Taylor is a second-year PhD student in the Paul G Allen School of Computer Science and Engineering. She is advised by Professor Jennifer Mankoff. In 2017, she graduated from the University of California, Santa Cruz with bachelor’s degrees in Computer Engineering and Cognitive Science. She then earned her Masters in Human Computer Interaction from Rochester Institute of Technology in 2019.
Her research interests focus on trying to make fabrication more accessible for people with disabilities. Her prior research explored how to make the e-textile circuit development process more accessible for adults with intellectual disabilities. Her recent projects focus on understanding the kinds of difficulties that people with disabilities face while knitting, and developing technologies to help users overcome some of these difficulties.
Avery is a Phd Student in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. They are advised by Prof. Jennifer Mankoff. They completed their bachelors in Computer Science at the University of Illinois at Urbana-Champaign in 2019, where Prof. Aditya Parameswaran and Prof. Karrie Karahalios advised them. They are an NSF Fellow and an ARCS Scholar.
Their research focuses on applying computer science to create or improve technologies that serve people with disabilities. Their current work focuses on 1) representation of people with disabilities in digital technologies like avatars and generative AI tools, and 2) how to support people with fluctuating access needs like neurodiverse people and people with chronic or mental health conditions.
Feelings of loneliness are associated with poor physical and mental health. Detection of loneliness through passive sensing on personal devices can lead to the development of interventions aimed at decreasing rates of loneliness.
Objective: The aim of this study was to explore the potential of using passive sensing to infer levels of loneliness and to identify the corresponding behavioral patterns.
Methods: Data were collected from smartphones and Fitbits (Flex 2) of 160 college students over a semester. The participants completed the University of California, Los Angeles (UCLA) loneliness questionnaire at the beginning and end of the semester. For a classification purpose, the scores were categorized into high (questionnaire score>40) and low (≤40) levels of loneliness. Daily features were extracted from both devices to capture activity and mobility, communication and phone usage, and sleep behaviors. The features were then averaged to generate semester-level features. We used 3 analytic methods: (1) statistical analysis to provide an overview of loneliness in college students, (2) data mining using the Apriori algorithm to extract behavior patterns associated with loneliness, and (3) machine learning classification to infer the level of loneliness and the change in levels of loneliness using an ensemble of gradient boosting and logistic regression algorithms with feature selection in a leave-one-student-out cross-validation manner.
Results: The average loneliness score from the presurveys and postsurveys was above 43 (presurvey SD 9.4 and postsurvey SD 10.4), and the majority of participants fell into the high loneliness category (scores above 40) with 63.8% (102/160) in the presurvey and 58.8% (94/160) in the postsurvey. Scores greater than 1 standard deviation above the mean were observed in 12.5% (20/160) of the participants in both pre- and postsurvey scores. The majority of scores, however, fell between 1 standard deviation below and above the mean (pre=66.9% [107/160] and post=73.1% [117/160]).
Our machine learning pipeline achieved an accuracy of 80.2% in detecting the binary level of loneliness and an 88.4% accuracy in detecting change in the loneliness level. The mining of associations between classifier-selected behavioral features and loneliness indicated that compared with students with low loneliness, students with high levels of loneliness were spending less time outside of campus during evening hours on weekends and spending less time in places for social events in the evening on weekdays (support=17% and confidence=92%). The analysis also indicated that more activity and less sedentary behavior, especially in the evening, was associated with a decrease in levels of loneliness from the beginning of the semester to the end of it (support=31% and confidence=92%).
Conclusions: Passive sensing has the potential for detecting loneliness in college students and identifying the associated behavioral patterns. These findings highlight intervention opportunities through mobile technology to reduce the impact of loneliness on individuals’ health and well-being.
Improving mobile keyboard typing speed increases in value as more tasks move to a mobile setting. Autocorrect is a powerful way to reduce the time it takes to manually fix typing errors, which results in typing speed increase. However, recent user studies of autocorrect uncovered an unexplored side-effect: participants’ aversion to typing errors despite autocorrect. We present the first computational model of typing on keyboards with autocorrect, which enables precise study of expert typists’ aversion to typing errors on such keyboards. Unlike empirical typing studies that last days, our model evaluates the effects of typists’ aversion to typing errors for any autocorrect accuracy in seconds. We show that typists’ aversion to typing errors adds a self-imposed limit on upper bound typing speeds, which decreases the value of highly accurate autocorrect. Our findings motivate future designs of keyboards with autocorrect that reduce typists’ aversion to typing errors to increase typing speeds.
