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Megan Hofmann

Megan is a Phd Student at the Human Computer Interaction Institute at Carnegie Mellon Unviversity. She is advised by Prof. Jennifer Mankoff of the University of Washington and and Prof. Scott E. Hudson. She completed her bachelors in Computer Science at Colorado State University in 2017. She is an NSF Fellow, and a Center for Machine Learning and Health Fellow. During her Undergraduate degree Megan’s research was adviced by Dr. Jaime Ruiz and Prof. Amy Hurst.

Her research focuses on creating computer aided design and fabrication tools that expand the digital fabrication process with new materials. She uses participatory observation and participatory design methods to study assistive technology and digital fabrication among many stakeholder (people with disabilities, caregivers, and clinicians).

Visit Megan’s homepage at https://www.megan-hofmann.com/publications/.

Research

Some recent projects (see more)

Ribbed and checkered knit colors generated programmatically using knitscript.

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

Model of an automatically-generated flat splint with breathing holes, and the same splint after it has been printed, heated, and shaped around a thumb

Domain Specific Metaheuristic Optimization

A picture of two people wearing 3D printed PPE. On the left is a face shield (42.2% of submissions) and on the right a facemask (28.4% of submissions).

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

Optimized tactile map

Maptimizer

Sampler of PPE found on the NIH repository

Medical Making During COVID

KnitPick: Manipulating Texture

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.

Digital Fabrication in Medical Practice

Splint that has been 3D printed in a material of an appropriate skin color and fit to a client's hand.

“Occupational Therapy is Making”

Understanding gender equity in author order assignment

Picture of 3D models and a printout

Expressing and Reusing Design Intent in 3D Models

Helping Hands

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:

Natasha Sidik

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Natasha Ann Sidik is a Senior at the University of Washington majoring in Psychology with a Minor in Informatics. As an advocate for inclusivity, she centers most of her work on learning, normalizing, and sharing best practices around accessibility. 
Growing up in Indonesia and the US as a non-traditional student gave her many perspectives and allowed her to network with diverse groups of people. Under the make4all Lab, Natasha is currently working on research to help improve the experiences of students with disabilities at the University of Washington. Find more of her work at https://sidiknatasha.github.io/portfolio/.

Navigating Illness, Finding Place

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Sylvia Janicki, Matt Ziegler, Jennifer Mankoff:
Navigating Illness, Finding Place: Enhancing the Experience of Place for People Living with Chronic Illness. COMPASS 2021: 173-187

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.

https://youtu.be/mo8ZIeDXyGE?t=1297
(Left) Users can select accessibility qualities to filter the places shown on the map. (Right) Clicking on a place opens a popup window to see all of that place's coded accessibility qualities, pictures, reviews, and additional textual descriptions.

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.

Interaction via Wireless Earbuds

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Xuhai Xu, Haitian Shi, Xin Yi, Wenjia Liu, Yukang Yan, Yuanchun Shi, Alex Mariakakis, Jennifer Mankoff, Anind K. Dey:
EarBuddy: Enabling On-Face Interaction via Wireless Earbuds. CHI 2020: 1-14

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

  • A user is tapping on their cheek. The tapping is sensed by a microphone in the earphone.
  • Plot of Test Accuracy versus number of left-out user's additional samples
  • Two barplots from the EarBuddy user study comparing EarBuddy, Table and Pocket.
  • Gesture design of EarBuddy.

Yuna Liu

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Yuna Liu is a second-year undergraduate majoring in Mathematics and Applied Mathematics. She is interested in simulation and mathematical modelling, and hopes to go to graduate school to study related fields. Yuna is currently on a UW EXP project that focuses on systematic review about the generalizability of passive sensing for health & well-being.

Brian Lee

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My name is Brian Lee and I am a Junior at the University of Washington studying computer science.
I am passionate about human computer interaction and accessibility in technology, and I am learning to build applications that can have an impact on everyone, not just a select few.
Currently, I am working with Kelly on the Sensing project, building a Samsung SmartWatch and Android phone app to allow people with chronic illnesses to tag and track sensor data throughout their day.

Aadi Jain

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I am an avid software enthusiast with keen interest and experience in a wide array of software domains ranging from full stack to low level embedded programming. Currently, a Junior here at the Paul G Allen Institute at UW pursuing Computer Science. I am working on the Sensing App under the supervision of Kelly Mack at the Make4All lab.

Simona Liao

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Simona is a sophomore at UW majoring in Computer Science and minoring in Gender, Women, Sexuality Studies. As an interdisciplinary student, she is passionate about applying technical skills to create a more equitable society. Currently, Simona is working on the UW EXP Study, which aimed to improve the well-being of Engineering students and process the EMA data collected from surveys. Simona is actively involved in leadership roles in the Society of Women Engineers at UW and Minorities in Tech in the Allen School.