Venkatesh Potluri is a Ph.D. student at the Paul G. Allen Center for Computer Science & Engineering at University of Washington. He is advised by Prof Jennifer Mankoff and Prof Jon Froehlich. Venkatesh believes that technology, when designed right, empowers everybody to fulfill their goals and aspirations. His broad research goals are to upgrade accessibility to the ever-changing ways of our interactions with technology, and, improve the independence and quality of life of people with disabilities. These goals stem from his personal experience as a researcher with a visual impairment. His research focus is to enable developers with visual impairments perform a variety of programming tasks efficiently. Previously, he was a Research Fellow at Microsoft Research India, where his team was responsible for building CodeTalk, an accessibility framework and a plugin for better IDE accessibility. Venkatesh earned a master’s degree in Computer Science at International Institute of Information Technology Hyderabad, where his research was on audio rendering of mathematical content.
Xin is a first-year Ph.D. student with Jennifer Mankoff and Shwetak Patel in the Paul G. Allen School of Computer Science & Engineering at the University of Washington – Seattle. Prior to joining UW, he obtained a Bachelor’s degree in computer science from the University of Massachusetts Amherst in 2018. While at UMass Amherst, he received a 21st Century Leaders Award, Rising Researcher Award, and Outstanding Undergraduate Achievements Award. He is interested in using wearable sensing, human-computer interaction and machine learning to advancing healthcare.
I am a first-year Ph.D. student working with Jennifer Mankoff and Anind K. Dey in the Information School at the University of Washington – Seattle. Prior to joining UW, I obtained my Bachelor’s degrees in Industrial Engineering (major) and Computer Science (minor) from Tsinghua University in 2018. While at Tsinghua, he received Best Paper Honorable Mentioned Award (CHI 2018), Person of the Year Award and Outstanding Undergraduate Awards. His research focuses on two aspects in the intersection of human-computer interaction, ubiquitous computing and machine learning: 1) the modeling of human behavior such as routine behavior and 2) novel interaction techniques.
The absence of tactile cues such as keys and buttons makes touchscreens difficult to navigate for people with visual impairments. Increasing tactile feedback and tangible interaction on touchscreens can improve their accessibility. However, prior solutions have either required hardware customization or provided limited functionality with static overlays. In addition, the investigation of tactile solutions for large touchscreens may not address the challenges on mobile devices. We therefore present Interactiles, a low-cost, portable, and unpowered system that enhances tactile interaction on Android touchscreen phones. Interactiles consists of 3D-printed hardware interfaces and software that maps interaction with that hardware to manipulation of a mobile app. The system is compatible with the built-in screen reader without requiring modification of existing mobile apps. We describe the design and implementation of Interactiles, and we evaluate its improvement in task performance and the user experience it enables with people who are blind or have low vision.
XiaoyiZhang, TracyTran, YuqianSun, IanCulhane, ShobhitJain, JamesFogarty, JenniferMankoff:Interactiles: 3D Printed Tactile Interfaces to Enhance Mobile Touchscreen Accessibility. ASSETS 2018: To Appear[PDF]
Web user interfaces today leverage many common GUI design patterns, including navigation bars and menus (hierarchical structure), tabular content presentation, and scrolling. These visual-spatial cues enhance the interaction experience of sighted users. However, the linear nature of screen translation tools currently available to blind users make it difficult to understand or navigate these structures. We introduce Spatial Region Interaction Techniques (SPRITEs) for nonvisual access: a novel method for navigating two-dimensional structures using the keyboard surface. SPRITEs 1) preserve spatial layout, 2) enable bimanual interaction, and 3) improve the end user experience. We used a series of design probes to explore different methods for keyboard surface interaction. Our evaluation of SPRITEs shows that three times as many participants were able to complete spatial tasks with SPRITEs than with their preferred current technology.
