deck access needs and found that their access needs differed greatly and often conflicted. Moreover, some people’s access needs changed throughout the day (e.g., needing lower contrast colors at night). Therefore, we conducted a design probe with 14 of the existing participants to understand the experience of using a plug-in that lets audience members at a presentation modify a local copy of the slides to meet their accessibility needs. We then interviewed four slide deck authors and presenters to offer a preview of the perspectives that other stakeholders of this tool might have. Finally, we created a functional prototype as a Google Slides plug-in with a subset of the features requested by the participants.

Touchpads are used widely to interact with computers, yet they provide minimal utility for screen-reader users. We explore the utility of touchpads as input devices for screen-reader users through the development and preliminary evaluation of Touchpad Mapper. This system maps digital content (i.e., images and videos) to the physical coordinates of a touchpad. We examined two usage scenarios: (1) identification of objects and their relative positioning in an image and (2) controlling a video seek bar and slider with rewinding and fast-forwarding features. We conducted task-based semi-structured interviews with two screen-reader users to assess our system’s performance. The participants reported positive experiences, highlighting that they extracted information faster using our system than the conventional keyboard-only interaction.

With the recent rapid rise in Generative Artificial Intelligence (GAI) tools, it is imperative that we understand their impact on people with disabilities, both positive and negative. However, although we know that AI in general poses both risks and opportunities for people with disabilities, little is known specifically about GAI in particular. To address this, we conducted a three-month autoethnography of our use of GAI to meet personal and professional needs as a team of researchers with and without disabilities. Our findings demonstrate a wide variety of potential accessibility-related uses for GAI while also highlighting concerns around verifiability, training data, ableism, and false promises.

Examinations of intersectionality and identity dimensions in accessibility research have primarily considered disability separately from a person’s race and ethnicity. Accessibility work often does not include considerations of race as a construct, or treats race as a shallow demographic variable, if race is mentioned at all. The lack of attention to race as a construct in accessibility research presents an oversight in our field, often systematically eliminating whole areas of need and vital perspectives from the work we do. Further, there has been little focus on the intersection of race and disability within accessibility research, and the relevance of their interplay. When research in race or disability does not mention the other, this work overlooks the potential to better understand the full nuance of marginalized and “otherized” groups. To address this gap, we present a series of case studies exploring the potential for research that lies at the intersection of race and disability. We provide examples of how to integrate racial equity perspectives into accessibility research, through positive examples found in these case studies and reflect on teaching at the intersection of race, disability, and technology. This paper highlights the value of considering how constructs of race and disability work alongside each other within accessibility research studies, designs of socio-technical systems, and education. Our analysis provides recommendations towards establishing this research direction.

People with disabilities face extra hardship in institutions of higher education because of accessibility barriers built into the educational system. While prior work investigates the needs of individual stakeholders, this work offers insights into the communication and collaboration between key stakeholders in creating access in institutions of higher education. The authors present reflections from their experiences working with disability service offices to meet their access needs and the results from interviewing 6 professors and 6 other disabled students about their experience in achieving access. Our results indicate that there are rich opportunities for technological solutions to support these stakeholders in communicating about and creating access.

The future of accessibility research is a topic we take up every day as researchers; yet it is important also to step back and ask ourselves about the most important, and overlooked, areas for inquiry in our field. With the rapid pace of change in both computational capabilities and the environmental, social, and political context in which disability plays out, we believe this is a critical time for such inquiry. This is even more important given the relatively narrow set of topics that accessibility research has focused on for most of our field’s history. We invite our community to come together to define what the next generation of accessibility research should engage with.

Computational notebooks, tools that facilitate storytelling through exploration, data analysis, and information visualization, have become the widely accepted standard in the data science community. These notebooks have been widely adopted through notebook software such as Jupyter, Datalore and Google Colab, both in academia and industry. While there is extensive research to learn how data scientists use computational notebooks, identify their pain points, and enable collaborative data science practices, very little is known about the various accessibility barriers experienced by blind and visually impaired (BVI) users using these notebooks. BVI users are unable to use computational notebook interfaces due to (1) inaccessibility of the interface, (2) common ways in which data is represented in these interfaces, and (3) inability for popular libraries to provide accessible outputs. We perform a large scale systematic analysis of 100000 Jupyter notebooks to identify various accessibility challenges in published notebooks affecting the creation and consumption of these notebooks. Through our findings, we make recommendations to improve accessibility of the artifacts of a notebook, suggest authoring practices, and propose changes to infrastructure to make notebooks accessible.

Dashboards are frequently used to monitor and share data across a breadth of domains including business, finance, sports, public policy, and healthcare, just to name a few. The combination of different components (e.g., key performance indicators, charts, filtering widgets) and the interactivity between components makes dashboards powerful interfaces for data monitoring and analysis. However, these very characteristics also often make dashboards inaccessible to blind and low vision (BLV) users. Through a co-design study with two screen reader users, we investigate challenges faced by BLV users and identify design goals to support effective screen reader-based interactions with dashboards. Operationalizing the findings from the co-design process, we present a prototype system, Azimuth, that generates dashboards optimized for screen reader-based navigation along with complementary descriptions to support dashboard comprehension and interaction. Based on a follow-up study with five BLV participants, we showcase how our generated dashboards support BLV users and enable them to perform both targeted and open-ended analysis. Reflecting on our design process and study feedback, we discuss opportunities for future work on supporting interactive data analysis, understanding dashboard accessibility at scale, and investigating alternative devices and modalities for designing accessible visualization dashboards.

Always-on, upper-body input from sensors like accelerometers, infrared cameras, and electromyography hold promise to enable accessible gesture input for people with upper-body motor impairments. When these sensors are distributed across the person's body, they can enable the use of varied body parts and gestures for device interaction. Personalized upper-body gestures that enable input from diverse body parts including the head, neck, shoulders, arms, hands and fingers and match the abilities of each user, could be useful for ensuring that gesture systems are accessible. In this work, we characterize the personalized gesture sets designed by 25 participants with upper-body motor impairments and develop design recommendations for upper-body personalized gesture interfaces. We found that the personalized gesture sets that participants designed were highly ability-specific. Even within a specific type of disability, there were significant differences in what muscles participants used to perform upper-body gestures, with some pre-dominantly using shoulder and upper-arm muscles, and others solely using their finger muscles. Eight percent of gestures that participants designed were with their head, neck, and shoulders, rather than their hands and fingers, demonstrating the importance of tracking the whole upper-body. To combat fatigue, participants performed 51% of gestures with their hands resting on or barely coming off of their armrest, highlighting the importance of using sensing mechanisms that are agnostic to the location and orientation of the body. Lastly, participants activated their muscles but did not visibly move during 10% of the gestures, demonstrating the need for using sensors that can sense muscle activations without movement. Both inertial measurement unit (IMU) and electromyography (EMG) wearable sensors proved to be promising sensors to differentiate between personalized gestures. Personalized upper-body gesture interfaces that take advantage of each person's abilities are critical for enabling accessible upper-body gestures for people with upper-body motor impairments.

Speechreading is the art of using visual and contextual cues in the environment to support listening. Often used by d/Deaf and Hard-of-Hearing (d/DHH) individuals, it highlights nuances of rich communication. However, lived experiences of speechreaders are underdocumented in HCI literature, and the impact of online environments and interactions of captioning with speechreading has not been explored in depth. We bridge these gaps through a three-part study consisting of formative interviews, design probes, and design sessions with 12 d/DHH individuals who speechread. Our primary contribution is to understand the lived experience of speechreading in online communication, and thus to better understand the richness and variety of techniques d/DHH individuals use to provision access. We highlight technical, environmental and sociocultural factors that impact communication accessibility, explore the design space of speechreading supports and share considerations for the design future of speechreading technology.

For non-technical domain experts and designers it can be a substantial challenge to create designs that meet domain specific goals. This presents an opportunity to create specialized tools that produce optimized designs in the domain. However, implementing domain-specific optimization methods requires a rare combination of programming and domain expertise. Creating flexible design tools with re-configurable optimizers that can tackle a variety of problems in a domain requires even more domain and programming expertise. We present OPTIMISM, a toolkit which enables programmers and domain experts to collaboratively implement an optimization component of design tools. OPTIMISM supports the implementation of metaheuristic optimization methods by factoring them into easy to implement and reuse components: objectives that measure desirable qualities in the domain, modifiers which make useful changes to designs, design and modifier selectors which determine how the optimizer steps through the search space, and stopping criteria that determine when to return results. Implementing optimizers with OPTIMISM shifts the burden of domain expertise from programmers to domain experts.

Knitting machines can fabricate complex fabric structures using robust industrial fabrication machines. However, machine knitting’s full capabilities are only available through low-level programming languages that operate on individual machine operations. We present KnitScript, a domain-specific machine knitting scripting language that supports computationally driven knitting designs. KnitScript provides a comprehensive virtual model of knitting machines, giving access to machine-level capabilities as they are needed while automating a variety of tedious and error-prone details. Programmers can extend KnitScript with Python programs to create more complex programs and user interfaces. We evaluate the expressivity of KnitScript through a user study where nine machine knitters used KnitScript code to modify knitting patterns. We demonstrate the capabilities of KnitScript through three demonstrations where we create: a program for generating knitted figures of randomized trees, a parameterized hat template that can be modified with accessibility features, and a pattern for a parametric mixed-material lampshade. KnitScript advances the state of machine-knitting research by providing a platform to develop and share complex knitting algorithms, design tools, and patterns. 1

The U.S. National Institute of Health (NIH) 3D Print Exchange is a public, open-source repository for 3D printable medical device designs with contributions from clinicians, expert-amateur makers, and people from industry and academia. In response to the COVID-19 pandemic, the NIH formed a collection to foster submissions of low-cost, locally manufacturable personal protective equipment (PPE) . We evaluated the 623 submissions in this collection to understand: what makers contributed, how they were made, who made them, and key characteristics of their designs. We found an immediate design convergence to manufacturing-focused remixes of a few initial designs affiliated with NIH partners and major for-profit groups. The NIH worked to review safe, effective designs but was overloaded by manufacturing-focused design adaptations. Our work contributes insights into: the outcomes of distributed, community-based medical making; the features that the community accepted as “safe” making; and how platforms can support regulated maker activities in high-risk domains.

