We present an algorithm for global knit structure planning that leverages a generalization of power diagrams to triangulated surfaces. This generalization is based on modified geodesic heat kernels and is used to quantize the curl measure of a normalized knitting time function gradient. Knit singularity positions are optimized jointly in a global fashion via an iterative Lloyd-type algorithm, leading to faster and more optimal placement of singularities than prior work, allowing for practical creation of denser knit graphs. In this denser setting, we present singularity ordering constraints that more robustly achieve helix-free knit graphs. The speed and robustness of the method is demonstrated via a diverse array of knits, and a virtual gallery of helix-free knit graphs. We also provide further demonstration of user constraints for knit singularity masking, level set alignment constraints, and apparent seam placement via curl boosting.

Accessibility research can only serve the needs of marginalized populations by considering intersectional and critical frameworks that provide a complete picture of how access is created in different contexts.To identify the novel assistive practices of often ignored disabled communities, we present findings from 12 interviews with disabled sex workers (i.e., people who sell their own erotic labor) (e.g., escorting, webcamming, lap dancing).Based on their experiences, we present systems of access as a framework to analyze how disabled people develop access strategies in contexts that include stakeholders that actively intend them harm.By applying this framework, we call on researchers to: presume the presence of adversarial stakeholders in the lives of marginalized disabled communities, respect the consequence-based and harm-reduction practices of disabled people in these contexts, and seek out these marginalized communities with safe practices that prevent undue harm.

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.

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.

Digital knitting machines have the capability to reliably manufacture seamless, textured, and multi-material garments, but these capabilities are obscured by limiting CAD tools. Recent innovations in computational knitting build on emerging programming infrastructure that gives full access to the machine’s capabilities but requires an extensive understanding of machine operations and execution. In this paper, we contribute a critical missing piece of the knitting-machine programming pipeline–a program optimizer. Program optimization allows programmers to focus on developing novel algorithms that produce desired fabrics while deferring concerns of efficient machine operations to the optimizer. We present KODA, the Knit-program Optimization by Dependency Analysis method. KODA re-orders and reduces machine instructions to reduce knitting time, increase knitting reliability, and manage boilerplate operations that adjust the machine state. The result is a system that enables programmers to write readable and intuitive knitting algorithms while producing efficient and verified programs.

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.

With recent advances in Generative AI, it is becoming easier to automatically manipulate 3D models. However, current methods tend to apply edits to models globally, which risks compromising the intended functionality of the 3D model when fabricated in the physical world. For example, modifying functional segments in 3D models, such as the base of a vase, could break the original functionality of the model, thus causing the vase to fall over. We introduce a method for automatically segmenting 3D models into functional and aesthetic elements. This method allows users to selectively modify aesthetic segments of 3D models, without affecting the functional segments. To develop this method we first create a taxonomy of functionality in 3D models by qualitatively analyzing 1000 models sourced from a popular 3D printing repository, Thingiverse. With this taxonomy, we develop a semi-automatic classification method to decompose 3D models into functional and aesthetic elements. We propose a system called Style2Fab that allows users to selectively stylize 3D models without compromising their functionality. We evaluate the effectiveness of our classification method compared to human-annotated data, and demonstrate the utility of Style2Fab with a user study to show that functionality-aware segmentation helps preserve model functionality.

We present FibeRobo, a thermally-actuated liquid crystal elastomer (LCE) fiber that can be embedded or structured into textiles and enable silent and responsive interactions with shape-changing, fiber-based interfaces. Three definitive properties distinguish FibeRobo from other actuating threads explored in HCI. First, they exhibit rapid thermal self-reversing actuation with large displacements (∼40%) without twisting. Second, we present a reproducible UV fiber drawing setup that produces hundreds of meters of fiber with a sub-millimeter diameter. Third, FibeRobo is fully compatible with existing textile manufacturing machinery such as weaving looms, embroidery, and industrial knitting machines. This paper contributes to developing temperature-responsive LCE fibers, a facile and scalable fabrication pipeline with optional heating element integration for digital control, mechanical characterization, and the establishment of higher hierarchical textile structures and design space. Finally, we introduce a set of demonstrations that illustrate the design space FibeRobo enables.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Motion gestures are an underutilized input modality for mobile interaction despite numerous potential advantages. Negulescu et al. found that the lack of feedback on attempted motion gestures made it difficult for participants to diagnose and correct errors, resulting in poor recognition performance and user frustration. In this article, we describe and evaluate a training and feedback technique, Glissando , which uses audio characteristics to provide feedback on the system’s interpretation of user input. This technique enables feedback by verbally confirming correct gestures and notifying users of errors in addition to providing continuous feedback by manipulating the pitch of distinct musical notes mapped to each of three dimensional axes in order to provide both spatial and temporal information.

In this abstract, we present a modular design methodology for prototyping and 3D printing affordable, highly customized, assistive technology. This methodology creates 3D printed attachments for prosthetic limbs that perform a diverse group of tasks. We demonstrate the methodology with two case studies where two participants with upper limb amputations help design devices to play the cello and use a hand-cycle.

An increasing number of online communities support the open-source sharing of designs that can be built using rapid prototyping to construct physical objects. In this paper, we examine the designs and motivations for assistive technology found on Thingiverse.com, the largest of these communities at the time of this writing. We present results from a survey of all assistive technology that has been posted to Thingiverse since 2008 and a questionnaire distributed to the designers exploring their relationship with assistive technology and the motivation for creating these designs. The majority of these designs are intended to be manufactured on a 3D printer and include assistive devices and modifications for individuals with disabilities, older adults, and medication management. Many of these designs are created by the end-users themselves or on behalf of friends and loved ones. These designers frequently have no formal training or expertise in the creation of assistive technology. This paper discusses trends within this community as well as future opportunities and challenges.

In this demonstration, we discuss a case study involving a student with limited hand motor ability and the process of exploring consumer grade, Do-It-Yourself (DIY) technology in order to create a viable assistive solution. This paper extends our previous research into DIY tools in special education settings [1] and presents the development of a unique tool, GripFab, for creating 3D-printed custom handgrips. We offer a description of the design process for a handgrip, explain the motivation behind the creation of GripFab, and explain current and planned features of this tool.