Leveraging Routine Behavior and Contextually-Filtered Features for Depression Detection among College Students

The rate of depression in college students is rising, which is known to increase suicide risk, lower academic performance and double the likelihood of dropping out. Researchers have used passive mobile sensing technology to assess mental health. Existing work on finding relationships between mobile sensing and depression, as well as identifying depression via sensing features, mainly utilize single data channels or simply concatenate multiple channels. There is an opportunity to identify better features by reasoning about co-occurrence across multiple sensing channels. We present a new method to extract contextually filtered features on passively collected, time-series data from mobile devices via rule mining algorithms. We first employ association rule mining algorithms on two different user groups (e.g., depression vs. non-depression). We then introduce a new metric to select a subset of rules that identifies distinguishing behavior patterns between the two groups. Finally, we consider co-occurrence across the features that comprise the rules in a feature extraction stage to obtain contextually filtered features with which to train classifiers. Our results reveal that the best model with these features significantly outperforms a standard model that uses unimodal features by an average of 9.7% across a variety of metrics. We further verified the generalizability of our approach on a second dataset, and achieved very similar results.

Leveraging Routine Behavior and Contextually-Filtered Features for Depression Detection among College Students. Xuhai Xu, Prerna Chikersal, Afsaneh Doryab, Daniella Villaalba, Janine M. Dutcher, Michael J. Tumminia, Tim Althoff, Sheldon Cohen, Kasey Creswell, David Creswell, Jennifer Mankoff and Anind K. Dey. IMWUT, Article No 116. 10.1145/3351274

A pipeline starting with data collection (including from mobile phone sensors, campus map, and fitbit) which feeds into feature extraction. This is piped into association rule mining, and features plus rules are combined to create contextually filtered features, which are then piped into a machine learning classifier. Ground truth comes from the BDI-II questionnaire.
The high-level pipeline of the integration of rule mining algorithms and machine learning models. The dashed frame highlights the novel contribution of the paper. We designed a new metric to select the top rules from the rule set generated by ARM. We also proposed a new approach to extract contextually filtered features based on the top rules. Finally, we use these features to train classifiers.

Passively-sensed Behavioral Correlates of Discrimination Events in College Students

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 more time in bed on the day of the event. Some of these patterns continue the next day.

Passively-sensed Behavioral Correlates of Discrimination Events in College Students. Yasaman S. Sefidgar, Woosuk Seo, Kevin S. Kuehn, Tim Althoff, Anne Browning, Eve Ann Riskin, Paula S. Nurius, Anind K Dey, Jennifer Mankoff. CSCW 2019, To Appear

A bar plot sorted by number of reports, with about 100 reports of unfair treatment based on national origin, 90 based on intelligence, 70 based on gender, 60 based on apperance, 50 on age, 45 on sexual orientation, 35 on major, 30 on weight, 30 on height, 20 on income, 10 on disability, 10 on religion, and 10 on learning
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 sensor data collected by day in 5 categories: Activity, screen, locations, fitbit, and calls.
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.
A diagram showing compliance in surveys, organized by nweek of study. One line shows compliance in the large surveys given at pre, mid and post, which drops from 99% to 94% to 84%. The other line shows average weekly compliance in EMAs, which goes up in the second week to 93% but then drops slowly (with some variability) to 89%
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 day
Barplot showing significance of morning screen use, calls, minutes asleep, time in bed, range of activities, number of steps, anxiety, depression, and frustration on the day before, of, and after unfair treatment. All but minutes asleep are significant at p=.05 or below on the day of discrimination, but this drops off after.
Patterns 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.

Point-of-Care Manufacturing: Maker Perspectives on Digital Fabrication in Medical Practice

A venn diagram showing the domains of expertise of those we interviewed including people from hospitals, universities, non-profits, va networks, private practices, and government. We interviewed clinicians and facilitators in each of these domains and there was a great deal of overlap with participants falling into multiple categories. For example, one participant was in a VA network and in private practice, while another was at a university and also a non-profit.

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.

“Point-of-Care Manufacturing”: Maker Perspectives onDigital Fabrication in Medical Practice. Udaya Lakshmi, Megan Hofmann, Stephanie Valencia, Lauren Wilcox, Jennifer Mankoff and Rosa Arriaga. CSCW 2019. To Appear.

A venn diagram showing the domains of expertise of those we interviewed including people from hospitals, universities, non-profits, va networks, private practices, and government. We interviewed clinicians and facilitators in each of these domains and there was a great deal of overlap with participants falling into multiple categories. For example, one participant was in a VA network and in private practice, while another was at a university and also a non-profit.

Designing in the Public Square

A Makapo paddler in a one-person outrigger canoe (OC1) with the final version of CoOP attached.

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).
Shows 5 iterations of the CoOP system, each of which is progressively less bulky, and more integrated (the first is strapped on for example and the last is more integrated).
The design evolution of the CoOP system in order of iteration from left to right.

“Occupational Therapy is Making”: Clinical Rapid Prototyping and Digital Fabrication

Splint that has been 3D printed in a material of an appropriate skin color and fit to a client's hand.

Lyme Disease’s Impact

An ongoing, and very personal thread of research that our group engages in (due to my own journey with Lyme Disease, which I occasionally blog about here) is research into the impacts of Lyme Disease and opportunities for helping to support patients with Lyme Disease. From a patient perspective, Lyme disease is as tough to deal with as many other more well known conditions [1].