During the COVID-19 pandemic, risk negotiation became an important precursor to in-person contact. For young adults, social planning generally occurs through computer-mediated communication. Given the importance of social connectedness for mental health and academic engagement, we sought to understand how young adults plan in-person meetups over computer-mediated communication in the context of the pandemic. We present a qualitative study that explores young adults’ risk negotiation during the COVID-19 pandemic, a period of conflicting public health guidance. Inspired by cultural probe studies, we invited participants to express their preferred precautions for one week as they planned in-person meetups. We interviewed and surveyed participants about their experiences. Through qualitative analysis, we identify strategies for risk negotiation, social complexities that impede risk negotiation, and emotional consequences of risk negotiation. Our findings have implications for AI-mediated support for risk negotiation and assertive communication more generally. We explore tensions between risks and potential benefits of such systems.

Tactile maps can help people who are blind or have low-vision navigate and familiarize themselves with unfamiliar locations. Ideally, tactile maps can be customized to an individual’s unique needs and abilities because of their limited space for representation. We present Maptimizer, a tool that generates tactile maps based on users’ preferences and requirements. Maptimizer uses a two stage optimization process to pair representations with geographic information and tune those representations to present that information more clearly. In a small user study, Maptimizer helped participants more successfully and efficiently identify locations of interest in unknown areas. These results demonstrate the utility of optimization techniques and generative design in complex accessibility domains.

Methods are fundamental to doing research and can directly impact who is included in scientific advances. Given accessibility research's increasing popularity and pervasive barriers to conducting and participating in research experienced by people with disabilities, it is critical to ask how methods are made accessible. Yet papers rarely describe their methods in detail. This paper reports on 17 interviews with accessibility experts about how they include both facilitators and participants with disabilities in popular user research methods. Our findings offer strategies for anticipating access needs while remaining flexible and responsive to unexpected access barriers. We emphasize the importance of considering accessibility at all stages of the research process, and contextualize access work in recent disability and accessibility literature. We explore how technology or processes could reflect a norm of accessibility. Finally, we discuss how various needs intersect and conflict and offer a practical structure for planning accessible research.

Smartphone overuse is related to a variety of issues such as lack of sleep and anxiety. We explore the application of Self-Affirmation Theory on smartphone overuse intervention in a just-in-time manner. We present TypeOut, a just-in-time intervention technique that integrates two components: an in-situ typing-based unlock process to improve user engagement, and self-affirmation-based typing content to enhance effectiveness. We hypothesize that the integration of typing and self-affirmation content can better reduce smartphone overuse. We conducted a 10-week within-subject field experiment (N=54) and compared TypeOut against two baselines: one only showing the self-affirmation content (a common notification-based intervention), and one only requiring typing non-semantic content (a state-of-the-art method). TypeOut reduces app usage by over 50%, and both app opening frequency and usage duration by over 25%, all significantly outperforming baselines. TypeOut can potentially be used in other domains where an intervention may benefit from integrating self-affirmation exercises with an engaging just-in-time mechanism.

Recent research has demonstrated the capability of behavior signals captured by smartphones and wearables for longitudinal behavior modeling. However, there is a lack of a comprehensive public dataset that serves as an open testbed for fair comparison among algorithms. Moreover, prior studies mainly evaluate algorithms using data from a single population within a short period, without measuring the cross-dataset generalizability of these algorithms. We present the first multi-year passive sensing datasets, containing over 700 user-years and 497 unique users’ data collected from mobile and wearable sensors, together with a wide range of well-being metrics. Our datasets can support multiple cross-dataset evaluations of behavior modeling algorithms’ generalizability across different users and years. As a starting point, we provide the benchmark results of 18 algorithms on the task of depression detection. Our results indicate that both prior depression detection algorithms and domain generalization techniques show potential but need further research to achieve adequate cross-dataset generalizability. We envision our multi-year datasets can support the ML community in developing generalizable longitudinal behavior modeling algorithms.

We present the first toolkit that equips blind and visually impaired (BVI) developers with the tools to create accessible data displays. Called PSST (Physical computing Streaming Sensor data Toolkit), it enables BVI developers to understand the data generated by sensors from a mouse to a micro:bit physical computing platform. By assuming visual abilities, earlier efforts to make physical computing accessible fail to address the need for BVI developers to access sensor data. PSST enables BVI developers to understand real-time, real-world sensor data by providing control over what should be displayed, as well as when to display and how to display sensor data. PSST supports filtering based on raw or calculated values, highlighting, and transformation of data. Output formats include tonal sonification, nonspeech audio files, speech, and SVGs for laser cutting. We validate PSST through a series of demonstrations and a user study with BVI developers.

We present an ethnographic study of a maker community that conducted safety-driven medical making to deliver over 80,000 devices for use at medical facilities in response to the COVID-19 pandemic. To achieve this, the community had to balance their clinical value of safety with the maker value of broadened participation in design and production. We analyse their struggles and achievement through the artifacts they produced and the labors of key facilitators between diverse community members. Based on this analysis we provide insights into how medical maker communities, which are necessarily risk-averse and safety-oriented, can still support makers' grassroots efforts to care for their communities. Based on these findings, we recommend that design tools enable adaptation to a wider set of domains, rather than exclusively presenting information relevant to manufacturing. Further, we call for future work on the portability of designs across different types of printers which could enable broader participation in future maker efforts at this scale.

This paper presents a computational framework for modeling biobehavioral rhythms - the repeating cycles of physiological, psychological, social, and environmental events - from mobile and wearable data streams. The framework incorporates four main components: mobile data processing, rhythm discovery, rhythm modeling, and machine learning. We evaluate the framework with two case studies using datasets of smartphone, Fitbit, and OURA smart ring to evaluate the framework’s ability to (1) detect cyclic biobehavior, (2) model commonality and differences in rhythms of human participants in the sample datasets, and (3) predict their health and readiness status using models of biobehavioral rhythms. Our evaluation demonstrates the framework’s ability to generate new knowledge and findings through rigorous micro- and macro-level modeling of human rhythms from mobile and wearable data streams collected in the wild and using them to assess and predict different life and health outcomes.

We present an interactive design system for knitting that allows users to create template patterns that can be fabricated using an industrial knitting machine. Our interactive design tool is novel in that it allows direct control of key knitting design axes we have identified in our formative study and does so consistently across the variations of an input parametric template geometry. This is achieved with two key technical advances. First, we present an interactive meshing tool that lets users build a coarse quadrilateral mesh that adheres to their knit design guidelines. This solution ensures consistency across the parameter space for further customization over shape variations and avoids helices, promoting knittability. Second, we lift and formalize low-level machine knitting constraints to the level of this coarse quad mesh. This enables us to not only guarantee hand- and machine-knittability, but also provides automatic design assistance through auto-completion and suggestions. We show the capabilities through a set of fabricated examples that illustrate the effectiveness of our approach in creating a wide variety of objects and interactively exploring the space of design variations.

Knitting is a popular craft that can be used to create customized fabric objects such as household items, clothing and toys. Additionally, many knitters find knitting to be a relaxing and calming exercise. Little is known about how disabled knitters use and benefit from knitting, and what accessibility solutions and challenges they create and encounter. We conducted interviews with 16 experienced, disabled knitters and analyzed 20 threads from six forums that discussed accessible knitting to identify how and why disabled knitters knit, and what accessibility concerns remain. We additionally conducted an iterative design case study developing knitting tools for a knitter who found existing solutions insufficient. Our innovations improved the range of stitches she could produce. We conclude by arguing for the importance of improving tools for both pattern generation and modification as well as adaptations or modifications to existing tools such as looms to make it easier to track progress

Medical making intersects opposing value systems of a medical “do no harm” ethos and makers’ drive to innovate. Since March 2020, online maker communities have formed to design, manufacture, and distribute personal protective equipment (PPE) and other medical devices needed to fight the COVID-19 pandemic. We present a participant observation study of 14 maker communities, which have developed differing driving principles for efforts with varied access to interdisciplinary expertise on online platforms that mutually shape collective action. Over time, these communities unintentionally align towards action-oriented or regulated practices because they often lack higher level insight and agency in choosing communication platforms. In response, we recommend: regulatory bodies to build coalitions with makers, online platforms to give communities more control over the presentation of information, and repositories to balance needs to distribute information while limiting the spread of misinformation.

Unprecedented maker efforts arose in response to COVID-19 medical supply gaps worldwide. Makers in the U.S., participated in peer-production activities to manufacture personal protective equipment (PPE). Whereas, medical makers, who innovate exclusively for points of care, pivoted towards safer, reliable PPE. What were their efforts to pivot medical maker infrastructure towards reliable production of safe equipment at higher volumes? We interviewed 13 medical makers as links between institutions, maker communities, and wider regional industry networks. These medical makers organized stopgap manufacturing in institutional spaces to resolve acute shortages (March–May) and chronic shortages (May–July). They act as intermediaries in efforts to prototype and produce devices under regulatory, material, and human constraints of a pandemic. We re-frame their making efforts as repair work to offer an alternate critical view of optimism around making for crisis. We contribute an understanding of these efforts to inform infrastructure design for making with purpose and safety leading to opportunities for community production of safe devices at scale.

Visual semantics provide spatial information like size, shape, and position, which are necessary to understand and efficiently use interfaces and documents. Yet little is known about whether blind and low-vision (BLV) technology users want to interact with visual affordances, and, if so, for which task scenarios. In this work, through semi-structured and task-based interviews, we explore preferences, interest levels, and use of visual semantics among BLV technology users across two device platforms (smartphones and laptops), and information seeking and interactions common in apps and web browsing. Findings show that participants could benefit from access to visual semantics for collaboration, navigation, and design. To learn this information, our participants used trial and error, sighted assistance, and features in existing screen reading technology like touch exploration. Finally, we found that missing information and inconsistent screen reader representations of user interfaces hinder learning. We discuss potential applications and future work to equip BLV users with necessary information to engage with visual semantics.

We present HulaMove, a novel interaction technique that leverages the movement of the waist as a new eyes-free and hands-free input method for both the physical world and the virtual world. We first conducted a user study (N=12) to understand users’ ability to control their waist. We found that users could easily discriminate eight shifting directions and two rotating orientations, and quickly confirm actions by returning to the original position (quick return). We developed a design space with eight gestures for waist interaction based on the results and implemented an IMU-based real-time system. Using a hierarchical machine learning model, our system could recognize waist gestures at an accuracy of 97.5%. Finally, we conducted a second user study (N=12) for usability testing in both real-world scenarios and virtual reality settings. Our usability study indicated that HulaMove significantly reduced interaction time by 41.8% compared to a touch screen method, and greatly improved users’ sense of presence in the virtual world. This novel technique provides an additional input method when users’ eyes or hands are busy, accelerates users’ daily operations, and augments their immersive experience in the virtual world.

