![[An Indian male] wearing a brown hat and a gray hoodie, with the hood up, and a pair of sunglasses hooked onto the neck of the hoodie. Behind the boy is a large lake with a snow capped mountain and forest scenery. The boy is smiling at the camera, which is taken in a selfie style.](https://i0.wp.com/make4all.org/wp-content/uploads/main_photo-scaled-e1712018871537.jpg?resize=1038%2C576&ssl=1)
![[An Indian male] wearing a gray hoodie, sitting in a striped multicolor chair. In his arms are two kittens, both orange and white, the one on the left pressing its face into his arm while the other looks to the side. The boy is looking down at the cat on the right.](https://i0.wp.com/make4all.org/wp-content/uploads/personal_photo.jpg?resize=474%2C632&ssl=1)
Varun Narayanswamy is a student in the Master’s from Human Computer Interaction and Design (MHCI+D). His research interests include HCI, data visualization, frontend development, mobile development, and education technology.
Varun Narayanswamy
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Yusuf Mohammed
Yusuf is a second-year undergraduate at the University of Washington majoring in Computer Science. He has prior experience with full stack web development and databases. He is interested in how generative AI can be used to improve accessibility. He is also interested in Machine Learning and Systems Programming. In his free time, he enjoys playing spikeball and watching football. Currently, he is working on the Text Simplification Project in the Make4All lab.
KnitScript: A Domain-Specific Scripting Language for Advanced Machine Knitting
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
Domain Specific Metaheuristic Optimization
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
A Multi-StakeholderAnalysis of Accessibility in Higher Education
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 ofers insights into the communication and collaboration between key stakeholders in creating access in institutions of higher education. The authors present refectionsfrom their experiences working with disability service ofces 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
Kelly Avery Mack
, Natasha A Sidik, Aashaka Desai, Emma J. McDonnell, Kunal Mehta, Christina Zhang, Jennifer Mankoff: Maintaining the Accessibility Ecosystem: a Multi-Stakeholder Analysis of Accessibility in Higher Education. ASSETS 2023: 100:1-100:6
COVID-19 Risk Negotation
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.
Margaret E. Morris, Jennifer Brown, Paula S. Nurius, Savanna Yee, Jennifer Mankoff, Sunny Consolvo:
“I Just Wanted to Triple Check… They were all Vaccinated”: Supporting Risk Negotiation in the Context of COVID-19.ACM Trans. Comput. Hum. Interact. 30(4): 60:1-60:31 (2023)
Generative Artificial Intelligence’s Utility for Accessibility
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.
Glazko, K. S., Yamagami, M., Desai, A., Mack, K. A., Potluri, V., Xu, X., & Mankoff, J. An Autoethnographic Case Study of Generative Artificial Intelligence’s Utility for Accessibility. ASSETS 2023. https://dl.acm.org/doi/abs/10.1145/3597638.3614548
News: Can AI help boost accessibility? These researchers tested it for themselves
Presentation (starts at about 20mins)
How Do People with Limited Movement Personalize Upper-Body Gestures?
Personalized upper-body gestures that can enable input from diverse body parts (e.g., head, neck, shoulders, arms, hands, and fingers), and match the abilities of each user, might make gesture systems more accessible for people with upper-body motor disabilities. Static gesture sets that make ability assumptions about the user (e.g., touch thumb and index finger together in midair) may not be accessible. In our work, we characterize the personalized gesture sets designed by 25 participants with upper-body motor disabilities. We found that the personalized gesture sets that participants designed were specific to their abilities and needs. Six participants mentioned that their inspiration for designing the gestures was based on “how I would do [the gesture] with the abilities that I have”. We suggest three considerations when designing accessible upper-body gesture interfaces:
1) Track the whole upper body. Our participants used their whole upper-body to perform the gestures, and some switched back and forth from the left to the right hand to combat fatigue.
2) Use sensing mechanisms that are agnostic to the location and orientation of the body. About half of our participants kept their hand on or barely took their hand off of the armrest to decrease arm movement and fatigue.
3) Use sensors that can sense muscle activations without movement. Our participants activated their muscles but did not visibly move in 10% of the personalized gestures.
Our work highlights the need for personalized upper-body gesture interfaces supported by multimodal biosignal sensors (e.g., accelerometers, sensors that can sense muscle activity like EMG).
Julie Zhang
Julie Zhang is a freshman at the University of Washington intending to major in Computer Science. She has prior coding experience with data analysis and front-end web development. She hopes to learn more about qualitative coding, human-computer interactions, and fabrication technology to improve accessibility. In her free time, she enjoys running, crocheting, and gardening. She’s excited to work on mobility devices with Make4All!
Brianna Lynn Wimer
Brianna is a Ph.D. student in Computer Science and Engineering at the University of Notre Dame and a visiting researcher at the University of Washington. She’s advised by Dr. Ronald Metoyer (Notre Dame) and Dr. Jennifer Mankoff (Washington). Brianna earned her Bachelor’s in Computer Science from the University of Alabama in 2021, advised by Prof. Chris Crawford. She is also a Google Ph.D. Fellow.
Her research centers on improving data visualizations for accessibility, particularly for those with visual impairments. She works on identifying accessibility challenges and crafting more user-friendly interactive visualization experiences.
Visit Brianna’s homepage at: https://www.briannawimer.com/