Detecting Loneliness

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.

Doryab, Afsaneh, et al. “Identifying Behavioral Phenotypes of Loneliness and Social Isolation with Passive Sensing: Statistical Analysis, Data Mining and Machine Learning of Smartphone and Fitbit Data.” JMIR mHealth and uHealth 7.7 (2019): e13209.

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.

News: Smartphones and Fitbits can spot loneliness in its tracks, Science 101

Uncertainty in Measurement

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.

 

 

Helping Hands

Prosthetic limbs and assistive technology (AT) require customization and modification over time to effectively meet the needs of end users. Yet, this process is typically costly and, as a result, abandonment rates are very high. Rapid prototyping technologies such as 3D printing have begun to alleviate this issue by making it possible to inexpensively, and iteratively create general AT designs and prosthetics. However for effective use, technology must be applied using design methods that support physical rapid prototyping and can accommodate the unique needs of a specific user. While most research has focused on the tools for creating fitted assistive devices, we focus on the requirements of a design process that engages the user and designer in the rapid iterative prototyping of prosthetic devices.

We present a case study of three participants with upper-limb amputations working with researchers to design prosthetic devices for specific tasks. Kevin wanted to play the cello, Ellen wanted to ride a hand-cycle (a bicycle for people with lower limb mobility impairments), and Bret wanted to use a table knife. Our goal was to identify requirements for a design process that can engage the assistive technology user in rapidly prototyping assistive devices that fill needs not easily met by traditional assistive technology. Our study made use of 3D printing and other playful and practical prototyping materials. We discuss materials that support on-the-spot design and iteration, dimensions along which in-person iteration is most important (such as length and angle) and the value of a supportive social network for users who prototype their own assistive technology. From these findings we argue for the importance of extensions in supporting modularity, community engagement, and relatable prototyping materials in the iterative design of prosthetics

Prosthetic limbs and assistive technology (AT) require customization and modification over time to effectively meet the needs of end users. Yet, this process is typically costly and, as a result, abandonment rates are very high. Rapid prototyping technologies such as 3D printing have begun to alleviate this issue by making it possible to inexpensively, and iteratively create general AT designs and prosthetics. However for effective use, technology must be applied using design methods that support physical rapid prototyping and can accommodate the unique needs of a specific user. While most research has focused on the tools for creating fitted assistive devices, we focus on the requirements of a design process that engages the user and designer in the rapid iterative prototyping of prosthetic devices.

We present a case study of three participants with upper-limb amputations working with researchers to design prosthetic devices for specific tasks. Kevin wanted to play the cello, Ellen wanted to ride a hand-cycle (a bicycle for people with lower limb mobility impairments), and Bret wanted to use a table knife. Our goal was to identify requirements for a design process that can engage the assistive technology user in rapidly prototyping assistive devices that fill needs not easily met by traditional assistive technology. Our study made use of 3D printing and other playful and practical prototyping materials. We discuss materials that support on-the-spot design and iteration, dimensions along which in-person iteration is most important (such as length and angle) and the value of a supportive social network for users who prototype their own assistive technology. From these findings we argue for the importance of extensions in supporting modularity, community engagement, and relatable prototyping materials in the iterative design of prosthetics

Photos

Project Files

https://www.thingiverse.com/thing:2365703

Project Publications

Helping Hands: Requirements for a Prototyping Methodology for Upper-limb Prosthetics Users

Reference:

Megan Kelly Hofmann, Jeffery Harris, Scott E Hudson, Jennifer Mankoff. 2016.Helping Hands: Requirements for a Prototyping Methodology for Upper-limb Prosthetics Users. InProceedings of the 34th Annual ACM Conference on Human Factors in Computing Systems (CHI ’16). ACM, New York, NY, USA, 525-534.

Making Connections: Modular 3D Printing for Designing Assistive Attachments to Prosthetic Devices

Reference:

Megan Kelly Hofmann. 2015. Making Connections: Modular 3D Printing for Designing Assistive Attachments to Prosthetic Devices. In Proceedings of the 17th International ACM SIGACCESS Conference on Computers & Accessibility (ASSETS ’15). ACM, New York, NY, USA, 353-354. DOI=http://dx.doi.org/10.1145/2700648.2811323

Supporting Navigation in the Wild for the Blind

uncovering_thumbnailSighted individuals often develop significant knowledge about their environment through what they can visually observe. In contrast, individuals who are visually impaired mostly acquire such knowledge about their environment through information that is explicitly related to them. Our work examines the practices that visually impaired individuals use to learn about their environments and the associated challenges. In the first of our two studies, we uncover four types of information needed to master and navigate the environment. We detail how individuals’ context impacts their ability to learn this information, and outline requirements for independent spatial learning. In a second study, we explore how individuals learn about places and activities in their environment. Our findings show that users not only learn information to satisfy their immediate needs, but also to enable future opportunities – something existing technologies do not fully support. From these findings, we discuss future research and design opportunities to assist the visually impaired in independent spatial learning.

Uncovering information needs for independent spatial learning for users who are visually impaired. Nikola Banovic, Rachel L. Franz, Khai N. Truong, Jennifer Mankoff, and Anind K. DeyIn Proceedings of the 15th international ACM SIGACCESS conference on Computers and accessibility (ASSETS ’13). ACM, New York, NY, USA, Article 24, 8 pages. (pdf)

StepGreen

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 productsStepgreen  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) (pdfvideo 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)