When chronic illness, such as Lyme disease, is viewed through a disability lens, equitable access to public spaces becomes an important area for consideration. Yet chronic illness is often viewed solely through an individualistic, medical model lens. We contribute to this field of study in four consecutive steps using Lyme disease as a case study: (1) we highlight urban design and planning literature to make the case for its relevance to chronic illness; (2) we explore the place-related impacts of living with chronic illness through an analysis of interviews with fourteen individuals living with Lyme disease; (3) we derive a set of design guidelines from our literature review and interviews that serve to support populations living with chronic illness; and (4) we present an interactive mapping prototype that applies our design guidelines to support individuals living with chronic illness in experiencing and navigating public and outdoor spaces.

We present a machine learning approach that uses data from smartphones and fitness trackers of 138 college students to identify students that experienced depressive symptoms at the end of the semester and students whose depressive symptoms worsened over the semester. Our novel approach is a feature extraction technique that allows us to select meaningful features indicative of depressive symptoms from longitudinal data. It allows us to detect the presence of post-semester depressive symptoms with an accuracy of 85.7% and change in symptom severity with an accuracy of 85.4%. It also predicts these outcomes with an accuracy of >80%, 11–15 weeks before the end of the semester, allowing ample time for pre-emptive interventions. Our work has significant implications for the detection of health outcomes using longitudinal behavioral data and limited ground truth. By detecting change and predicting symptoms several weeks before their onset, our work also has implications for preventing depression.

Accessibility research and disability studies are intertwined fields focused on, respectively, building a world more inclusive of people with disability and understanding and elevating the lived experiences of disabled people. Accessibility research tends to focus on creating technology related to impairment, while disability studies focuses on understanding disability and advocating against ableist systems. Our paper presents a reflexive analysis of the experiences of three accessibility researchers and one disability studies scholar. We focus on moments when our disability was misunderstood and causes such as expecting clearly defined impairments. We derive three themes: ableism in research, oversimplification of disability, and human relationships around disability. From these themes, we suggest paths toward more strongly integrating disability studies perspectives and disabled people into accessibility research.

Past research regarding on-body interaction typically requires custom sensors, limiting their scalability and generalizability. We propose EarBuddy, a real-time system that leverages the microphone in commercial wireless earbuds to detect tapping and sliding gestures near the face and ears. We develop a design space to generate 27 valid gestures and conducted a user study (N=16) to select the eight gestures that were optimal for both human preference and microphone detectability. We collected a dataset on those eight gestures (N=20) and trained deep learning models for gesture detection and classification. Our optimized classifier achieved an accuracy of 95.3%. Finally, we conducted a user study (N=12) to evaluate EarBuddy's usability. Our results show that EarBuddy can facilitate novel interaction and that users feel very positively about the system. EarBuddy provides a new eyes-free, socially acceptable input method that is compatible with commercial wireless earbuds and has the potential for scalability and generalizability

Automatic knitting machines are robust, digital fabrication devices that enable rapid and reliable production of attractive, functional objects by combining stitches to produce unique physical properties. However, no existing design tools support optimization for desirable physical and aesthetic knitted properties. We present KnitGIST (Generative Instantiation Synthesis Toolkit for knitting), a program synthesis pipeline and library for generating hand- and machine-knitting patterns by intuitively mapping objectives to tactics for texture design. KnitGIST generates a machine-knittable program in a domain-specific programming language.

In this work, we apply an activity theory lens to analyze nonvisual computing for blind and low-vision computer users. Our analysis indicates major challenges for users in translating the activities they are working towards into specific tasks to be completed in a system comprehensible manner. Specifically, blind and low-vision students learning to use accessible technologies struggled with organizing their activities, tracking the history and status of their operations, and understanding how the system was acting underneath these interactions. We discuss how activity-centered design can be applied to nonvisual interfaces to better match user behavior in a computational system.

Blind and visually impaired (BVI) individuals are increasingly creating visual content online; however, there is a lack of tools that allow these individuals to modify the visual attributes of the content and verify the validity of those modifications. In this poster paper, we discuss the design and preliminary exploration of a multi-modal and accessible approach for BVI developers to edit visual layouts of webpages while maintaining visual aesthetics.

Consumer-fabrication technologies potentially improve the effectiveness and adoption of assistive technology (AT) by engaging AT users in AT creation. However, little is known about the role of clinicians in this revolution. We investigate clinical AT fabrication by working as expert fabricators for clinicians over a four-month period. We observed and co-designed AT with four occupational therapists at two clinics: a free clinic for uninsured clients, and a Veteran's Affairs Hospital. We find that existing fabrication processes, particularly with respect to rapid prototyping, do not align with clinical practice and itsdo-no-harm ethos. We recommend software solutions that would integrate into client care by: amplifying clinicians' expertise, revealing appropriate fabrication opportunities, and supporting adaptable fabrication.

This paper draws on a collaborative project called the Africatown Activation to examine the role design practices play in contributing to (or conspiring against) the flourishing of the Black community in Seattle, Washington. Specifically, we describe the efforts of a community group called Africatown to design and build an installation that counters decades of disinvestment and ongoing displacement in the historically Black Central Area neighborhood. Our analysis suggests that despite efforts to include community, conventional design practices may perpetuate forms of institutional racism: enabling activities of community engagement that may further legitimate racialized forms of displacement. We discuss how focusing on amplifying the legacies of imagination already at work may help us move beyond a simple reading of design as the solution to systemic forms of oppression.

People eat every day and biting is one of the most fundamental and natural actions that they perform on a daily basis. Existing work has explored tooth click location and jaw movement as input techniques, however clenching has the potential to add control to this input channel. We propose clench interaction that leverages clenching as an actively controlled physiological signal that can facilitate interactions. We conducted a user study to investigate users' ability to control their clench force. We found that users can easily discriminate three force levels, and that they can quickly confirm actions by unclenching (quick release). We developed a design space for clench interaction based on the results and investigated the usability of the clench interface. Participants preferred the clench over baselines and indicated a willingness to use clench-based interactions. This novel technique can provide an additional input method in cases where users' eyes or hands are busy, augment immersive experiences such as virtual/augmented reality, and assist individuals with disabilities.

Improving mobile keyboard typing speed increases in value as more tasks move to a mobile setting. Autocorrect reduces the time it takes to manually fix typing errors, which results in typing speed increase. However, recent user studies uncovered an unexplored side-effect: participants' aversion to typing errors despite autocorrect. We present a computational model of typing on keyboards with autocorrect, which enables precise study of expert typists' aversion to typing errors on such keyboards. Unlike empirical typing studies that last days, our model evaluates this phenomenon for any autocorrect accuracy in seconds. We show that typists' aversion to typing errors imposes a limit on upper bound typing speeds, even for highly accurate autocorrect. Our findings motivate future keyboard designs that reduce typists' aversion to typing errors to increase typing speeds.

Knitting creates complex, soft fabrics with unique texture properties that can be used to create interactive objects.However, little work addresses the challenges of designing and using knitted textures computationally. We present KnitPick: a pipeline for interpreting hand-knitting texture patterns into KnitGraphs which can be output to machine and hand-knitting instructions. Using KnitPick, we contribute a measured and photographed data set of 472 knitted textures. Based on findings from this data set, we contribute two algorithms for manipulating KnitGraphs. KnitCarving shapes a graph while respecting a texture, and KnitPatching combines graphs with disparate textures while maintaining a consistent shape. KnitPick is the first system to bridge the gap between hand- and machine-knitting when creating complex knitted textures.

From the white cane to the smartphone, technology has been an effective tool for broadening blind and low vision participation in a sighted world. In the face of this increased participation, individuals with visual impairments remain on the periphery of most sight-first activities. In this paper, we describe a multi-month public-facing co-design engagement with an organization that supports blind and low vision outrigger paddling. Using a mixed-ability design team, we developed an inexpensive cooperative outrigger paddling system, called CoOP, that shares control between sighted and visually impaired paddlers. The results suggest that public design, a DIY (do-it-yourself) stance, and attentiveness to shared physical experiences, represent key strategies for creating assistive technologies that support shared experiences.

Maker culture is on the rise in healthcare with the adoption of consumer-grade fabrication technologies. However, little is known about the activities and resources involved in prototyping medical devices to improve patient care. In this paper, we refer to such activity asmedical making to report findings based on a qualitative study of stakeholder engagement in physical prototyping (making) experiences. We examine perspectives from diverse stakeholders including clinicians, engineers, administrators, and medical researchers. Through 18 semi-structured interviews with medical-makers in the US and Canada, we analyze making activity in medical settings. We find that medical makers share strategies to address risks, adopt labor roles, and acquire resources within traditional medical practice. Our findings outline how medical-makers mitigate risks for patient safety, collaborate with local and global stakeholder networks, and overcome constraints of co-location and material practices. We recommend a clinician-aided software system, partially-open repositories, and a collaborative skill-sharing social network to extend their strategies in support of medical making.

A deep understanding of how discrimination impacts psychological health and well-being of students could allow us to better protect individuals at risk and support those who encounter discrimination. While the link between discrimination and diminished psychological and physical well-being is well established, existing research largely focuses on chronic discrimination and long-term outcomes. A better understanding of the short-term behavioral correlates of discrimination events could help us to concretely quantify such experiences, which in turn could support policy and intervention design. In this paper we specifically examine, for the first time, what behaviors change and in what ways in relation to discrimination. We use actively-reported and passively-measured markers of health and well-being in a sample of 209 first-year college students over the course of two academic quarters. We examine changes in indicators of psychological state in relation to reports of unfair treatment in terms of five categories of behaviors: physical activity, phone usage, social interaction, mobility, and sleep. We find that students who encounter unfair treatment become more physically active, interact more with their phone in the morning, make more calls in the evening, and spend more time in bed on the day of the event. Some of these patterns continue the next day. Our results further our understanding of the impact of discrimination and can inform intervention work.

The absence of tactile cues such as keys and buttons makes touchscreens difficult to navigate for people with visual impairments. Increasing tactile feedback and tangible interaction on touchscreens can improve their accessibility. However, prior solutions have either required hardware customization or provided limited functionality with static overlays. Prior investigation of tactile solutions for large touchscreens also may not address the challenges on mobile devices. We therefore present Interactiles, a low cost, portable, and unpowered system that enhances tactile interaction on Android touchscreen phones. Interactiles consists of 3D-printed hardware interfaces and software that maps interaction with that hardware to manipulation of a mobile app. The system is compatible with the built-in screen reader without requiring modification of existing mobile apps. We describe the design and implementation of Interactiles, and we evaluate its improvement in task performance and the user experience it enables with people who are blind or have low vision.

