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Anticipate and Adjust: Cultivating Access in Human-Centered Methods

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In order for “human-centered research” to include all humans, we need to make sure that research practices are accessible for both participants and researchers with disabilities. Yet, people rarely discuss how to make common methods accessible. We interviewed 17 accessibility experts who were researchers or community organizers about their practices. Our findings emphasize the importance of considering accessibility at all stages of the research process and across different dimensions of studies like communication, materials, time, and space. We explore how technology or processes could reflect a norm of accessibility and offer a practical structure for planning accessible research.

Anticipate and Adjust: Cultivating Access in Human-Centered Methods. Kelly Mac, Emma J. McDonnell, Venkatesh Potluri, Maggie Xu, Jailyn Zabala, Jeffrey P. Bigham, Jennifer Mankoff, and Cynthia L. Bennett. CHI 2022

Medical Making During COVID

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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:

BLV Understanding of Visual Semantics

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Venkatesh Potluri, Tadashi E. Grindeland, Jon E. Froehlich, Jennifer Mankoff: Examining Visual Semantic Understanding in Blind and Low-Vision Technology Users. CHI 2021: 35:1-35:14

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.

Understanding Disabled Knitters

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Taylor Gotfrid, Kelly Mack, Kathryn J. Lum, Evelyn Yang, Jessica K. Hodgins, Scott E. Hudson, Jennifer Mankoff: Stitching Together the Experiences of Disabled Knitters. CHI 2021: 488:1-488:14

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

KnitGIST: Generative Texture Design

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Hofmann, M., Mankoff, J., & Hudson, S. E. (2020, October). KnitGIST: A Programming Synthesis Toolkit for Generating Functional Machine-Knitting Textures. In Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology (pp. 1234-1247).

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.

Picture of three different textures and how they look compressed and stretched.
Picture showing Stockinette, Cables, Seed stitch and Ribbing
A picture of a knit lampshade in blue and green surrounding a lit lamp on an orange table.

Living Disability Theory

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A picture of a carved wooden cane in greens and blues

It was my honor this year to participate in an auto-ethnographic effort to explore accessibility research from a combination of personal and theoretical perspectives. In the process, and thanks to my amazing co-authors, I learned so much about myself, disability studies, ableism and accessibility.

Best Paper Award Hoffman, M., Kasnitz, D., Mankoff, J. and Bennett, C. l. (2020) Living Disability Theory: Reflections on Access, Research, and Design. In Proceedings of ASSETS 2020, 4:1-4:13

Abstract: 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.

KnitPick: Manipulating Texture

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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.

Passively-sensing Discrimination

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See the UW News article featuring this study!

A deeper understanding of how discrimination impacts psychological health and well-being of students would 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 the experience, 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 less time in bed on the day of the event. Some of these patterns continue the next day.

Passively-sensed Behavioral Correlates of Discrimination Events in College Students. Yasaman S. Sefidgar, Woosuk Seo, Kevin S. Kuehn, Tim Althoff, Anne Browning, Eve Ann Riskin, Paula S. Nurius, Anind K Dey, Jennifer Mankoff. CSCW 2019.

A bar plot sorted by number of reports, with about 100 reports of unfair treatment based on national origin, 90 based on intelligence, 70 based on gender, 60 based on apperance, 50 on age, 45 on sexual orientation, 35 on major, 30 on weight, 30 on height, 20 on income, 10 on disability, 10 on religion, and 10 on learning
Breakdown of 448 reports of unfair treatment by type. National, Orientation, and Learning refer to ancestry or national origin, sexual orientation, and learning disability respectively. See Table 3 for details of all categories. Participants were able to report multiple incidents of unfair treatment, possibly of different types, in each report. As described in the paper, we do not have data on unfair treatment based on race.
A heatplot showing sensor data collected by day in 5 categories: Activity, screen, locations, fitbit, and calls.
A heatplot showing compliance with sensor data collection. Sensor data availability for each day of the study is shown in terms of the number of participants whose data is available on a given day. Weeks of the study are marked on the horizontal axis while different sensors appear on the vertical axis. Important calendar dates (e.g., start / end of the quarter and exam periods) are highlighted as are the weeks of daily surveys. The brighter the cells for a sensor the larger the number of people contributing data for that sensor. Event-based sensors (e.g., calls) are not as bright as sensors continuously sampled (e.g., location) as expected. There was a technical issue in the data collection application in the middle of study, visible as a dark vertical line around the beginning of April.
A diagram showing compliance in surveys, organized by nweek of study. One line shows compliance in the large surveys given at pre, mid and post, which drops from 99% to 94% to 84%. The other line shows average weekly compliance in EMAs, which goes up in the second week to 93% but then drops slowly (with some variability) to 89%
Timeline and completion rate of pre, mid, and post questionnaires as well as EMA surveys. Y axis
shows the completion rates and is narrowed to the range 50-100%. The completion rate of pre, mid, and post questionnaires are percentages of the original pool of 209 participants, whereas EMA completion rates are based on the 176 participants who completed the study. EMA completion rates are computed as the average completion rate of the surveys administered in a certain week of the study. School-related events (i.e., start and end of quarters as well as exam periods) are marked. Dark blue bars (Daily Survey) show the weeks when participants answered surveys every day, four times a day
Barplot showing significance of morning screen use, calls, minutes asleep, time in bed, range of activities, number of steps, anxiety, depression, and frustration on the day before, of, and after unfair treatment. All but minutes asleep are significant at p=.05 or below on the day of discrimination, but this drops off after.
Patterns of feature significance from the day before to two days after the discrimination event. The
shortest bars represent the highest significance values (e.g., depressed and frustrated on day 0; depressed on day 1; morning screen use on day 2). There are no significant differences the day before. Most short-term relationships exist on the day of the event, a few appear on the next day (day 1). On the third day one
significant difference, repeated, from the first day is observed.

Digital Fabrication in Medical Practice

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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.

Designing in the Public Square

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Design in the Public Square: Supporting Cooperative Assistive Technology Design Through Public Mixed-Ability Collaboration (CSCW 2019)

Mark. S. Baldwin, Sen H Hirano, Jennifer Mankoff, Gillian Hayes

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 DEVICE, 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.

A close-up of version three of the CoOP system mounted to the rudder assembly and the transmitter
used to control the rudder (right corner).
Shows 5 iterations of the CoOP system, each of which is progressively less bulky, and more integrated (the first is strapped on for example and the last is more integrated).
The design evolution of the CoOP system in order of iteration from left to right.