Archives

KnitScript: A Domain-Specific Scripting Language for Advanced Machine Knitting

Leave a comment

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.

Megan Hofmann, Lea Albaugh, Tongyan Wang, Jennifer Mankoff, Scott E. Hudson: KnitScript: A Domain-Specific Scripting Language for Advanced Machine Knitting. UIST 2023: 21:1-21:21

https://youtube.com/watch?v=emV3xNVlg-0%3Fsi%3DLOM1EWRCmhcnzQJY

Domain Specific Metaheuristic Optimization

Leave a comment

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.

Megan Hofmann, Nayha Auradkar, Jessica Birchfield, Jerry Cao, Autumn G. Hughes, Gene S.-H. Kim, Shriya Kurpad, Kathryn J. Lum, Kelly Mack, Anisha Nilakantan, Margaret Ellen Seehorn, Emily Warnock, Jennifer Mankoff, Scott E. Hudson: OPTIMISM: Enabling Collaborative Implementation of Domain Specific Metaheuristic Optimization. CHI 2023: 709:1-709:19

https://youtube.com/watch?v=wjQrFeLbOiw%3Fsi%3DkMTxEkEBjoUrQDJ3

Rapid Convergence: The Outcomes of Making PPE during a Healthcare Crisis

Leave a comment

Kelly Avery Mack, Megan Hofmann, Udaya Lakshmi, Jerry Cao, Nayha Auradkar, Rosa I. Arriaga, Scott E. Hudson, Jennifer Mankoff. Rapid Convergence: The Outcomes of Making PPE During a Healthcare Crisis. [Link to the paper]

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.

Maptimizer

Leave a comment

Megan Hofmann, Kelly Mack, Jessica Birchfield, Jerry Cao, Autumn G. Hughes, Shriya Kurpad, Kathryn J. Lum, Emily Warnock, Anat Caspi, Scott E. Hudson, Jennifer Mankoff:
Maptimizer: Using Optimization to Tailor Tactile Maps to Users Needs. CHI 2022: 592:1-592:15 [pdf]

Tactile maps can help people who are blind or have low vision navigate and familiarize themselves with unfamiliar locations. Ideally, tactile maps are created by considering an individual’s unique needs and abilities because of their limited space for representation. However, significant customization is not supported by existing tools for generating tactile maps. We present the Maptimizer system which generates tactile maps that are customized to a user’s preferences and requirements, while making simplified and easy to read tactile maps. 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 user study with six blind/low-vision participants, 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 that require significant customization by the end user.

A system diagram showing the maptimizer data flow setup. The inputs are geography sets, representations options, and user preferences. Geography types and representation options are paired and tuned using an optimizer. The output is a tactile map.

Medical Making During COVID

Leave a comment

The onset of COVID-19 led many makers to dive deeply into the potential applications of their work to help with the pandemic. Our group’s efforts on this front, all of which were collaborations with a variety of people from multiple universities, led me to this reflective talk about the additional work that is needed for us to take the next step towards democratizing fabrication.

https://www.youtube.com/watch?v=xfz7JDSyWXI&ab_channel=stanfordonline

This talk is based on a series of papers studying and working with people who make, including the following recent COVID-related papers:

KnitPick: Manipulating Texture

Leave a comment

Knitting creates complex, soft objects with unique and controllable texture properties that can be used to create interactive objects. However, little work addresses the challenges of using knitted textures. We present KnitPick: a pipeline for interpreting pre-existing hand-knitting texture patterns into a directed-graph representation of knittable structures (KnitGraphs) which can be output to machine and hand-knitting instructions. Using KnitPick, we contribute a measured and photographed data set of 300 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. Using these algorithms and textures in our data set we are able to create three Knitting based interactions: roll, tug, and slide. KnitPick is the first system to bridge the gap between hand- and machine-knitting when creating complex knitted textures.

