We present HulaMove, a novel interaction technique that leverages the movement of the waist as a new eyes-free and hands-free input method for both the physical world and the virtual world. We first conducted a user study (N=12) to understand users’ ability to control their waist. We found that users could easily discriminate eight shifting directions and two rotating orientations, and quickly confirm actions by returning to the original position (quick return). We developed a design space with eight gestures for waist interaction based on the results and implemented an IMU-based real-time system. Using a hierarchical machine learning model, our system could recognize waist gestures at an accuracy of 97.5%. Finally, we conducted a second user study (N=12) for usability testing in both real-world scenarios and virtual reality settings. Our usability study indicated that HulaMove significantly reduced interaction time by 41.8% compared to a touch screen method, and greatly improved users’ sense of presence in the virtual world. This novel technique provides an additional input method when users’ eyes or hands are busy, accelerates users’ daily operations, and augments their immersive experience in the virtual world.
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
Olivia is a student at Santa Clara University pursuing a BS in Computer Science and Engineering with minors in Mathematics and Economics, and will be graduating in June, 2021. In the summer of 2019, she participated in CRA-WP’s Distributed Research Experience for Undergraduates (DREU) in the Make4All group with Jennifer Mankoff. She worked closely with Yasaman Sefidgar and Han Zhang to investigate the contribution of correlated stressors on mental health in college students leveraging actively-reported data from surveys and passively-sensed data from phones and wearables from the UWEXP study. She hopes to pursue a PhD in Computer Science and explore the field of human-computer interaction further.
Aashaka is a PhD candidate in the UW Paul G. Allen School of Computer Science and Engineering. She is advised by Dr. Jennifer Mankoff and Dr. Richard Ladner. Her research focuses on d/Deaf and hard-of-hearing communication accessibility and explores how can we support all ways of communicating. She explores a range of modalities (speechreading, signing, captioning) as well as languages (multilingualism) in my work. She aims to both document the fluidity of language/communication as well as build technologies that support minoritized communication practices.
Wen is a fourth-year undergraduate student majoring in Computer Science at the University of Washington. She is interested in the intersection between design, accessibility, and technology. She is also passionate about computer science education and making it equitable and accessible for all students. She is currently working with Venkatesh Potluri on implementing SPRITEs, a method for vision-impaired users to navigate two-dimensional structures using the keyboard surface, as a plugin on open-source screen readers.
Nayha is a junior at the Allen School majoring in Computer Science and minoring in Neural Computation and Engineering. She is passionate about using technology to make the world more accessible. She is currently working on the COVID PPE Analysis project where she does quantitative and qualitative analysis on features of Personal Protective Equipment (PPE) designed in response to the COVID-19 pandemic. In the Allen School, she is the chair of ACM-W, an organization dedicated to cultivating a strong supportive community of women in tech.
My name is Evelyn Yang and I am a sophomore at the University of Washington majoring in Computer Science and minoring in Education, Learning, and Society. I am interested in topics related to equity and accessibility in technology, and I hope to learn how to use CS to address those issues. I am currently working with Taylor on the accessible knitting project.
Jaimie is a third year undergraduate majoring in Informatics looking to concentrate on human-computer interaction and data science. She is interested in creating visual elements that users interact with and how information is presented to others. Currently, she is working on the UWEXP project in the lab.
Caiwei is a 3rd year undergraduate at UW. Her majors are Computer Science & Applied and Computational Mathematical Science, in the track of Data Science and Statistics. She has great interests in data manipulation and machine learning and would like to explore more in related fields. Currently working on a UWEXP project, she focuses on applying data analysis and machine learning skills to analyze students’ mental health.