With the increasing popularity of consumer-grade 3D printing, many people are creating, and even more using, objects shared on sites such as Thingiverse. However, our formative study of 962 Thingiverse models shows a lack of re-use of models, perhaps due to the advanced skills needed for 3D modeling. An end user program perspective on 3D modeling is needed. Our framework (PARTs) empowers amateur modelers to graphically specify design intent through geometry. PARTs includes a GUI, scripting API and exemplar library of assertions which test design expectations and integrators which act on intent to create geometry. PARTs lets modelers integrate advanced, model specific functionality into designs, so that they can be re-used and extended, without programming. In two workshops, we show that PARTs helps to create 3D printable models, and modify existing models more easily than with a standard tool.

Web user interfaces today leverage many common GUI design patterns, including navigation bars and menus (hierarchical structure), tabular content presentation, and scrolling. These visual-spatial cues enhance the interaction experience of sighted users. However, the linear nature of screen translation tools currently available to blind users make it difficult to understand or navigate these structures. We introduce Spatial Region Interaction Techniques (SPRITEs) for nonvisual access: a novel method for navigating two-dimensional structures using the keyboard surface. SPRITEs 1) preserve spatial layout, 2) enable bimanual interaction, and 3) improve the end user experience. We used a series of design probes to explore different methods for keyboard surface interaction. Our evaluation of SPRITEs shows that three times as many participants were able to complete spatial tasks with SPRITEs than with their preferred current technology.

Housing quality can impact quality of life factors such as physical health and finances. However, prospective tenants lack critical information about things like utility costs and landlord quality. Our contribution is a series of three studies exploring the complexities of the information economy around rental housing, including information available to landlords but not tenants, as well as information tenants may not prioritize until after lease signing about their own desires. Our first mixed methods study demonstrates that tenants cannot easily predict factors impacting the quality of life when selecting a rental and need better information about rental housing and the overall rental system. Using speed dating, we explore scenarios premised on information sharing and quality of life to analyze risks and rewards of crowdsourced information collection and sharing within a multi-stakeholder peer-to-peer rental search system. Finally, we present eDigs, a rental search system we have deployed to enhance the information economy for prospective tenants using a combination of opinion, implicit observation, and communal information. We also explore user responses to the system.

We present the first wireless physical analytics system for 3D printed objects using commonly available conductive plastic filaments. Our design can enable various data capture and wireless physical analytics capabilities for 3D printed objects, without the need for electronics. To achieve this goal, we make three key contributions: (1) demonstrate room scale backscatter communication and sensing using conductive plastic filaments, (2) introduce the first backscatter designs that detect a variety of bi-directional motions and support linear and rotational movements, and (3) enable data capture and storage for later retrieval when outside the range of the wireless coverage, using a ratchet and gear system. We validate our approach by wirelessly detecting the opening and closing of a pill bottle, capturing the joint angles of a 3D printed e-NABLE prosthetic hand, and an insulin pen that can store information to track its use outside the range of a wireless receiver.

Women remain underrepresented in many fields in computer science, particularly at higher levels. In academia, success and promotion are influenced by a researcher's publication record. In many fields, including computer science, multi-author papers are the norm. Evidence from other fields shows that author order norms can influence the assignment of credit. We conduct interviews of students and faculty in human-computer interaction (HCI) and machine learning (ML) to determine factors related to assignment of author order in collaborative publication. The outcomes of these interviews then informed metrics of interest for a bibliometric analysis of gender and collaboration in research papers published from 1996 to 2016 in three top HCI and ML conferences. Based on our findings, we make recommendations for assignment of credit in multi-author papers and interpretation of author order, particularly in regard to how this area affects women.

The growing accessibility of 3D printing to everyday users has led to the rapid adoption, sharing of 3D models on sites such as Thingiverse.com, and visions of a future in which customization is a norm and 3D printing can solve a variety of real-world problems. However, in practice, creating models is difficult and many end users simply print models created by others. In this paper, we explore a specific area of model design that is a challenge for end users' measurement. When a model must conform to a specific real world goal once printed, it is important that that goal is precisely specified. We demonstrate that measurement errors are a significant (yet often overlooked) challenge for end users through a systematic study of the sources and types of measurement errors. We argue for a new design principle--accommodating measurement error--that designers, as well as novice modelers, should to use at design time. We offer two strategies--buffer insertion and replacement of minimal parts--to help designers, as well as novice modelers, to build models that are robust to measurement error. We argue that these strategies can reduce the need for and costs of iteration and demonstrate their use in a series of printed objects.

Text entry is an increasingly important activity for mobile device users. As a result, increasing text entry speed of expert typists is an important design goal for physical and soft keyboards. Mathematical models that predict text entry speed can help with keyboard design and optimization. Making typing errors when entering text is inevitable. However, current models do not consider how typists themselves reduce the risk of making typing errors (and lower error frequency) by typing more slowly. We demonstrate that users respond to costly typing errors by reducing their typing speed to minimize typing errors. We present a model that estimates the effects of risk aversion to errors on typing speed. We estimate the magnitude of this speed change, and show that disregarding the adjustments to typing speed that expert typists use to reduce typing errors leads to overly optimistic estimates of maximum errorless expert typing speeds.

An ability to detect behaviors that negatively impact people's wellbeing and show people how they can correct those behaviors could enable technology that improves people's lives. Existing supervised machine learning approaches to detect and generate such behaviors require lengthy and expensive data labeling by domain experts. In this work, we focus on the domain of routine behaviors, where we model routines as a series of frequent actions that people perform in specific situations. We present an approach that bypasses labeling each behavior instance that a person exhibits. Instead, we weakly label instances using people's demonstrated routine. We classify and generate new instances based on the probability that they belong to the routine model. We illustrate our approach on an example system that helps drivers become aware of and understand their aggressive driving behaviors. Our work enables technology that can trigger interventions and help people reflect on their behaviors when those behaviors are likely to negatively impact them.

Common appliances have shifted toward flat interface panels, making them inaccessible to blind people. Although blind people can label appliances with Braille stickers, doing so generally requires sighted assistance to identify the original functions and apply the labels. We introduce Facade - a crowdsourced fabrication pipeline to help blind people independently make physical interfaces accessible by adding a 3D printed augmentation of tactile buttons overlaying the original panel. Facade users capture a photo of the appliance with a readily available fiducial marker (a dollar bill) for recovering size information. This image is sent to multiple crowd workers, who work in parallel to quickly label and describe elements of the interface. Facade then generates a 3D model for a layer of tactile and pressable buttons that fits over the original controls. Finally, a home 3D printer or commercial service fabricates the layer, which is then aligned and attached to the interface by the blind person. We demonstrate the viability of Facade in a study with 11 blind participants.

We present the results of a study of e-NABLE, a distributed, collaborative volunteer effort to design and fabricate upper-limb assistive technology devices for limb-different users. Informed by interviews with 14 stakeholders in e-NABLE, including volunteers and clinicians, we discuss differences and synergies among each group with respect to motivations, skills, and perceptions of risks inherent in the project. We found that both groups are motivated to be involved in e-NABLE by the ability to use their skills to help others, and that their skill sets are complementary, but that their different perceptions of risk may result in uneven outcomes or missed expectations for end users. We offer four opportunities for design and technology to enhance the stakeholders' abilities to work together.

Textiles are an old and well developed technology that have many desirable characteristics. They can be easily folded, twisted, deformed, or cut; some can be stretched; many are soft. Textiles can maintain their shape when placed under tension and can even be engineered with variable stretching ability. Conversely, 3D printing is a relatively new technology that can precisely produce functional, rigid objects with custom geometry. Combining 3D printing and textiles opens up new opportunities for rapidly creating rigid objects with embedded flexibility as well as soft materials imbued with additional functionality. In this paper, we introduce a suite of techniques for integrating 3D printing with textiles during the printing process, opening up a new design space that takes inspiration from both fields. We demonstrate how the malleability, stretchability and aesthetic qualities of textiles can enhance rigid printed objects, and how textiles can be augmented with functional properties enabled by 3D printing.

Digital keypads have proliferated on common appliances, from microwaves and refrigerators to printers and remote controls. For blind people, such interfaces are inaccessible. We conducted a formative study with 6 blind people which demonstrated a need for custom designs for tactile labels without dependence on sighted assistance. To address this need, we introduce Facade - a crowdsourced fabrication pipeline to make physical interfaces accessible by adding a 3D printed augmentation of tactile buttons overlaying the original panel. Blind users capture a photo of an inaccessible interface with a standard marker for absolute measurements using perspective transformation. Then this image is sent to multiple crowd workers, who work in parallel to quickly label and describe elements of the interface. These labels are then used to generate 3D models for a layer of tactile and pressable buttons that fits over the original controls. Users can customize the shape and labels of the buttons using a web interface. Finally, a consumer-grade 3D printer fabricates the layer, which is then attached to the interface using adhesives. Such fabricated overlay is an inexpensive ($10) and more general solution to making physical interfaces accessible.

In this experience report, we describe the experiences of volunteer assistive device designers, clinicians, and human computer interaction and fabrication researchers who met at a summit on Do-It-Yourself Assistive Technology. From the perspectives of these stakeholders, we elucidate significant challenges of introducing rapid prototyping to the design of professional assistive technology, and opportunities for advancing assistive technology. We describe these challenges and opportunities in the context of an emerging gap between clinical and volunteer assistive device design. Whereas clinical process is fully led by the question, "will this do harm", while volunteers chaotically pursue the lofty goal of providing assistive technology to all. While all stakeholders hold the same core goals, there are many practical limitations to collaboration and development.

Human routines are blueprints of behavior, which allow people to accomplish purposeful repetitive tasks at many levels, ranging from the structure of their day to how they drive through an intersection. People express their routines through actions that they perform in the particular situations that triggered those actions. An ability to model routines and understand the situations in which they are likely to occur could allow technology to help people improve their bad habits, inexpert behavior, and other suboptimal routines. However, existing routine models do not capture the causal relationships between situations and actions that describe routines. Our main contribution is the insight that byproducts of an existing activity prediction algorithm can be used to model those causal relationships in routines. We apply this algorithm on two example datasets, and show that the modeled routines are meaningful-that they are predictive of people's actions and that the modeled causal relationships provide insights about the routines that match findings from previous research. Our approach offers a generalizable solution to model and reason about routines.