Lyme disease can be difficult to navigate because of the disagreements about its diagnosis and the disease process. In addition, it is woefully underfunded and understudied, given that the CDC estimates around 300,000 new cases occur per year (similar to the rate of breast cancer) [2].

Bar chart showing that Lyme disease is woefully under studied.

As an HCI researcher, I started out trying to understand the relationship that Lyme Disease patients have with digital technologies. For example, we studied the impact of conflicting information online on patients [3] and how patients self-mediate the accessibility of online content [4]. It is my hope to eventually begin exploring technologies that can improve quality of life as well.

However, one thing patients need right away is peer reviewed evidence about the impact that Lyme disease has on patients (e.g. [3]) and the value of treatment for patients (e.g. [4]). Here, as a technologist, the opportunity is to work with big data (thousands of patient reports) to unpack trends and model outcomes in new ways. That research is still in the formative stages, but in our most recent publication [4] we use straightforward subgroup analysis to demonstrate that treatment effectiveness is not adequately captured simply by looking at averages.

This chart shows that there is a large subgroup (about a third) of respondents to our survey who reported positive response to treatment, even though the average response was not positive.

There are many opportunities and much need for further data analysis here, including documenting the impact of differences such as gender on treatment (and access to treatment), developing interventions that can help patients to track symptoms, manage interaction within and between doctors, and navigate accessibility and access issues.

[1] Johnson, L., Wilcox, S., Mankoff, J., & Stricker, R. B. (2014). Severity of chronic Lyme disease compared to other chronic conditions: a quality of life survey. PeerJ2, e322.

[2] Johnson, L., Shapiro, M. & Mankoff, J. Removing the mask of average treatment effects in chronic Lyme Disease research using big data and subgroup analysis.

[3] Mankoff, J., Kuksenok, K., Kiesler, S., Rode, J. A., & Waldman, K. (2011, May). Competing online viewpoints and models of chronic illness. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 589-598). ACM.

[4] Kuksenok, K., Brooks, M., & Mankoff, J. (2013, April). Accessible online content creation by end users. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 59-68). ACM.

 

3D Printed Wireless Analytics

Picture of a 3D printed arm with backscatter sensing technology attached to it.

Wireless Analytics for 3D Printed Objects: Vikram Iyer, Justin Chan, Ian Culhane, Jennifer Mankoff, Shyam Gollakota UIST, Oct. 2018 [PDF]

We created a wireless physical analytics system works with commonly available conductive plastic filaments. Our design can enable various data capture and wireless physical analytics capabilities for 3D printed objects, without the need for electronics.

We make three key contributions:

(1) We demonstrate room scale backscatter communication and sensing using conductive plastic filaments.

(2) We introduce the first backscatter designs that detect a variety of bi-directional motions and support linear and rotational movements. An example is shown below

(3) As shown in the image below, we enable data capture and storage for later retrieval when outside the range of the wireless coverage, using a ratchet and gear system.

We validate our approach by wirelessly detecting the opening and closing of a pill bottle, capturing the joint angles of a 3D printed e-NABLE prosthetic hand, and an insulin pen that can store information to track its use outside the range of a wireless receiver.

Selected Media

6 of the most amazing things that were 3D-printed in 2018 (Erin Winick, MIT Technology Review, 12/24/2018)

Researchers develop 3D printed objects that can track and store how they are used (Sarah McQuate), UW Press release. 10/9/2018

Assistive Objects Can Track Their Own Use (Elizabeth Montalbano), Design News. 11/14/2018

People

Students

Vikram Iyer
Justin Chan
Ian Culhane

Faculty

Jennifer Mankoff
Shyam Gollakota

Contact: printedanalytics@cs.washington.edu

Interactiles

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]

Figure 2. Floating windows created for number pad (left), scrollbar (right) and control button (right bottom). The windows can be transparent; we use colors for demonstration.

Figure 4. Average task completion times of all tasks in the study.

Nonvisual Interaction Techniques at the Keyboard Surface

Rushil Khurana,Duncan McIsaac, Elliot Lockerman,Jennifer Mankoff Nonvisual Interaction Techniques at the Keyboard Surface, CHI 2018, To Appear

A table (shown on screen). Columns are mapped to the number row of the keyboard and rows to the leftmost column of keys, and (1) By default the top left cell is selected. (2) The right hand presses the ‘2’ key, selecting the second column (3) The left hand selects the next row (4) The left hand selects the third row. In each case, the position of the cell and its content are read out aloud.

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.

Talk [Slides]:

Sample Press:

KOMO Radio | New screen reader method helps blind, low-vision users browse complex web pages

Device helps blind, low-vision users better browse web pages. Allen Cone

Graph showing task completion rates for different kinds of tasks in our user study

A user is searching a table (shown on screen) for the word ‘Jill’. Columns are mapped to the number row of the keyboard and rows to the leftmost column of keys. (1) By default the top left cell is selected. (2) The right hand presses the ‘2’ key, selecting the second column (3) The left hand selects the next row (4) The left hand selects the third row. In each case, the number of occurrences of the search query in the respective column or row are read aloud. When the query is found, the position and content of the cell are read out aloud.

The Tangible Desktop

Mark S. BaldwinGillian R. HayesOliver L. HaimsonJennifer MankoffScott 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.