Bo is a master’s student in technology innovation at the University of Washington. His research has focused on designing and fabricating novel technologies to be more accessible and affordable for the public. In the Make4all lab, he is working on a tactile graphic project.
Yunqi (George) Wang is a senior at the University of Washington majoring in Computer Science and Mathematics. He is passionate about making technology more inclusive and considers humans as the primary factor when it comes to design practice. He is currently working on an EMG gesture project for people with disability to have better access to electronic devices.
Accessible design and technology could support the large and growing group of people with chronic illnesses. However, human computer interactions(HCI) has largely approached people with chronic illnesses through a lens of medical tracking or treatment rather than accessibility. We describe and demonstrate a framework for designing technology in ways that center the chronically ill experience. First, we identify guiding tenets: 1) treating chronically ill people not as patients but as people with access needs and expertise, 2) recognizing the way that variable ability shapes accessibility considerations, and 3) adopting a theoretical understanding of chronic illness that attends to the body. We then illustrate these tenets through autoethnographic case studies of two chronically ill authors using technology. Finally, we discuss implications for technology design, including designing for consequence-based accessibility, considering how to engage care communities, and how HCI research can engage chronically ill participants in research.
Kelly Mack*, Emma J. McDonnell*, Leah Findlater, and Heather D. Evans. In The 24th International ACM SIGACCESS Conference on Computers and Accessibility.
Sasha is excited about projects where engineering solutions meet medical needs, specifically those that enable individuals with disabilities to interact with the world around them in a more inclusive environment. In the past, she has worked on developing affordable and customizable orthotic devices for individuals with spinal cord injuries and attempted to simplify control methods for complex prosthetic hands. As a postdoc at UW, Sasha hopes to harness the advancements in metamaterials and smart textiles to create custom solutions for assistance and rehabilitation needs of individuals with disabilities.
Momona Yamagami is a CREATE postdoc at the Paul G. Allen Center for Computer Science & Engineering at the University of Washington. She is advised by Prof. Jennifer Mankoff. She completed her PhD in Electrical Engineering at the University of Washington advised by Profs. Sam Burden and Kat Steele in 2022.
Her research focuses on modeling and enhancing biosignals-based human-machine interaction to support accessibility and health. She is interested in studying how biosignals can be used to support accessible technology input tailored to an individual’s abilities.
Website: http://momona-yamagami.github.io/
Ellie is an undergraduate at Grinnell College in Grinnell, Iowa double majoring in Computer Science and Sociology. There, she works for Disability Resources creating community engagement content, building a new user-friendly website, and ensuring PDF compatibility with screen readers. Her interest in accessibility issues began here and was furthered by projects in her introductory computer science coursework. She is interested in human-computer interaction and hopes to use the intersection of both her degrees to work to use computing ethically in ways that promote accessibility and improve the human experience rather than exploit it.
She is working with Make4All during the summer of 2022 through the DUB REU and is affiliated with AccessComputing. She will be working on the machine embroidery project.
Tongyan is a first-year Master’s student at UW majoring in Industrial & Systems Engineering. She graduated from Peking University with a B. Eng. in Materials Science & Engineering in 2021, where she also engaged in research in the field of Operations Research focusing on the optimization of the emergency food system.
She has been deeply attracted to the field of accessibility since discovering it. She is passionate about promoting technology accessibility and hopes to utilize her interdisciplinary background to better serve the goal. Currently, she is working on the machine embroidery project at the lab.
Xuhai Xu, Prerna Chikersal, Janine M. Dutcher, Yasaman S. Sefidgar, Seo Woosuk, Michael J. Tumminia, Daniella K. Villalba, Sheldon Cohen,
Kasey G. Creswell, Creswell, J. David, Afsaneh Doryab, Paula S. Nurius, Eve Riskin, Anind K. Dey, & Jennifer Mankoff. Leveraging Collaborative-Filtering for Personalized Behavior Modeling: A Case Study of Depression Detection among College Students. Proc. ACM interact. mob. wearable ubiquitous technol., Article 41. (March 21) 27pages.
The prevalence of mobile phones and wearable devices enables the passive capturing and modeling of human behavior at an unprecedented resolution and scale. Past research has demonstrated the capability of mobile sensing to model aspects of physical health, mental health, education, and work performance, etc. However, most of the algorithms and models proposed in previous work follow a one-size-fits-all (i.e., population modeling) approach that looks for common behaviors amongst all users, disregarding the fact that individuals can behave very differently, resulting in reduced model performance. Further, black-box models are often used that do not allow for interpretability and human behavior understanding. We present a new method to address the problems of personalized behavior classification and interoperability, and apply it to depression detection among college students. Inspired by the idea of collaborative-filtering, our method is a type of memory-based learning algorithm. It leverages the relevance of mobile-sensed behavior features among individuals to calculate personalized relevance weights, which are used to impute missing data and select features according to a specific modeling goal (e.g., whether the student has depressive symptoms) in different time epochs, i.e., times of the day and days of the week. It then compiles features from epochs using majority voting to obtain the final prediction. We apply our algorithm on a depression detection dataset collected from first-year college students with low data-missing rates and show that our method outperforms the state-of the-art machine learning model by 5.1% in accuracy and 5.5% in F1 score. We further verify the pipeline-level generalizability of our approach by achieving similar results on a second dataset, with an average improvement of 3.4% across performance metrics. Beyond achieving better classification performance, our novel approach is further able to generate personalized interpretations of the models for each individual. These interpretations are supported by existing depression-related literature and can potentially inspire automated and personalized depression intervention design in the future.
