Sabrina Pearson
I’m a freshman majoring in Computer Science, from Kirkland Washington. In the lab, I am currently working on the Don’t Touch My Belly project, a fabrication project that aims to explore themes of consent, consisting of a maturity shirt that reacts the wearer’s pregnant belly is touched without asking. I am still exploring the many fields of computer science, and am passionate about using technology to solve human problems.
Hi, there! My name is Estelle Jiang and I’m currently a junior at the University of Washington majoring in Informatics, with a concentration in Human Computer Interaction. I’m passionate about exploring & creating the best experience for the user and designing sustainably and meaningful interactions between people, technology, and products. I think design is not only about how it looks like, but also what is inside.I have been working on Don’t Touch My Belly project in Make4All Lab.
Woosuk Seo
Woosuk is now a PhD student at the University of Michigan. While he was part of the UW EXP project, he wrote: I am a Research Assistant in Computer Science and Engineering. I earned my Bachelor’s degree in Informatics at University of Washington. My research goal is to understand the users and to provide them proper information through human-centered design. I aim to empower those people who are often marginalized from mainstream technology. More specifically, I am interested in social computing, health informatics and assistive technology. In the lab, I am working on UW Experience project.
Alex McGregor
I am a freshman majoring in Computer Science from Spokane, Washington. In the Make4All lab, I have been working on learning how to write programs using Python in the 3D modeling software Fusion 360. One of my biggest passions is 3D printing, so learning more about the modeling side of that has been very rewarding. This is the first lab that I have been a part of, so another thing that has been new for me is learning how labs run and how it differs from my normal coursework. I am excited to see what’s next and continue working with all the other great people in the lab!
Hi there! I’m a CSE senior passionate about creating physical, interactive things to solve human problems. In the lab I am currently working on Interactiles, which improves the accessibility of mobile phones by introducing tangible, tactile interaction to touchscreens, and Don’t Touch My Belly, a maternity shirt that reacts when the wearer’s pregnant belly is touched without asking and aims to explore themes of consent and women’s bodies. My portfolio can be seen here: http://dropr.com/tracytran
Jasper O’Leary
I am a PhD student in Human Centered Design and Engineering (HCDE). Previously, I earned my bachelor’s degree in Computer Science from the University of California, Berkeley. I study how digital fabrication technology can move beyond its focus on making trinket-scale objects for a universal “maker.” Instead, I imagine how we can leverage digital fabrication to build longer-lasting infrastructure such as urban installations and ad-hoc shelters. How can these tools could be useful in a diverse set of people and needs, rather than just appealing to one universal user? How can we build effectively at the body or building scale? What role do different materials play in this process? I approach these questions through a mix of research through design, ethnography, and system-building.
Website: jasperoleary.com
Nicole Riley
I am a computer science major from Bellevue, Washington. I am a post-baccalaureate student in CSE (I already have a degree in neurobiology and psychology from UW so I am happy to talk about brains as well as tech). In the Allen School I am the treasurer of ACM-W, a CSE Student Advisory Council at large representative, and a Society of Women Engineers Girls Who Code lead. I have tutored within the CSE department and TAed a variety of course (14x and 311). In the lab, I am working on the UW Experience project working on a dashboard that measures participant compliance.
Ni, H., Cho, S., Mankoff, J., & Yang, J. (2017). Automated recognition of hypertension through overnight continuous HRV monitoring. Journal of Ambient Intelligence and Humanized Computing, 1-13.
Hypertension is a common and chronic disease, caused by high blood pressure. Since hypertension often has no warning signs or symptoms, many cases remain undiagnosed. Untreated or sub-optimally controlled hypertension may lead to cardiovascular, cerebrovascular and renal morbidity and mortality, along with dysfunction of the autonomic nervous system. Therefore, it could be quite valuable to predict or provide early warnings about hypertension. Heart rate variability (HRV) analysis has emerged as the most valuable non-invasive test to assess autonomic nervous system function, and has great potential for detecting hypertension. However, HRV indicators may be subtle and present at random, resulting in two challenges: how to support continuous monitoring for hours at a time while being unobtrusive, and how to efficiently analyze the collected data to minimize data collection and user burden. In this paper, we present a machine learning-based approach for detecting hypertension, using a waist belt continuous sensing system that is worn overnight. Using 24 hypertension patients and 24 healthy controls, we demonstrate that our approach can differentiate hypertension patients from healthy controls with 93.33% accuracy. This represents a promising approach for performing hypertension classification in the field, and also we would improve its performance based on a large number of hypertensive subjects monitored by the proposed pervasive sensors.
Mark S. Baldwin, Gillian R. Hayes, Oliver L. Haimson, Jennifer Mankoff, Scott E. Hudson: The Tangible Desktop: A Multimodal Approach to Nonvisual Computing. TACCESS 10(3): 9:1-9:28 (2017)
Audio-only interfaces, facilitated through text-to-speech screen reading software, have been the primary mode of computer interaction for blind and low-vision computer users for more than four decades. During this time, the advances that have made visual interfaces faster and easier to use, from direct manipulation to skeuomorphic design, have not been paralleled in nonvisual computing environments. The screen reader–dependent community is left with no alternatives to engage with our rapidly advancing technological infrastructure. In this article, we describe our efforts to understand the problems that exist with audio-only interfaces. Based on observing screen reader use for 4 months at a computer training school for blind and low-vision adults, we identify three problem areas within audio-only interfaces: ephemerality, linear interaction, and unidirectional communication. We then evaluated a multimodal approach to computer interaction called the Tangible Desktop that addresses these problems by moving semantic information from the auditory to the tactile channel. Our evaluation demonstrated that among novice screen reader users, Tangible Desktop improved task completion times by an average of 6 minutes when compared to traditional audio-only computer systems.
Also see: Mark S. Baldwin, Jennifer Mankoff, Bonnie A. Nardi, Gillian R. Hayes: An Activity Centered Approach to Nonvisual Computer Interaction. ACM Trans. Comput. Hum. Interact. 27(2): 12:1-12:27 (2020)
Kim, J., Guo, A., Yeh, T., Hudson, S. E., & Mankoff, J. (2017, June). Understanding Uncertainty in Measurement and Accommodating its Impact in 3D Modeling and Printing. In Proceedings of the 2017 Conference on Designing Interactive Systems (pp. 1067-1078). ACM.
3D printing enables everyday users to augment objects around them with personalized adaptations. There has been a proliferation of 3D models available on sharing platforms supporting this. If a model is parametric, a novice modeler can obtain a custom model simply by entering a few parameters (e.g., in the Customizer tool on Thingiverse.com). In theory, such custom models could fit any real world object one intends to augment. But in practice, a printed model seldom fits on the first try; multiple iterations are often necessary, wasting a considerable amount of time and material. We argue that parameterization or scaling alone is not sufficient for customizability, because users must correctly measure an object to specify parameters.
In a study of attempts to measure length, angle, and diameter, we demonstrate measurement errors as a significant (yet often overlooked) factor that adversely impacts the adaptation of 3D models to existing objects, requiring increased iteration. Images taken from our study are shown below.
We argue for a new design principle—accommodating measurement uncertainty—that designers as well as novices should begin to consider. We offer two strategies—modular joint and, buffer insertion—to help designers to build models that are robust to measurement uncertainty. Examples shown below.
