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Modeling Human Routines

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Modeling and Understanding Human Routine Behavior

Human routines are blueprints of behavior, which allow people to accomplish their purposeful repetitive tasks and activities. People express their routines through actions that they perform in the particular situations that triggered those actions. An ability to model routines and understand the situations in which they are likely to occur could allow technology to help people improve their bad habits, inexpert behavior, and other suboptimal routines. In this project we explore generalizable routine modeling approaches that encode patterns of routine behavior in ways that allow systems, such as smart agents, to classify, predict, and reason about human actions under the inherent uncertainty present in human behavior. Such technologies can have a positive effect on society by making people healthier, safer, and more efficient in their routine tasks.

Routines_Viz_Tool

Modeling and Understanding Human Routine Behavior
Nikola Banovic, Tofi Buzali, Fanny Chevalier, Jennifer Mankoff, and Anind K. Dey
In Proceedings of the 2016 ACM annual conference on Human Factors in Computing Systems(CHI ’16). ACM, New York, NY, USA.
Honorable Mention Award

Dynamic question ordering

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In recent years, surveys have been shifting online, offering the possibility for adaptive questions, where later questions depend on responses to earlier questions. We present a general framework for dynamically ordering questions, based on previous responses, to engage respondents, improving survey completion and imputation of unknown items. Our work considers two scenarios for data collection from survey-takers. In the first, we want to maximize survey completion (and the quality of necessary imputations) and so we focus on ordering questions to engage the respondent and collect hopefully all the information we seek, or at least the information that most characterizes the respondent so imputed values will be accurate. In the second scenario, our goal is to give the respondent a personalized prediction, based on information they provide. Since it is possible to give a reasonable prediction with only a subset of questions, we are not concerned with motivating the user to answer all questions. Instead, we want to order questions so that the user provides information that most reduces the uncertainty of our prediction, while not being too burdensome to answer.

Publications
Kirstin Early, Stephen E. Fienberg, Jennifer Mankoff. (2016). Test time feature ordering with FOCUS: Interactive predictions with minimal user burden. In Proceedings of 2016 ACM Conference on Pervasive and Ubiquitous Computing. Honorable Mention: Top 5% of submissions. Talk slides.

Infant Oxygen Monitoring

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Hospitalized children on continuous oxygen monitors generate >40,000 data points per patient each day. These data do not show context or reveal trends over time, techniques proven to improve comprehension and use. Management of oxygen in hospitalized patients is suboptimal—premature infants spend >40% of each day outside of evidence-based oxygen saturation ranges and weaning oxygen is delayed in infants with bronchiolitis who are physiologically ready. Data visualizations may improve user knowledge of data trends and inform better decisions in managing supplemental oxygen delivery.

First, we studied the workflows and breakdowns for nurses and respiratory therapists (RTs) in the supplemental oxygen delivery of infants with respiratory disease. Secondly, using end-user design we developed a data display that informed decision-making in this context. Our ultimate goal is to improve the overall work process using a combination of visualization and machine learning.

Visualization mockup for displaying O2 saturation over time to nurses.
Visualization mockup for displaying O2 saturation over time to nurses.

StepGreen

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The goal of the Stepgreen project is to leverage Internet scale technologies to create opportunities for reduced energy consumption. The original vision of the project was to leverage existing online social networks to encourage individual change. Since then the project has broadened to include a number of other ideas. We have explored the impact of demographics on energy use practices; studied the value of empathetic figures such as a polar bear for motivation and exploredorganizational-level planning. We have also developed mobile technologies that can provide feedback about green actions on the go.

StepGreen.org Website

StepGreen.org Website

Try StepGreen.org out: The Stepgreen.org website provides a mechanism for allowing individuals to report on and track their environmental impact. It includes a visualization that can be displayed on an individual’s social networking web page. Go to Stepgreen.organd see for yourself how we leverage social networks to engage individuals in green behaviors.

Learn about our software products. Stepgreen  is a service that we are hoping to share with non-profits that are encouraging behavior change,  such as an open API you can use to build your own clients for encouraging green behavior. Please contact us at stepgreen@cs.cmu.edu if you are interested in collaborating with us. 

Learn about our research and our publications

Keep in touch with us through our Facebook page  and Twitter account.

Sample Publications

JOURNAL PAPERS & MAGAZINE ARTICLES

  1. J. Mankoff. “HCI and Sustainability: A Tale of Two Motivations,” Interactions.
  2. Dillahunt, T. & Mankoff, J. (2011) In the dark, out in the cold. ACM Crossroads 17(4):39-41
  3. Jennifer Mankoff, Robin Kravets, Eli Blevis, Some Computer Science Issues in Creating a Sustainable World, IEEE Computer 41(8):102-105. (pdf)
    1. Reprinted as: Jennifer Mankoff, Robin Kravets and Eli Blevis, Some Computer Science Issues in Creating a Sustainable World. Posted on November 17th, 2008 in Articles, Climate, OpEd, Technology http://www.earthzine.org/2008/11/17/some-computer-science-issues-in-creating-a-sustainable-world/

