Extracting heart rate from videos of online participants

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

doi:10.1145/2858036.2858064

Extracting heart rate from videos of online participants

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

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

9 Citaten (Scopus)

Samenvatting

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.

Originele taal-2	Niet gedefinieerd
Titel	CHI '16 : Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems
Uitgeverij	Association for Computing Machinery, Inc.
Pagina's	4562-4567
Aantal pagina's	6
ISBN van geprinte versie	978-1-4503-3362-7
DOI's	https://doi.org/10.1145/2858036.2858064
Status	Gepubliceerd - 2016
Extern gepubliceerd	Ja

Toegang tot document

10.1145/2858036.2858064

Citeer dit

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title = "Extracting heart rate from videos of online participants",

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.",

author = "Thomas Muender and Miller, {Matthew K.} and Birk, {Max V.} and Mandryk, {Regan L.}",

year = "2016",

doi = "10.1145/2858036.2858064",

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isbn = "978-1-4503-3362-7",

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booktitle = "CHI '16 : Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems",

publisher = "Association for Computing Machinery, Inc.",

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Extracting heart rate from videos of online participants. / Muender, Thomas; Miller, Matthew K.; Birk, Max V. et al.
CHI '16 : Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, Inc., 2016. blz. 4562-4567.

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

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