This paper presents a case study of three participants with upper-limb amputations working with researchers to design prosthetic devices for specific tasks: playing the cello, operating a hand-cycle, and using a table knife. Our goal was to identify requirements for a design process that can engage the assistive technology user in rapidly prototyping assistive devices that fill needs not easily met by traditional assistive technology. Our study made use of 3D printing and other playful and practical prototyping materials. We discuss materials that support on-the-spot design and iteration, dimensions along which in-person iteration is most important (such as length and angle) and the value of a supportive social network for users who prototype their own assistive technology. From these findings we argue for the importance of extensions in supporting modularity, community engagement, and relatable prototyping materials in the iterative design of prosthetics.

RFID tags can be used to add inexpensive, wireless, batteryless sensing to objects. However, quickly and accurately estimating the state of an RFID tag is difficult. In this work, we show how to achieve low-latency manipulation and movement sensing with off-the-shelf RFID tags and readers. Our approach couples a probabilistic filtering layer with a monte-carlo-sampling-based interaction layer, preserving uncertainty in tag reads until they can be resolved in the context of interactions. This allows designers' code to reason about inputs at a high level. We demonstrate the effectiveness of our approach with a number of interactive objects, along with a library of components that can be combined to make new designs.

Predictive algorithms are a critical part of the ubiquitous computing vision, enabling appropriate action on behalf of users. A common class of algorithms, which has seen uptake in ubiquitous computing, is supervised machine learning algorithms. Such algorithms are trained to make predictions based on a set of features (selected at training time). However, features needed at prediction time (such as mobile information that impacts battery life, or information collected from users via experience sampling) may be costly to collect. In addition, both cost and value of a feature may change dynamically based on real-world context (such as battery life or user location) and prediction context (what features are already known, and what their values are). We contribute a framework for dynamically trading off feature cost against prediction quality at prediction time. We demonstrate this work in the context of three prediction tasks: providing prospective tenants estimates for energy costs in potential homes, estimating momentary stress levels from both sensed and user-provided mobile data, and classifying images to facilitate opportunistic device interactions. Our results show that while our approach to cost-sensitive feature selection is up to 45% less costly than competing approaches, error rates are equivalent or better.

Everyday tools and objects often need to be customized for an unplanned use or adapted for specific user, such as adding a bigger pull to a zipper or a larger grip for a pen. The advent of low-cost 3D printing offers the possibility to rapidly construct a wide range of such adaptations. However, while 3D printers are now affordable enough for even home use, the tools needed to design custom adaptations normally require skills that are beyond users with limited 3D modeling experience.

Industrial knitting machines can produce finely detailed, seamless, 3D surfaces quickly and without human intervention. However, the tools used to program them require detailed manipulation and understanding of low-level knitting operations. We present a compiler that can automatically turn assemblies of high-level shape primitives (tubes, sheets) into low-level machine instructions. These high-level shape primitives allow knit objects to be scheduled, scaled, and otherwise shaped in ways that require thousands of edits to low-level instructions. At the core of our compiler is a heuristic transfer planning algorithm for knit cycles, which we prove is both sound and complete. This algorithm enables the translation of high-level shaping and scheduling operations into needle-level operations. We show a wide range of examples produced with our compiler and demonstrate a basic visual design interface that uses our compiler as a backend.

Volunteer campaigns for data collection make it possible for non-profit organizations to extend their ability to monitor and respond to critical environmental and societal issues. Yet mobile data collection technologies that have the potential to lower the costs and increase the accuracy of volunteer-collected data are not commonly used in these campaigns. In this paper we conduct a series of studies that reveal the complex issues affecting technology adoption in this domain. First, we surveyed and interviewed existing volunteering campaigns to map out current technology usage within volunteer campaigns. Next, we provided two organizations with a customizable tool for data collection (Sensr) and studied its use and non-use across six real volunteer-driven campaigns over six months. Our study explored success and failure across the first few phases of the campaign lifecycle (campaign creation, initial deployment, and adoption). Our results highlight the impact of resource constraints, cognitive factors, the depth of volunteer engagement, and stakeholders' perspective on technology as important factors contributing to the adoption and usage of mobile data collection technologies. We use these findings to argue for specific design features to accelerate the adoption and use of such tools in volunteer data collection campaigns.

We present a new type of 3D printer that can form precise, but soft and deformable 3D objects from layers of off-the-shelf fabric. Our printer employs an approach where a sheet of fabric forms each layer of a 3D object. The printer cuts this sheet along the 2D contour of the layer using a laser cutter and then bonds it to previously printed layers using a heat sensitive adhesive. Surrounding fabric in each layer is temporarily retained to provide a removable support structure for layers printed above it. This process is repeated to build up a 3D object layer by layer. Our printer is capable of automatically feeding two separate fabric types into a single print. This allows specially cut layers of conductive fabric to be embedded in our soft prints. Using this capability we demonstrate 3D models with touch sensing capability built into a soft print in one complete printing process, and a simple LED display making use of a conductive fabric coil for wireless power reception.

Increasingly natural, sensed, and touch-based input is being integrated into devices. Along the way, both custom and more general solutions have been developed for dealing with the uncertainty that is associated with these forms of input. However, it is difficult to provide dynamic, flexible, and continuous feedback about uncertainty using traditional interactive infrastructure. Our contribution is a general architecture with the goal of providing support for continual feedback about uncertainty. Our architecture is based on prior work in modeling uncertainty using Monte Carlo sampling, and tracks multiple interfaces -- one for each plausible and differentiable sequence of input that the user may have intended. Importantly, it considers how the presentation of uncertainty can be organized and implemented in a general way. Our primary contribution is a method for reducing the number of alternative interfaces and fusing possible interfaces into a single interface that both communicates uncertainty and allows for disambiguation. We demonstrate the value of this result through a collection of 11 new and existing feedback techniques along with two applications demonstrating the use of the feedback architecture.

One powerful aspect of 3D printing is its ability to extend, repair, or more generally modify everyday objects. However, nearly all existing work implicitly assumes that whole objects are to be printed from scratch. Designing objects as extensions or enhancements of existing ones is a laborious process in most of today's 3D authoring tools. This paper presents a framework for 3D printing to augment existing objects that covers a wide range of attachment options. We illustrate the framework through three exemplar attachment techniques -- print-over, print-to-affix and print-through, implemented in Encore, a design tool that supports a set of analysis metrics relating to viability, durability and usability that are visualized for the user to explore design options and tradeoffs. Encore also generates 3D models for production, addressing issues such as support jigs and contact geometry between the attached part and the original object. Our validation helps to illustrate the strengths and weaknesses of each technique. For example, print-over is stronger than print-to-affix with adhesives, and all the techniques' strengths are affected by surface curvature.

Physical therapists could make better treatment decisions if they had accurate patient home exercise data but today this information is only available from patient self-report. A more accurate source of data could be gained from wearable computing designed for physical therapy exercise support. Existing systems have been tested in the lab but we have little information about issues they may face in home settings. We designed a technology probe, SenseCap, and deployed it for seven days in ten physical therapy patients' homes. SenseCap is a wearable physical therapy support system that gathers patient exercise compliance and performance data and summarizes the data in charts on an iPad Dashboard for physical therapists to view when patients return to the clinic. In this paper, we present the results of our deployment, show in-home patient exercise data gathered by the probe, and make design recommendations based on patient and physical therapist responses.

Vision-based interfaces, such as those made popular by the Microsoft Kinect, suffer from the Midas Touch problem: every user motion can be interpreted as an interaction. In response, we developed an algorithm that combines facial features, body pose and motion to approximate a user's intention to interact with the system. We show how this can be used to determine when to pay attention to a user's actions and when to ignore them. To demonstrate the value of our approach, we present results from a 30-person lab study conducted to compare four engagement algorithms in single and multi-user scenarios. We found that combining intention to interact with a 'raise an open hand in front of you' gesture yielded the best results. The latter approach offers a 12% improvement in accuracy and a 20% reduction in time to engage over a baseline 'wave to engage' gesture currently used on the Xbox 360.

Tablet computers are often called upon to emulate classical pen-and-paper input. However, touchscreens typically lack the means to distinguish between legitimate stylus and finger touches and touches with the palm or other parts of the hand. This forces users to rest their palms elsewhere or hover above the screen, resulting in ergonomic and usability problems. We present a probabilistic touch filtering approach that uses the temporal evolution of touch contacts to reject palms. Our system improves upon previous approaches, reducing accidental palm inputs to 0.016 per pen stroke, while correctly passing 98% of stylus inputs.

An increasing number of researchers are using social engagement techniques such as neighborhood comparison and competition to encourage energy conservation, yet community reception and experience with such systems have not been well studied. We also find that researchers have not thoroughly investigated how different households use these systems and how their uses differ from one another. We explore these questions in a 4-10 month field deployment of a social-energy monitoring application across 15 households, in two distinct locations. We contribute results that describe conditions under which these techniques were effective and ineffective. Our results imply that understanding factors such as a building, or community's layout, context knowledge of community members, accountability and adherence to social norms, trust, and length of residence are key for future design of social-energy applications.

Location prediction enables us to use a person's mobility history to realize various applications such as efficient temperature control, opportunistic meeting support, and automated receptionists. Indoor location prediction is a challenging problem, particularly due to a high density of possible locations and short transition distances between these locations. In this paper we present Indoor-ALPS, an Adaptive Indoor Location Prediction System that uses temporal-spatial features to create individual daily models for the prediction of when a user will leave their current location (transition time) and the next location she will transition to. We tested Indoor-ALPS on the Augsburg Indoor Location Tracking Benchmark and compared our approach to the best performing temporal-spatial mobility prediction algorithm, Prediction by Partial Match (PPM). Our results show that Indoor-ALPS improves the temporal-spatial prediction accuracy over PPM for look-aheads up to 90 minutes by 6.2%, and for up to 30 minute look-aheads by 10.7%. These results demonstrate that Indoor-ALPS can be used to support a wide variety of indoor mobility prediction-based applications.

Mobile devices have become powerful ultra-portable personal computers supporting not only communication but also running a variety of complex, interactive applications. Because of the unique characteristics of mobile interaction, a better understanding of the time duration and context of mobile device uses could help to improve and streamline the user experience. In this paper, we first explore the anatomy of mobile device use and propose a classification of use based on duration and interaction type: glance, review, and engage. We then focus our investigation on short review interactions and identify opportunities for streamlining these mobile device uses through proactively suggesting short tasks to the user that go beyond simple application notifications. We evaluate the concept through a user evaluation of an interactive lock screen prototype, called ProactiveTasks. We use the findings from our study to create and explore the design space for proactively presenting tasks to the users. Our findings underline the need for a more nuanced set of interactions that support short mobile device uses, in particular review sessions.