CONFERENCE PAPERS

  1. Tawanna Dillahunt, Jennifer Mankoff, Eric Paulos. Understanding Conflict Between Landlords and Tenants: Implications for Energy Sensing and Feedback. Ubicomp ’10.  (full paper)(pdf)
  2. Jennifer Mankoff, Susan R. Fussell, Tawanna Dillahunt, Rachel Glaves, Catherine Grevet, Michael Johnson, Deanna Matthews, H. Scott Matthews, Robert McGuire, Robert Thompson. StepGreen.org: Increasing Energy Saving Behaviors via Social Networks. ICWSM’10.  (full paper) (pdf, video of talk)
  3. C. Grevet, J. Mankoff, S. D. Anderson Design and Evaluation of a Social Visualization aimed at Encouraging Sustainable Behavior. In Proceedings of HICSS 2010.  (full paper) (pdf)
  4. T. Dillahunt, J. Mankoff, E. Paulos, S. Fussell It’s Not All About “Green”: Energy Use in Low-Income Communities. In Proceedings of Ubicomp 2009. (Full paper) (pdf)
  5. J. Froehlich, T. Dillahunt, P. Klasnja, J. Mankoff, S. Consolvo, B. Harrison, J. A. Landay, UbiGreen: Investigating a Mobile Tool for Tracking and Supporting Green Transportation Habits. In Proceedings of CHI 2009. (Full paper) (pdf)
  6. J. Schwartz, J. Mankoff, H. Scott Matthews. Reflections of everyday activity in spending data. In Proceedings of CHI 2009.  (Note). (pdf)
  7. Jennifer Mankoff, Deanna Matthews, Susan R. Fussell and Michael Johnson. Leveraging Social Networks to Motivate Individuals to Reduce their Ecological Footprints. HICSS 2007. (pdf)

OTHER

  1. Rachael Nealer, Christopher Weber, H. Scott Matthews and Chris Hendrickson. Energy and Environmental Impacts of Consumer Purchases: A Case Study on Grocery Purchases. ISSST 2010
  2. Dillahunt, T., Becker, G., Mankoff, J. and Kraut, R. Motivating Environmentally Sustainable Behavior Changes with a Virtual Polar Bear.” Pervasive 2008 workshop on Pervasive Persuasive Technology and Environmental Sustainability. (pdf)
  3. Johnson, M., Fussell, S. Mankoff, J., Matthwes, D., and Setlock, L. “When Users Pledge to Take Green Actions, Are They Solving a Decision Problem?” INFORMS Fall 2008 Conference. (ppt)
  4. Johnson, M., Fussell, S. Mankoff, J. and Matthwes, D. “How Does Problem Representation Influence Decision Performance and Attitudes?” INFORMS Fall 2007 Conference. Abstract
  5.  Johnson, M.P. 2006. “Public Participation and Decision Support Systems: Theory, Requirements, and Applications.” For presentation at Association of Public Policy Analysis and Management Fall Conference, Madison, WI, November 3, 2006. (pdf)

Severity of Chronic Lyme Disease

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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. PeerJ, 2, e322.

The Centers for Disease Control and Prevention (CDC) health-related quality of life (HRQoL) indicators are widely used in the general population to determine the burden of disease, identify health needs, and direct public health policy. These indicators also allow the burden of illness to be compared across different diseases. Although Lyme disease has recently been acknowledged as a major health threat in the USA with more than 300,000 new cases per year, no comprehensive assessment of the health burden of this tickborne disease is available. This study assesses the HRQoL of patients with chronic Lyme disease (CLD) and compares the severity of CLD to other chronic conditions.

fig-2-1x

Exiting the cleanroom: On ecological validity and ubiquitous computing

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Carter, Scott, Jennifer Mankoff, Scott R. Klemmer, and Tara Matthews. “Exiting the cleanroom: On ecological validity and ubiquitous computing.” Human–Computer Interaction 23, no. 1 (2008): 47-99.

Over the past decade and a half, corporations and academies have invested considerable time and money in the realization of ubiquitous computing. Yet design approaches that yield ecologically valid understandings of ubiquitous computing systems, which can help designers make design decisions based on how systems perform in the context of actual experience, remain rare. The central question underlying this article is, What barriers stand in the way of real-world, ecologically valid design for ubicomp?

Using a literature survey and interviews with 28 developers, we illustrate how issues of sensing and scale cause ubicomp systems to resist iteration, prototype creation, and ecologically valid evaluation. In particular, we found that developers have difficulty creating prototypes that are both robust enough for realistic use and able to handle ambiguity and error and that they struggle to gather useful data from evaluations because critical events occur infrequently, because the level of use necessary to evaluate the system is difficult to maintain, or because the evaluation itself interferes with use of the system. We outline pitfalls for developers to avoid as well as practical solutions, and we draw on our results to outline research challenges for the future. Crucially, we do not argue for particular processes, sets of metrics, or intended outcomes, but rather we focus on prototyping tools and evaluation methods that support realistic use in realistic settings that can be selected according to the needs and goals of a particular developer or researcher.