The Limits of Expert Text Entry Speed on Mobile Keyboards with Autocorrect Nikola Banovic, Ticha Sethapakdi, Yasasvi Hari, Anind K. Dey, Jennifer Mankoff. Mobile HCI 2019.
An example mobile device with a soft keyboard: A) text entry area, which in our study contained study progress, the current phrase to transcribe, and an area for transcribed characters, B) automatically suggested words, and C) a miniQWERTY soft keyboard with autocorrect.
Our model estimated expected mean typing speeds (lines) for different levels of typing error rate aversion (e) compared to mean empirical typing speed with automatic correction and suggestion (bar plot) in WPM across Accuracy. Error bars represent 95% confidence intervals.Median empirical error rates across Accuracy in session 3 with automated correction and suggestion. Error bars represent minimum and maximum error rate values, and dots represent outliers
Knitting creates complex, soft objects with unique and controllable texture properties that can be used to create interactive objects. However, little work addresses the challenges of using knitted textures. We present KnitPick: a pipeline for interpreting pre-existing hand-knitting texture patterns into a directed-graph representation of knittable structures (KnitGraphs) which can be output to machine and hand-knitting instructions. Using KnitPick, we contribute a measured and photographed data set of 300 knitted textures. Based on findings from this data set, we contribute two algorithms for manipulating KnitGraphs. KnitCarving shapes a graph while respecting a texture, and KnitPatching combines graphs with disparate textures while maintaining a consistent shape. Using these algorithms and textures in our data set we are able to create three Knitting based interactions: roll, tug, and slide. KnitPick is the first system to bridge the gap between hand- and machine-knitting when creating complex knitted textures.
KnitPick converts KnitSpeak into KnitGraphs which can be carved, patched and output to knitted results Data set measurement setup, including camera, scale, and stretching rigThe above images show a progression from the original Star texture to the same texture with 15 columns removed by texture carving. These photographs were shown to crowd-workers who rated their similarity. Even with a whole repetition width removed from the Stars, the pattern remains a recognizable star pattern.
A deeper understanding of how discrimination impacts psychological health and well-being of students would allow us to better protect individuals at risk and support those who encounter discrimination. While the link between discrimination and diminished psychological and physical well-being is well established, existing research largely focuses on chronic discrimination and long-term outcomes. A better understanding of the short-term behavioral correlates of discrimination events could help us to concretely quantify the experience, which in turn could support policy and intervention design. In this paper we specifically examine, for the first time, what behaviors change and in what ways in relation to discrimination. We use actively-reported and passively-measured markers of health and well-being in a sample of 209 first-year college students over the course of two academic quarters. We examine changes in indicators of psychological state in relation to reports of unfair treatment in terms of five categories of behaviors: physical activity, phone usage, social interaction, mobility, and sleep. We find that students who encounter unfair treatment become more physically active, interact more with their phone in the morning, make more calls in the evening, and spend less time in bed on the day of the event. Some of these patterns continue the next day.
Breakdown of 448 reports of unfair treatment by type. National, Orientation, and Learning refer to ancestry or national origin, sexual orientation, and learning disability respectively. See Table 3 for details of all categories. Participants were able to report multiple incidents of unfair treatment, possibly of different types, in each report. As described in the paper, we do not have data on unfair treatment based on race.A heatplot showing compliance with sensor data collection. Sensor data availability for each day of the study is shown in terms of the number of participants whose data is available on a given day. Weeks of the study are marked on the horizontal axis while different sensors appear on the vertical axis. Important calendar dates (e.g., start / end of the quarter and exam periods) are highlighted as are the weeks of daily surveys. The brighter the cells for a sensor the larger the number of people contributing data for that sensor. Event-based sensors (e.g., calls) are not as bright as sensors continuously sampled (e.g., location) as expected. There was a technical issue in the data collection application in the middle of study, visible as a dark vertical line around the beginning of April.Timeline and completion rate of pre, mid, and post questionnaires as well as EMA surveys. Y axis shows the completion rates and is narrowed to the range 50-100%. The completion rate of pre, mid, and post questionnaires are percentages of the original pool of 209 participants, whereas EMA completion rates are based on the 176 participants who completed the study. EMA completion rates are computed as the average completion rate of the surveys administered in a certain week of the study. School-related events (i.e., start and end of quarters as well as exam periods) are marked. Dark blue bars (Daily Survey) show the weeks when participants answered surveys every day, four times a dayPatterns of feature significance from the day before to two days after the discrimination event. The shortest bars represent the highest significance values (e.g., depressed and frustrated on day 0; depressed on day 1; morning screen use on day 2). There are no significant differences the day before. Most short-term relationships exist on the day of the event, a few appear on the next day (day 1). On the third day one significant difference, repeated, from the first day is observed.