The space around the body provides a large interaction volume that can allow for big interactions on small mobile devices. However, interaction techniques making use of this opportunity are underexplored, primarily focusing on distributing information in the space around the body. We demonstrate three types of around-body interaction including canvas, modal and context-aware interactions in six demonstration applications. We also present a sensing solution using standard smartphone hardware: a phone's front camera, accelerometer and inertia measurement units. Our solution allows a person to interact with a mobile device by holding and positioning it between a normal field of view and its vicinity around the body. By leveraging a user's proprioceptive sense, around-body Interaction opens a new input channel that enhances conventional interaction on a mobile device without requiring additional hardware.

We present Air+Touch, a new class of interactions that interweave touch events with in-air gestures, offering a unified input modality with expressiveness greater than each input modality alone. We demonstrate how air and touch are highly complementary: touch is used to designate targets and segment in-air gestures, while in-air gestures add expressivity to touch events. For example, a user can draw a circle in the air and tap to trigger a context menu, do a finger 'high jump' between two touches to select a region of text, or drag and in-air 'pigtail' to copy text to the clipboard. Through an observational study, we devised a basic taxonomy of Air+Touch interactions, based on whether the in-air component occurs before, between or after touches. To illustrate the potential of our approach, we built four applications that showcase seven exemplar Air+Touch interactions we created.

Sighted individuals often develop significant knowledge about their environment through what they can visually observe. In contrast, individuals who are visually impaired mostly acquire such knowledge about their environment through information that is explicitly related to them. This paper examines the practices that visually impaired individuals use to learn about their environments and the associated challenges. In the first of our two studies, we uncover four types of information needed to master and navigate the environment. We detail how individuals' context impacts their ability to learn this information, and outline requirements for independent spatial learning. In a second study, we explore how individuals learn about places and activities in their environment. Our findings show that users not only learn information to satisfy their immediate needs, but also to enable future opportunities -- something existing technologies do not fully support. From these findings, we discuss future research and design opportunities to assist the visually impaired in independent spatial learning.

Indoor air quality (IAQ) is important for health as people spend the majority of time indoors, and it is particularly interesting over outdoor air because it strongly ties to indoor activities. Some activities easily exacerbate IAQ, resulting in serious pollution. However, people may not notice such changes because many pollutants are colorless and odorless, while many activities are inconspicuous and routine. We implemented inAir, a system that measures and visualizes IAQ that households appropriate and integrate into everyday life. The research goals of this work include understanding the IAQ dynamics with respect to habitual behaviors and analyzing behavioral and quantitative changes towards improving IAQ by the use of inAir. From our longitudinal study for four months, we found that inAir successfully elicited the reflection upon, and the modification of habitual behaviors for healthy domestic environments, which resulted in the significant improvement of IAQ.

Like most online content, user-generated content (UGC) poses accessibility barriers to users with disabilities. However, the accessibility difficulties pervasive in UGC warrant discussion and analysis distinct from other kinds of online content. Content authors, community culture, and the authoring tool itself all affect UGC accessibility. The choices, resources available, and strategies in use to ensure accessibility are different than for other types of online content. We contribute case studies of two UGC communities with accessible content: Wikipedia, where authors focus on access to visual materials and navigation, and an online health support forum where users moderate the cognitive accessibility of posts. Our data demonstrate real world moderation strategies and illuminate factors affecting success, such as community culture. We conclude with recommended strategies for creating a culture of accessibility around UGC.

Doing research is, in part, an act of foresight. Even though it is not explicit in many projects, we especially value research that is still relevant five, ten or more years after it is completed. However, published research in the field of interactive computing (and technology research in general) often lacks evidence of systematic thinking about the long-term impacts of current trends. For example, trends on an exponential curve change much more rapidly than intuition predicts. As a result, research may accidentally emphasize near-term thinking. When thinking about the future is approached systematically, we can critically examine multiple potential futures, expand the set of externalities under consideration, and address both negative and positive forecasts of the future. The field of Futures Studies provides methods that can support analysis of long-term trends, support the identification of new research areas and guide design and evaluation. We survey methods for futuristic thinking and discuss their relationship to Human Computer Interaction. Using the sustainability domain an example, we present a case study of a Futures Studies approach - the Delphi Method. We show how Futures Studies can be incorporated into Human Computer Interaction and highlight future work such as rethinking the role of externalities in the validation process.

Rapid depletion of fossil fuels and water resources has become an international problem. Urban residential households are among the primary consumers of resources and are deeply affected by resource shortages. Despite the global nature of these problems, most of the solutions being developed to address these issues are based on studies done in the developed world. We present a study of energy, water and fuel conservation practices in urban India. Our study highlights a culture of deep conservation and the results raise questions about the viability of typical solutions such as home energy monitors. We identify new opportunities for design such as point-of-use feedback technologies, modular solutions, distributed energy storage, harnessing by-products and automated load shifting.

Across HCI and social computing platforms, mobile applications that support citizen science, empowering non-experts to explore, collect, and share data have emerged. While many of these efforts have been successful, it remains difficult to create citizen science applications without extensive programming expertise. To address this concern, we present Sensr, an authoring environment that enables people without programming skills to build mobile data collection and management tools for citizen science. We demonstrate how Sensr allows people without technical skills to create mobile applications. Findings from our case study demonstrate that our system successfully overcomes technical constraints and provides a simple way to create mobile data collection tools.

Reducing the large energy consumption of temperature regulation systems is a challenge for researchers and practitioners alike. In this paper, we explore and compare two common types of solutions: A manual systems that encourages reduced energy use, and an intelligent automatic control system. We deployed an eco-feedback system with the ability to remotely control one's thermostat to ten participants for three months. Participants appreciated the ability to remotely control the thermostat, and controlled their heating system with 78.8% accuracy, a 6.3% improvement over not having this system. However, despite having feedback and remote control, they still wasted a lot of energy heating when away from home for the day. Using data from our deployment, we developed TherML, an occupancy prediction algorithm that uses GPS data from a user's smartphone to automatically control the indoor temperature of a home with 92.1% accuracy. We compare TherML to other state-of-the-art techniques, and show that the higher accuracy of our approach optimizes both energy usage and user comfort. We end with recommendations for a mixed initiative system that leverages aspects of both the manual and automated approaches that can better match heating control to users' routines and preferences.

Among the billions of photos that have been contributed to online photo-sharing sites, there are many that are provocative, controversial, and deeply personal. Previous research has examined motivations for sharing images online and has identified several key motivations for doing so: expression, curation of identity, maintaining social connections, and recording experiences. However, few studies have focused on the perceived risks of posting photos online and even fewer have examined the risks associated with provocative, controversial, or deeply personal images. In our work, we used photo-elicitation interviews to explore the motivations for posting these types of images and the perceived risks of doing so. In this paper, we describe our findings from those interviews.

People with chronic health problems use online resources to understand and manage their condition, but many such resources can present competing and confusing viewpoints. We surveyed and interviewed with people experiencing prolonged symptoms after a Lyme disease diagnosis. We explore how competing viewpoints in online content affect participants' understanding of their disease. Our results illustrate how chronically ill people search for information and support, and work to help others over time. Participant identity and beliefs about their illness evolved, and this led many to take on new roles, creating content and advising others who were sick. What we learned about online content creation suggests a need for designs that support this journey and engage with complex issues surrounding online health resources.

Current input handling systems provide effective techniques for modeling, tracking, interpreting, and acting on user input. However, new interaction technologies violate the standard assumption that input is certain. Touch, speech recognition, gestural input, and sensors for context often produce uncertain estimates of user inputs. Current systems tend to remove uncertainty early on. However, information available in the user interface and application can help to resolve uncertainty more appropriately for the end user. This paper presents a set of techniques for tracking the state of interactive objects in the presence of uncertain inputs. These techniques use a Monte Carlo approach to maintain a probabilistically accurate description of the user interface that can be used to make informed choices about actions. Samples are used to approximate the distribution of possible inputs, possible interactor states that result from inputs, and possible actions (callbacks and feedback) interactors may execute. Because each sample is certain, the developer can specify most of the behavior of interactors in a familiar, non-probabilistic fashion. This approach retains all the advantages of maintaining information about uncertainty while minimizing the need for the developer to work in probabilistic terms. We present a working implementation of our framework and illustrate the power of these techniques within a paint program that includes three different kinds of uncertain input.

Disability studies and assistive technology are two related fields that have long shared common goals - understanding the experience of disability and identifying and addressing relevant issues. Despite these common goals, there are some important differences in what professionals in these fields consider problems, perhaps related to the lack of connection between the fields. To help bridge this gap, we review some of the key literature in disability studies. We present case studies of two research projects in assistive technology and discuss how the field of disability studies influenced that work, led us to identify new or different problems relevant to the field of assistive technology, and helped us to think in new ways about the research process and its impact on the experiences of individuals who live with disability. We also discuss how the field of disability studies has influenced our teaching and highlight some of the key publications and publication venues from which our community may want to draw more deeply in the future.

A cord, although simple in form, has many interesting physical affordances that make it powerful as an input device. Not only can a length of cord be grasped in different locations, but also pulled, twisted and bent---four distinct and expressive dimensions that could potentially act in concert. Such an input mechanism could be readily integrated into headphones, backpacks, and clothing. Once grasped in the hand, a cord can be used in an eyes-free manner to control mobile devices, which often feature small screens and cramped buttons. In this note, we describe a proof-of-concept cord-based sensor, which senses three of the four input dimensions we propose. In addition to a discussion of potential uses, we also present results from our preliminary user study. The latter sought to compare the targeting performance and selection accuracy of different cord-based input modalities. We conclude with brief set of design recommendations drawn upon results from our study.

Energy use in the home is a topic of increasing interest and concern, and one on which technology can have a significant impact. However, existing work typically focuses on moderately affluent homeowners who have relative autonomy with respect to their home, or does not address socio-economic status, class, and other related issues. For the 30% of the U.S. population who rent their homes, many key decisions regarding energy use must be negotiated with a landlord. Because energy use impacts the bottom line of both landlords and tenants, this can be a source of conflict in the landlord/tenant relationship. Ubicomp technologies for reducing energy use in rental units must engage with landlord/tenant conflicts to be successful. Unfortunately, little detailed knowledge is available about the impact of landlord/tenant conflicts on energy use. We present an analysis of a series of qualitative studies with landlords and tenants. We argue that a consideration of multiple stakeholders, and the power imbalances among them, will drive important new research questions and lead to more widely applicable solutions. The main contribution of our work is a set of open research questions and design recommendations for technologies that may affect and be affected by the conflict between stakeholders around energy use.