With the increasing popularity of consumer-grade 3D printing, many people are creating, and even more using, objects shared on sites such as Thingiverse. However, our formative study of 962 Thingiverse models shows a lack of re-use of models, perhaps due to the advanced skills needed for 3D modeling. An end user program perspective on 3D modeling is needed. Our framework (PARTs) empowers amateur modelers to graphically specify design intent through geometry. PARTs includes a GUI, scripting API and exemplar library of assertions which test design expectations and integrators which act on intent to create geometry. PARTs lets modelers integrate advanced, model specific functionality into designs, so that they can be re-used and extended, without programming. In two workshops, we show that PARTs helps to create 3D printable models, and modify existing models more easily than with a standard tool.
Perry-Hill, J., Shi, P., Mankoff, J. & Ashbrook, D. Understanding Volunteer AT Fabricators: Opportunities and Challenges in DIY-AT for Others in e-NABLE. Accepted to CHI 2017
We present the results of a study of e-NABLE, a distributed, collaborative volunteer effort to design and fabricate upper-limb assistive technology devices for limb-different users. Informed by interviews with 14 stakeholders in e-NABLE, including volunteers and clinicians, we discuss differences and synergies among each group with respect to motivations, skills, and perceptions of risks inherent in the project. We found that both groups are motivated to be involved in e-NABLE by the ability to use their skills to help others, and that their skill sets are complementary, but that their different perceptions of risk may result in uneven outcomes or missed expectations for end users. We offer four opportunities for design and technology to enhance the stakeholders’ abilities to work together.
A variety of 3D-printed upper-limb assistive technology devices designed and produced by volunteers in the e-NABLE community. Photos were taken by the fourth author in the e-NABLE lab on RIT’s campus.
Anhong Guo, Jeeeun Kim, Xiang ‘Anthony’ Chen, Tom Yeh, Scott E. Hudson, Jennifer Mankoff, & Jeffrey P. Bigham, Facade: Auto-generating Tactile Interfaces to Appliances, In Proceedings of the 35th Annual ACM Conference on Human Factors in Computing Systems (CHI’17), Denver, CO (To appear)
Common appliances have shifted toward flat interface panels, making them inaccessible to blind people. Although blind people can label appliances with Braille stickers, doing so generally requires sighted assistance to identify the original functions and apply the labels. We introduce Facade – a crowdsourced fabrication pipeline to help blind people independently make physical interfaces accessible by adding a 3D printed augmentation of tactile buttons overlaying the original panel. Facade users capture a photo of the appliance with a readily available fiducial marker (a dollar bill) for recovering size information. This image is sent to multiple crowd workers, who work in parallel to quickly label and describe elements of the interface. Facade then generates a 3D model for a layer of tactile and pressable buttons that fits over the original controls. Finally, a home 3D printer or commercial service fabricates the layer, which is then aligned and attached to the interface by the blind person. We demonstrate the viability of Facade in a study with 11 blind participants.
Quantifying Aversion to Costly Typing Errors in Expert Mobile Text Entry
Text entry is an increasingly important activity for mobile device users. As a result, increasing text entry speed of expert typists is an important design goal for physical and soft keyboards. Mathematical models that predict text entry speed can help with keyboard design and optimization. Making typing errors when entering text is inevitable. However, current models do not consider how typists themselves reduce the risk of making typing errors (and lower error frequency) by typing more slowly. We demonstrate that users respond to costly typing errors by reducing their typing speed to minimize typing errors. We present a model that estimates the effects of risk aversion to errors on typing speed. We estimate the magnitude of this speed change, and show that disregarding the adjustments to typing speed that expert typists use to reduce typing errors leads to overly optimistic estimates of maximum errorless expert typing speeds.
Nikola Banovic, Varun Rao, Abinaya Saravanan, Anind K. Dey, and Jennifer Mankoff. 2017. Quantifying Aversion to Costly Typing Errors in Expert Mobile Text Entry. (To appear) In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (CHI ’17). ACM, New York, NY, USA.