Maker culture in health care is on the rise with the rapid adoption of consumer-grade fabrication technologies. However, little is known about the activity and resources involved in prototyping medical devices to improve patient care. In this paper, we characterize medical making based on a qualitative study of medical stakeholder engagement in physical prototyping (making) experiences. We examine perspectives from diverse stakeholders including clinicians, engineers, administrators, and medical researchers. Through 18 semi-structured interviews with medical-makers in US and Canada, we analyze making activity in medical settings. We find that medical-makers share strategies to address risks, define labor roles, and acquire resources by adapting traditional structures or creating new infrastructures. Our findings outline how medical-makers mitigate risks for patient safety, collaborate with local and global stakeholder networks, and overcome constraints of co-location and material practices. We recommend a clinician-aided software system, partially-open repositories, and a collaborative skill-share social network to extend their strategies in support of medical making.
Design in the Public Square: Supporting Cooperative Assistive Technology Design Through Public Mixed-Ability Collaboration (CSCW 2019)
Mark. S. Baldwin, Sen H Hirano, Jennifer Mankoff, Gillian Hayes
From the white cane to the smartphone, technology has been an effective tool for broadening blind and low vision participation in a sighted world. In the face of this increased participation, individuals with visual impairments remain on the periphery of most sight-first activities. In this paper, we describe a multi-month public-facing co-design engagement with an organization that supports blind and low vision outrigger paddling. Using a mixed-ability design team, we developed an inexpensive cooperative outrigger paddling system, called DEVICE, that shares control between sighted and visually impaired paddlers. The results suggest that public design, a DIY (do-it-yourself) stance, and attentiveness to shared physical experiences, represent key strategies for creating assistive technologies that support shared experiences.
A close-up of version three of the CoOP system mounted to the rudder assembly and the transmitter used to control the rudder (right corner).The design evolution of the CoOP system in order of iteration from left to right.
Jasper Tran O’Leary, Sara Zewde, Jennifer Mankoff , Daniela K. Rosner
CHI 2019
This paper draws on a collaborative project called the Africatown Activation to examine the role design practices play in contributing to (or conspiring against) the flourishing of the Black community in Seattle, Washington. Specifically, we describe the efforts of a community group called Africatown to design and build an installation that counters decades of disinvestment and ongoing displacement in the historically Black Central Area neighborhood. Our analysis suggests that despite efforts to include community, conventional design practices may perpetuate forms of institutional racism: enabling activities of community engagement that may further legitimate racialized forms of displacement. We discuss how focusing on amplifying the legacies of imagination already at work may help us move beyond a simple reading of design as the solution to systemic forms of oppression.
Consumer-fabrication technologies potentially improve the effectiveness and adoption of assistive technology (AT) by engaging AT users in AT creation. However, little is known about the role of clinicians in this revolution. We investigate clinical AT fabrication by working as expert fabricators for clinicians over a four-month period. We observed and co-designed AT with four occupational therapists at two clinics: a free clinic for uninsured clients, and a Veteran’s Affairs Hospital. We find that existing fabrication processes, particularly with respect to rapid prototyping, do not align with clinical practice and its \textit{do-no-harm} ethos. We recommend software solutions that would integrate into client care by: amplifying clinicians’ expertise, revealing appropriate fabrication opportunities, and supporting adaptable fabrication.
“Occupational Therapy is Making'”: Design Iteration and Digital Fabrication in Occupational Therapy