Extracting heart rate from videos of online participants

Thomas Muender, Matthew K. Miller, Max V. Birk, Regan L. Mandryk

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

5 Citations (Scopus)

Abstract

Crowdsourcing experiments online allows for low-cost data gathering with large participant pools; however, collecting data online does not give researchers access to certain metrics. For example, physiological measures such as heart rate (HR) can provide high-resolution data about the physical, emotional, and mental state of the participant. We investigate and characterize the feasibility of gathering HR from videos of online participants engaged in single user and social tasks. We show that room lighting, head motion, and network bandwidth influence measurement quality, but that instructing participants in good practices substantially improves measurement quality. Our work takes a step towards online physiological data collection.
Original languageUndefined
Title of host publicationCHI '16 : Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems
PublisherAssociation for Computing Machinery, Inc
Pages4562-4567
Number of pages6
ISBN (Print)978-1-4503-3362-7
DOIs
Publication statusPublished - 2016
Externally publishedYes

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