Captivates: A Smart Eyeglass Platform for Across-Context Physiological Measurement
Name
3478079.pdf
Description
Published version
Size
22.66 MB
Format
Adobe PDF
Checksum (MD5)
ae3addf29f6e94c81c2fce9d1f0605eb
Author(s)
Chwalek, Patrick
Ramsay, David
Paradiso, Joseph A
Date Issued
2021
Journal
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Publisher
Association for Computing Machinery (ACM)
Citation
Chwalek, Patrick, Ramsay, David and Paradiso, Joseph A. 2021. "Captivates: A Smart Eyeglass Platform for Across-Context Physiological Measurement." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 5 (3).
Version
Final published version
Abstract
We present Captivates, an open-source smartglasses system designed for long-term, in-the-wild psychophysiological monitoring at scale. Captivates integrate many underutilized physiological sensors in a streamlined package, including temple and nose temperature measurement, blink detection, head motion tracking, activity classification, 3D localization, and head pose estimation. Captivates were designed with an emphasis on: (1) manufacturing and scalability, so we can easily support large scale user studies for ourselves and offer the platform as a generalized tool for ambulatory psychophysiology research; (2) robustness and battery life, so long-term studies result in trustworthy data individual's entire day in natural environments without supervision or recharge; and (3) aesthetics and comfort, so people can wear them in their normal daily contexts without self-consciousness or changes in behavior.
Captivates are intended to enable large scale data collection without altering user behavior. We validate that our sensors capture useful data robustly for a small set of beta testers. We also show that our additional effort on aesthetics was imperative to meet our goals; namely, earlier versions of our prototype make people uncomfortable to interact naturally in public, and our additional design and miniaturization effort has made a significant impact in preserving natural behavior. There is tremendous promise in translating psychophysiological laboratory techniques into real-world insight. Captivates serve as an open-source bridge to this end. Paired with an accurate underlying model, Captivates will be able to quantify the long-term psychological impact of our design decisions and provide real-time feedback for technologists interested in actuating a cognitively adaptive, user-aligned future.
Captivates are intended to enable large scale data collection without altering user behavior. We validate that our sensors capture useful data robustly for a small set of beta testers. We also show that our additional effort on aesthetics was imperative to meet our goals; namely, earlier versions of our prototype make people uncomfortable to interact naturally in public, and our additional design and miniaturization effort has made a significant impact in preserving natural behavior. There is tremendous promise in translating psychophysiological laboratory techniques into real-world insight. Captivates serve as an open-source bridge to this end. Paired with an accurate underlying model, Captivates will be able to quantify the long-term psychological impact of our design decisions and provide real-time feedback for technologists interested in actuating a cognitively adaptive, user-aligned future.
MIT Department
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3478079