KnitPick: Programming and Modifying Complex Knitted Textures for Machine and Hand Knitting, Megan Hofmann, Lea Albaugh, Ticha Sethapakdi, Jessica Hodgins, Scott e. Hudson, James McCann, Jennifer Mankoff. UIST 2019. The KnitPick Data set can be found here.

A picture of a knit speak file which is compiled into a knit graph (which can be modified using carving and patching) and then compiled to knitout, which can be printed on a knitting machine. Below the graph is a picture of different sorts of lace textures supported by knitpick.
KnitPick converts KnitSpeak into KnitGraphs which can be carved, patched and output to knitted results
A photograph of the table with our data measurement setup, along with piles of patches that are about to be measured and have recently been measured. One patch is attached to the rods and clips used for stretching.
Data set measurement setup, including camera, scale, and stretching rig
A series of five images, each progressively skinnier than the previous. Each image is a knitted texture with 4 stars on it. They are labeled (a) original swatch (b) 6 columns removed (c) 9 columns removed (d) 12 columns removed (e) 15 columns removed
The above images show a progression from the original Star texture to the same texture with 15 columns removed by texture carving. These photographs were shown to crowd-workers who rated their similarity. Even with a whole repetition width removed from the Stars, the pattern remains a recognizable star pattern.

Digital Fabrication in Medical Practice

Leave a comment

Maker culture in health care is on the rise with the rapid adoption of consumer-grade fabrication technologies. However, little is known about the activity and resources involved in prototyping medical devices to improve patient care. In this paper, we characterize medical making based on a qualitative study of medical 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 US and Canada, we analyze making activity in medical settings. We find that medical-makers share strategies to address risks, define labor roles, and acquire resources by adapting traditional structures or creating new infrastructures. 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-share social network to extend their strategies in support of medical making.

“Point-of-Care Manufacturing”: Maker Perspectives onDigital Fabrication in Medical Practice. Udaya Lakshmi, Megan Hofmann, Stephanie Valencia, Lauren Wilcox, Jennifer Mankoff and Rosa Arriaga. CSCW 2019. To Appear.

A venn diagram showing the domains of expertise of those we interviewed including people from hospitals, universities, non-profits, va networks, private practices, and government. We interviewed clinicians and facilitators in each of these domains and there was a great deal of overlap with participants falling into multiple categories. For example, one participant was in a VA network and in private practice, while another was at a university and also a non-profit.

“Occupational Therapy is Making”

Leave a comment
Splint that has been 3D printed in a material of an appropriate skin color and fit to a client's hand.
Best Paper Award Icon “Occupational Therapy is Making'”: Design Iteration and Digital Fabrication in Occupational Therapy

 

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 its \textit{do-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.

Understanding gender equity in author order assignment

Leave a comment

Academic success and promotion are heavily influenced by publication record. In many fields, including computer science, multi-author papers are the norm. Evidence from other fields shows that norms for ordering author names can influence the assignment of credit. We interviewed 38 students and faculty in human- computer interaction (HCI) and machine learning (ML) at two institutions to determine factors related to assignment of author order in collaborative publication in the field of computer science. We found that women were concerned with author order earlier in the process:

Our female interviews reported raising author order in discussion earlier in the process than men.

Interview outcomes informed metrics for our bibliometric analysis of gender and collaboration in papers published between 1996 and 2016 in three top HCI and ML conferences. We found expected results overall — being the most junior author increased the likelihood of first authorship, while being the most senior author increased the likelihood of last authorship. However, these effects disappeared or even reversed for women authors:

Comparison of regression weights for author rank (blue) with author rank crossed with gender (orange). Regression was predicting author position (first, middle, last)

Based on our findings, we make recommendations for assignment of credit in multi-author papers and interpretation of author order, particularly with respect to how these factors affect women.

Expressing and Reusing Design Intent in 3D Models

Leave a comment

Megan K Hofmann, Gabriella Han, Scott E Hudson, Jennifer Mankoff. Greater Than the Sum of Its PARTs: Expressing and Reusing Design Intent in 3D Models CHI 2018, To Appear.

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.

Picture of 3D models and a printout