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Completed STANDARD GRANT National Science Foundation (US)

CRII: HCC: Sustaining Cognitive Flow in Physical Making

$1.75M USD

Funder National Science Foundation (US)
Recipient Organization University of Texas At Arlington
Country United States
Start Date Jun 01, 2021
End Date Feb 29, 2024
Duration 1,003 days
Number of Grantees 1
Roles Principal Investigator
Data Source National Science Foundation (US)
Grant ID 2105054
Grant Description

Getting "in the zone," whether it be from an immersive video game, playing a sport, or knitting a sweater, is a natural response of the mind that allows people to fully concentrate and cope with difficult, tedious, and often repetitive tasks. This response, known as entering a cognitive state of flow, has many known benefits within craft and making communities, including a lessened impact of failure, a distorted sense of time, and a disappearance of self-consciousness that are often associated with the "joy of making." This project promotes the progress of science by developing a method of detecting flow states from wearable sensor technologies.

By better understanding how tasks, environments, and individual characteristics influence flow, this project will provide principles for designing and evaluating creative technologies that support novice makers in developing positive coping mechanisms. The team will also create tools and tutorials that will allow the methods to be taken up by researchers and designers in making and related domains.

Flow is a well-studied and valuable concept to interaction design, yet researchers and designers are limited to using flow as a form of qualitative assessment. By treating flow as an activity recognition problem, this project will investigate how off-the-shelf physiological and activity sensing can be used to unobtrusively recognize flow on a moment-by-moment scale.

Through documentation of users completing equivalent flow-intensive tasks, this work will develop (1) the first open-access flow activity dataset of its kind, (2) a benchmark deep learning model of flow, and (3) an open-source qualitative analysis tool that provides real-time flow characterization. This research will produce knowledge to reinforce and extend our understanding of the body's response to flow states; in conjunction, case studies will produce design exemplars to operationalize how flow can be used as a design variable to induce or sustain flow states within creative technologies.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

All Grantees

University of Texas At Arlington

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