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dc.contributor.authorD’Mello, Sidney K.
dc.contributor.authorOlney, Andrew M.
dc.contributor.authorKory Westlund, Jacqueline Marie
dc.date.accessioned2015-08-21T12:45:23Z
dc.date.available2015-08-21T12:45:23Z
dc.date.issued2015-06
dc.date.submitted2014-04
dc.identifier.issn1932-6203
dc.identifier.urihttp://hdl.handle.net/1721.1/98177
dc.description.abstractResearchers in the cognitive and affective sciences investigate how thoughts and feelings are reflected in the bodily response systems including peripheral physiology, facial features, and body movements. One specific question along this line of research is how cognition and affect are manifested in the dynamics of general body movements. Progress in this area can be accelerated by inexpensive, non-intrusive, portable, scalable, and easy to calibrate movement tracking systems. Towards this end, this paper presents and validates Motion Tracker, a simple yet effective software program that uses established computer vision techniques to estimate the amount a person moves from a video of the person engaged in a task (available for download from http://jakory.com/motion-tracker/). The system works with any commercially available camera and with existing videos, thereby affording inexpensive, non-intrusive, and potentially portable and scalable estimation of body movement. Strong between-subject correlations were obtained between Motion Tracker’s estimates of movement and body movements recorded from the seat (r =.720) and back (r = .695 for participants with higher back movement) of a chair affixed with pressure-sensors while completing a 32-minute computerized task (Study 1). Within-subject cross-correlations were also strong for both the seat (r =.606) and back (r = .507). In Study 2, between-subject correlations between Motion Tracker’s movement estimates and movements recorded from an accelerometer worn on the wrist were also strong (rs = .801, .679, and .681) while people performed three brief actions (e.g., waving). Finally, in Study 3 the within-subject cross-correlation was high (r = .855) when Motion Tracker’s estimates were correlated with the movement of a person’s head as tracked with a Kinect while the person was seated at a desk (Study 3). Best-practice recommendations, limitations, and planned extensions of the system are discussed.en_US
dc.language.isoen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.isversionofhttp://dx.doi.org/10.1371/journal.pone.0130293en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.sourcePublic Library of Scienceen_US
dc.titleMotion Tracker: Camera-Based Monitoring of Bodily Movements Using Motion Silhouettesen_US
dc.typeArticleen_US
dc.identifier.citationWestlund, Jacqueline Kory, Sidney K. D’Mello, and Andrew M. Olney. “Motion Tracker: Camera-Based Monitoring of Bodily Movements Using Motion Silhouettes.” Edited by Philip Allen. PLoS ONE 10, no. 6 (June 18, 2015): e0130293.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Media Laboratoryen_US
dc.contributor.departmentProgram in Media Arts and Sciences (Massachusetts Institute of Technology)en_US
dc.contributor.mitauthorWestlund, Jacqueline Koryen_US
dc.relation.journalPLOS ONEen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsWestlund, Jacqueline Kory; D’Mello, Sidney K.; Olney, Andrew M.en_US
dc.identifier.orcidhttps://orcid.org/0000-0002-0418-4674
mit.licensePUBLISHER_CCen_US
mit.metadata.statusComplete


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