Information about the location and size of the targets that users interact with in real world settings can enable new innovations in human performance assessment and soft-ware usability analysis. Accessibility APIs provide some information about the size and location of targets. How-ever this information is incomplete because it does not sup-port all targets found in modern interfaces and the reported sizes can be inaccurate. These accessibility APIs access the size and location of targets through low-level hooks to the operating system or an application. We have developed an alternative solution for target identification that leverages visual affordances in the interface, and the visual cues produced as users interact with targets. We have used our novel target identification technique in a hybrid solution that combines machine learning, computer vision, and accessibility API data to find the size and location of targets users select with 89% accuracy. Our hybrid approach is superior to the performance of the accessibility API alone: in our dataset of 1355 targets covering 8 popular applications, only 74% of the targets were correctly identified by the API alone.

New input technologies (such as touch), recognition based input (such as pen gestures) and next-generation interactions (such as inexact interaction) all hold the promise of more natural user interfaces. However, these techniques all create inputs with some uncertainty. Unfortunately, conventional infrastructure lacks a method for easily handling uncertainty, and as a result input produced by these technologies is often converted to conventional events as quickly as possible, leading to a stunted interactive experience. We present a framework for handling input with uncertainty in a systematic, extensible, and easy to manipulate fashion. To illustrate this framework, we present several traditional interactors which have been extended to provide feedback about uncertain inputs and to allow for the possibility that in the end that input will be judged wrong (or end up going to a different interactor). Our six demonstrations include tiny buttons that are manipulable using touch input, a text box that can handle multiple interpretations of spoken input, a scrollbar that can respond to inexactly placed input, and buttons which are easier to click for people with motor impairments. Our framework supports all of these interactions by carrying uncertainty forward all the way through selection of possible target interactors, interpretation by interactors, generation of (uncertain) candidate actions to take, and a mediation process that decides (in a lazy fashion) which actions should become final.

With the advent of Web 2.0 technologies, more and more online content is being generated by users. Even trained web developers often fail to take accessibility issues into consideration, so it is no surprise that users may fail to do so as well. In this paper, we examine two self-moderating communities of individuals with Lyme disease who are affected by ""brain fog"". Through qualitative analysis of over 100 discussion threads that deal with issues of accessibility, we explore how the individuals in these communities fail and succeed to establish and enforce, through moderation, the creation of cognitively accessible content.

The greatest contributor of CO2 emissions in the average American household is personal transportation. Because transportation is inherently a mobile activity, mobile devices are well suited to sense and provide feedback about these activities. In this paper, we explore the use of personal ambient displays on mobile phones to give users feedback about sensed and self-reported transportation behaviors. We first present results from a set of formative studies exploring our respondents' existing transportation routines, willingness to engage in and maintain green transportation behavior, and reactions to early mobile phone "green" application design concepts. We then describe the results of a 3-week field study (N=13) of the UbiGreen Transportation Display prototype, a mobile phone application that semi-automatically senses and reveals information about transportation behavior. Our contributions include a working system for semi-automatically tracking transit activity, a visual design capable of engaging users in the goal of increasing green transportation, and the results of our studies, which have implications for the design of future green applications.

In this paper we show that financial information can be used to sense many aspects of human activity. This simple technique gives people information about their daily lives, is easily accessible to many at no extra cost, requires little setup, and does not require the manufacture of any external devices. We will focus on how financial data can be used to show users where they spend their time, when they accomplish certain habits, and what the impact of their activities is on the environment. We validate our idea by implementing three demonstration applications intended for personal use. Finally, this paper discusses limitations of sensing using financial data and possible solutions.

Personal energy consumption, specifically home energy consumption such as heating, cooling, and electricity, has been an important environmental and economic topic for decades. Despite the attention paid to this area, few researchers have specifically explored these issues within a community that makes up approximately 30% of U.S. households -- those below the federal poverty line. We present a study of 26 low-income households in two very different locations -- a small town in the Southern U.S. and a northerly metropolitan area. Through a photo-elicitation study and directed interviews, we explore the relationship between energy saving behaviors, external factors, and users' intrinsic values and beliefs. Most of our participants are committed to saving energy for non-financial reasons, even when not responsible for paying bills. Challenges to saving energy include safety and lack of control over the environment. We discuss how Ubicomp technologies for saving energy can address some of these challenges.

Understanding how pointing performance varies in real world computer use and over time can provide valuable insight about how systems should accommodate changes in pointing behavior. Unfortunately, pointing data from individuals with pointing problems is rarely studied during real world use. Instead, it is most frequently evaluated in a laboratory where it is easier to collect and evaluate data. We developed a technique to collect and analyze real world pointing performance which we used to investigate the variance in performance of six individuals with a range of pointing abilities. Features of pointing performance we analyzed include metrics such as movement trajectories, clicking, and double clicking. These individuals exhibited high variance during both supervised and unsupervised (or real world) computer use across multiple login sessions. The high variance found within each participant highlights the potential inaccuracy of judging performance based on a single laboratory session.

Since not all persons interact with computer systems in the same way, computer systems should not interact with all individuals in the same way. This paper presents a significant step in automatically detecting characteristics of persons with a wide range of abilities based on observing their user input events. Three datasets are used to build learned statistical models on pointing data collected in a laboratory setting from individuals with varying ability to use computer pointing devices. The first dataset is used to distinguish between pointing behaviors from individuals with pointing problems vs. individuals without with 92.7% accuracy. The second is used to distinguish between pointing data from Young Adults and Adults vs. Older Adults vs. individuals with Parkinson’s Disease with 91.6% accuracy. The final data set is used to predict the need for a specific adaptation based on a user’s performance with 94.4 % accuracy. These results suggest that it may be feasible to use such models to automatically identify computer users who would benefit from accessibility tools, and to even make specific tool recommendations.

Over the past decade and a half, corporations and academies have invested considerable time and money in the realization of ubiquitous computing. Yet design approaches that yield ecologically valid understandings of ubiquitous computing systems, which can help designers make design decisions based on how systems perform in the context of actual experience, remain rare. The central question underlying this article is, What barriers stand in the way of real-world, ecologically valid design for ubicomp? Using a literature survey and interviews with 28 developers, we illustrate how issues of sensing and scale cause ubicomp systems to resist iteration, prototype creation, and ecologically valid evaluation. In particular, we found that developers have difficulty creating prototypes that are both robust enough for realistic use and able to handle ambiguity and error and that they struggle to gather useful data from evaluations because critical events occur infrequently, because the level of use necessary to evaluate the system is difficult to maintain, or because the evaluation itself interferes with use of the system. We outline pitfalls for developers to avoid as well as practical solutions, and we draw on our results to outline research challenges for the future. Crucially, we do not argue for particular processes, sets of metrics, or intended outcomes, but rather we focus on prototyping tools and evaluation methods that support realistic use in realistic settings that can be selected according to the needs and goals of a particular developer or researcher.

We present the iterative design of Momento, a tool that providesintegrated support for situated evaluation of ubiquitouscomputing applications. We derived requirements for Momento from a user-centered design process that includedinterviews, observations and field studies of early versionsof the tool. Motivated by our findings, Momento supportsremote testing of ubicomp applications, helps with participantadoption and retention by minimizing the need for newhardware, and supports mid-to-long term studies to addressinfrequently occurring data. Also, Momento can gather logdata, experience sampling, diary, and other qualitative data.

If applications were able to detect a user's expertise, then software could automatically adapt to better match exper-tise. Detecting expertise is difficult because a user's skill changes as the user interacts with an application and differs across applications. This means that expertise must be sensed dynamically, continuously, and unobtrusively so as not to burden the user. We present an approach to this prob-lem that can operate without a task model based on low-level mouse and menu data which can typically be sensed across applications at the operating systems level. We have implemented and trained a classifier that can detect "nov-ice" or "skilled" use of an image editing program, the GNU Image Manipulation Program (GIMP), at 91% accuracy, and tested it against real use. In particular, we developed and tested a prototype application that gives the user dy-namic application information that differs depending on her performance.

A common task in graphical user interfaces is controlling onscreen elements using a pointer. Current adaptive pointing techniques require applications to be built using accessibility libraries that reveal information about interactive targets, and most do not handle path/menu navigation. We present a pseudo-haptic technique that is OS and application independent, and can handle both dragging and clicking. We do this by associating a small force with each past click or drag. When a user frequently clicks in the same general area (e.g., on a button), the patina of past clicks naturally creates a pseudo-haptic magnetic field with an effect similar to that ofsnapping or sticky icons. Our contribution is a bottom-up approach to make targets easier to select without requiring prior knowledge of them.

Many of today's desktop applications are designed for use with a pointing device and keyboard. Someone with a disability, or in a unique environment, may not be able to use one or both of these devices. We have developed an approach for automatically modifying desktop applications to accommodate a variety of input alternatives as well as a demonstration implementation, the Input Adapter Tool (IAT). Our work is differentiated from past work by our focus on input adaptation (such as adapting a paint program to work without a pointing device) rather than output adaptation (such as adapting web pages to work on a cellphone). We present an analysis showing how different common interactive elements and navigation techniques can be adapted to specific input modalities. We also describe IAT, which supports a subset of these adaptations, and illustrate how it adapts different inputs to two applications, a paint program and a form entry program.

Rapid, early, but rough system prototypes are becoming a standard and valued part of the user interface design process. Pen, paper, and tools like Flash™ and Director™ are well suited to creating such prototypes. However, in the case of physical forms with embedded technology, there is a lack of tools for developing rapid, early prototypes. Instead, the process tends to be fragmented into prototypes exploring forms that look like the intended product or explorations of functioning interactions that work like the intended product - bringing these aspects together into full design concepts only later in the design process. To help alleviate this problem, we present a simple tool for very rapidly creating functioning, rough physical prototypes early in the design process - supporting what amounts to interactive physical sketching. Our tool allows a designer to combine exploration of form and interactive function, using objects constructed from materials such as thumbtacks, foil, cardboard and masking tape, enhanced with a small electronic sensor board. By means of a simple and fluid tool for delivering events to "screen clippings," these physical sketches can then be easily connected to any existing (or new) program running on a PC to provide real or Wizard of Oz supported functionality.