Nurius, P. S., Sefidgar, Y. S., Kuehn, K. S, Jake, X, Zhang, H., Browning, A., Riskin, E., Dey, A. K., & Mankoff, J. Distress among undergraduates: Marginality, stressors and resilience supports. Journal of American College Health, 1-9.
Stress and related mental health struggles are of growing concern at colleges and universities across the country and internationally, with some evidence of levels higher than general population peers. The university experience can pose considerable strain on students, in some cases adding to early and current life stressors, and, if not mitigated, can lead to impaired well-being and academic success/retention.
This study provides a 2019 data snapshot of multiple stressor effects on early-stage students, resilience resources (or the lack thereof) that can mitigate these effects, and sociodemographic characteristics reflecting minoritized identities. Participants were 253 first- and second-year undergraduate students (age =18.76; 49.80% male, 69% students of color) enrolled at the University of Washington.
Multivariate analysis demonstrated significant associations between greater stress exposures and lower levels of resilience resources with each of three mental health indicators—perceived stress (intensity of experienced stress), depression, and anxiety. Stressors such as poor physical health, discrimination exposure, experiencing one or more marginalizing status (e.g., first generation student, having disabilities, sexual minority), and using maladaptive coping strategies (e.g, denial, self-blame) significantly accounted for each of the mental health indicators. Prior stressors such as adverse childhood experiences and other life and academic adversities were also significantly correlated with the mental health variables.
Race/ethnicity was less clearly patterned, although students of Asian descent reported significantly greater depression and anxiety, and females reported higher levels on all distress forms. In terms of resilience supports, those reporting greater social support and perception of oneself as a “bounce back” kind of person reported lesser psychological distress and these variables reduced the effects of stressors. Assessment of student well-being from this same project during the 2020 COVID-19 context indicated that students entering the pandemic with mental health vulnerabilities experienced significantly greater psychological distress and academic strain as the university pivoted toward remote instruction, signaling highly consequential differences (Morris et al., 2021)
These results support the value of “poly-strengths” –multiple forms of resilience- fostering resources–for mitigating the effects of stressors on psychological distress. College leaders are noting increases in the severity of students’ mental health concerns and demand for services, changing the roles of campus counseling centers, and requiring new institutional responses. Better understanding cumulative stress/resilience resource profiles, particularly among marginalized students and those experiencing discrimination, can help universities in prioritizing institutional support responses toward prevention, strengthening resilience, and mitigating psychological distress.
Han Zhang, Margaret E. Morris, Paula S. Nurius, Kelly Mack, Jennifer Brown, Kevin S. Kuehn, Yasaman S. Sefidgar, Xuhai Xu, Eve A. Riskin, Anind K. Dey and Jennifer Mankoff. Impact of Online Learning in the Context of COVID-19 on Undergraduates with Disabilities and Mental Health Concerns. ACM Transactions on Accessible Computing (TACCESS).
The COVID-19 pandemic upended college education and the experiences of students due to the rapid and uneven shift to online learning. This study examined the experiences of students with disabilities with online learning, with a consideration of surrounding stressors such as financial pressures. In a mixed method approach, we compared 28 undergraduate students with disabilities(including mental health concerns) to their peers during 2020, to assess differences and similarities in their educational concerns, stress levels and COVID-19 related adversities. We found that students with disabilities entered the Spring quarter of 2020 with significantly higher concerns about classes going online, and reported more recent negative life events than other students. These differences between the two groups diminished three months later with the exception of recent negative life events. For a fuller understanding of students’ experiences, we conducted qualitative analysis of open ended interviews. We examined both positive and negative experiences with online learning among students with disabilities and mental health concerns. Online learning enabled greater access – e.g., reducing the need for travel to campus–alongside ways in which online learning impeded academic engagement–e.g., reducing interpersonal interaction. Learning systems need to continue to meet the diverse and dynamic needs of students with disabilities.
![[An Asian male person] stands and smiles, facing the camera. He wears a hat, a sweater, and a jacket. The background has snowy mountains.](https://i0.wp.com/make4all.org/wp-content/uploads/Bo_liu-scaled-e1665435836976.jpg?resize=1038%2C576&ssl=1)