Sounds such as co-workers chatting nearby or a dripping faucet help us maintain awareness of and respond to our surroundings. Without a tool that communicates ambient sounds in a non-auditory manner, maintaining this awareness is difficult for people who are deaf. We present an iterative investigation of peripheral, visual displays of ambient sounds. Our major contributions are: (1) a rich understanding of what ambient sounds are useful to people who are deaf, (2) a set of visual and functional requirements for a peripheral sound display, based on feedback from people who are deaf, (3) lab-based evaluations investigating the characteristics of four prototypes, and (4) a set of design guidelines for successful ambient audio displays, based on a comparison of four implemented prototypes and user feedback. Our work provides valuable information about the sound awareness needs of the deaf and can help to inform further design of such applications.

Sounds constantly occur around us, keeping us aware of our surroundings. People who are deaf have difficulty maintaining an awareness of these ambient sounds. We present an investigation of peripheral, visual displays to help people who are deaf maintain an awareness of sounds in the environment. Our contribution is twofold. First, we present a set of visual design preferences and functional requirements for peripheral visualizations of non-speech audio that will help improve future applications. Visual design preferences include ease of interpretation, glance-ability, and appropriate distractions. Functional requirements include the ability to identify what sound occurred, view a history of displayed sounds, customize the information that is shown, and determine the accuracy of displayed information. Second, we designed, implemented, and evaluated two fully functioning prototypes that embody these preferences and requirements, serving as examples for future designers and furthering progress toward understanding how to best provide peripheral audio awareness for the deaf.

In this paper, we investigate how the choice of media for capture and access affects the diary study method. The diary study is a method of understanding participant behavior and intent in situ that minimizes the effects of observers on participants. We first situate diary studies within a framework of field studies and review related literature. We then report on three diary studies we conducted that involve photographs, audio recordings, location information and tangible artifacts. We then analyze our findings, specifically addressing the following questions: How do context information and episodic memory prompts captured by participants vary with media? In what way do different media "jog" memory? How do different media affect the diary study process? These questions are particularly important for diary studies because they can be especially useful as compared to other methods when a participant intends to do an action but does not or when actions are particularly difficult to sense. We also built and tested a tool based on participant and researcher frustrations with the method. Our contribution includes suggested modifications to traditional diary techniques that enable annotation and review of captured media; a new variation on the diary study appropriate for researchers using digital capture media; and a lightweight tool to support it, motivated by past work and findings from our studies.

The subArctic user interface toolkit has extensibility as one of its central goals. It seeks not only to supply a powerful library of reusable interactive objects, but also make it easy to create new, unusual, and highly customized interactions tailored to the needs of particular interfaces or task domains. A central part of this extensibility is the input model used by the toolkit. The subArctic input model provides standard reusable components that implement many typical input handling patterns for the programmer, allows inputs to be handled in very flexible ways, and allows the details of how inputs are handled to be modified to meet custom needs. This paper will consider the structure and operation of the subArctic input handling mechanism. It will demonstrate the flexibility of the system through a series of examples, illustrating techniques that it enables - many of which would be very difficult to implement in most toolkits.

Web access for users with disabilities is an important goal and challenging problem for web content developers and designers. This paper presents a comparison of different methods for finding accessibility problems affecting users who are blind. Our comparison focuses on techniques that might be of use to Web developers without accessibility experience, a large and important group that represents a major source of inaccessible pages. We compare a laboratory study with blind users to an automated tool, expert review by web designers with and without a screen reader, and remote testing by blind users. Multiple developers, using a screen reader, were most consistently successful at finding most classes of problems, and tended to find about 50% of known problems. Surprisingly, a remote study with blind users was one of the least effective methods. All of the techniques, however, had different, complementary strengths and weaknesses.

In interspecies households, it is common for the non homo sapien members to be isolated and ignored for many hours each day when humans are out of the house or working. For pack animals, such as canines, information about a pack member's extended pack interactions (outside of the nuclear household) could help to mitigate this social isolation. We have developed a Pack Activity Watch System: Allowing Broad Interspecies Love In Telecommunication with Internet-Enabled Sociability (PAWSABILITIES) for helping to support remote awareness of social activities. Our work focuses on canine companions, and includes, pawticipatory design, labradory tests, and canid camera monitoring.

Many context-aware services make the assumption that the context they use is completely accurate. However, in reality, both sensed and interpreted context is often ambiguous. A challenge facing the development of realistic and deployable context-aware services, therefore, is the ability to handle ambiguous context. Although some of this ambiguity may be resolved using automatic techniques, we argue that correct handling of ambiguous context will often need to involve the user. We use the term mediation to refer to the dialogue that ensues between the user and the system. In this article, we describe an architecture that supports the building of context-aware services that assume context is ambiguous and allows for mediation of ambiguity by mobile users in aware environments. We present design guidelines that arise from supporting mediation over space and time, issues not present in the graphical user interface domain where mediation has typically been used in the past. We illustrate the use of our architecture and evaluate it through an example context-aware application, a word predictor system.

As human-computer interaction becomes more closely modeled on human-human interaction, new techniques and strategies for human-computer interaction are required. In response to the inevitable shortcomings of recognition technologies, researchers have studied mediation: interaction techniques by which users can resolve system ambiguity and error. In this paper we approach the human-computer dialogue from the other side, examining system-initiated direction and mediation of human action. We conducted contextual interviews with a variety of experts in fields involving human-human direction, including a film director, photographer, golf instructor, and 911 operator. Informed by these interviews and a review of prior work, we present strategies for directing physical human action and an associated design space for systems that perform such direction. We illustrate these concepts with excerpts from our interviews and with our implemented system for automated media capture or Active Capture, in which an unaided computer system uses techniques identified in our design space to act as a photographer, film director, and cinematographer.

Traditionally, computer interfaces have been confined to conventional displays and focused activities. However, as displays become embedded throughout our environment and daily lives, increasing numbers of them must operate on the periphery of our attention. Peripheral displays can allow a person to be aware of information while she is attending to some other primary task or activity. We present the Peripheral Displays Toolkit (PTK), a toolkit that provides structured support for managing user attention in the development of peripheral displays. Our goal is to enable designers to explore different approaches to managing user attention. The PTK supports three issues specific to conveying information on the periphery of human attention. These issues are abstraction of raw input, rules for assigning notification levels to input, and transitions for updating a display when input arrives. Our contribution is the investigation of issues specific to attention in peripheral display design and a toolkit that encapsulates support for these issues. We describe our toolkit architecture and present five sample peripheral displays demonstrating our toolkit's capabilities.

We developed two visual displays for providing awareness of environmental audio to deaf individuals. Based on fieldwork with deaf and hearing participants, we focused on supporting awareness of non-speech audio sounds such as ringing phones and knocking in a work environment. Unlike past work, our designs support both monitoring and notification of sounds, support discovery of new sounds, and do not require a priori knowledge of sounds to be detected. Our Spectrograph design shows pitch and amplitude, while our Positional Ripples design shows amplitude and location of sounds. A controlled experiment involving deaf participants found neither display to be significantly distracting. However, users preferred the Positional Ripples display and found that display easier to monitor (notification sounds were detected with 90% success in a laboratory setting). The Spectrograph display also supported successful detection in most cases, and was well received when deployed in the field.

We present a technique for evaluating the usability and effectiveness of ambient displays. Ambient displays are abstract and aesthetic peripheral displays portraying non-critical information on the periphery of a user's attention. Although many innovative displays have been published, little existing work has focused on their evaluation, in part because evaluation of ambient displays is difficult and costly. We adapted a low-cost evaluation technique, heuristic evaluation, for use with ambient displays. With the help of ambient display designers, we defined a modified set of heuristics. We compared the performance of Nielsen's heuristics and our heuristics on two ambient displays. Evaluators using our heuristics found more, severe problems than evaluators using Nielsen's heuristics. Additionally, when using our heuristics, 3-5 evaluators were able to identify 40--60% of known usability issues. This implies that heuristic evaluation is an effective technique for identifying usability issues with ambient displays.

One of the first, most common, and most useful applications that today's computer users access is the World Wide Web (web). One population of users for whom the web is especially important is those with motor disabilities, because it may enable them to do things that they might not otherwise be able to do: shopping; getting an education; running a business. This is particularly important for low bandwidth users: users with such limited motor and speech that they can only produce one or two signals when communicating with a computer. We present requirements for low bandwidth web accessibility, and two tools that address these requirements. The first is a modified web browser, the second a proxy that modifies HTML. Both work without requiring web page authors to modify their pages.

Many context-aware services make the assumption that the context they use is completely accurate. However, in reality, both sensed and interpreted context is often ambiguous. A challenge facing the development of realistic and deployable context-aware services, therefore, is the ability to handle ambiguous context. In this paper, we describe an architecture that supports the building of context-aware services that assume context is ambiguous and allows for mediation of ambiguity by mobile users in aware environments. We illustrate the use of our architecture and evaluate it through three example context-aware services, a word predictor system, an In/Out Board, and a reminder tool.

Interfaces based on recognition technologies are used extensively in both the commercial and research worlds. But recognizers are still error-prone, and this results in human performance problems, brittle dialogues, and other barriers to acceptance and utility of recognition systems. Interface techniques specialized to recognition systems can help reduce the burden of recognition errors, but building these interfaces depends on knowledge about the ambiguity inherent in recognition. We have extended a user interface toolkit in order to model and to provide structured support for ambiguity at the input event level. This makes it possible to build re-usable interface components for resolving ambiguity and dealing with recognition errors. These interfaces can help to reduce the negative effects of recognition errors. By providing these components at a toolkit level, we make it easier for application writers to provide good support for error handling. Further, with this robust support, we a...

Because of its promise of natural interaction, recognition is coming into its own as a mainstream technology for use with computers. Both commercial and research applications are beginning to use it extensively. However the errors made by recognizers can be quite costly, and this is increasingly becoming a focus for researchers. We present a survey of existing error correction techniques in the user interface. These mediation techniques most commonly fall into one of two strategies, repetition and choice. Based on the needs uncovered by this survey, we have developed OOPS, a toolkit that supports resolution of input ambiguity through mediation. This paper describes four new interaction techniques built using OOPS, and the toolkit mechanisms required to build them. These interaction techniques each address problems not directly handled by standard approaches to mediation, and can all be re-used in a variety of settings. INTRODUCTION Because of its promise of natural interaction, recog...

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No abstract available.