Motion Tracker: Camera-Based Monitoring of Bodily Movements Using Motion Silhouettes
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Westlund-2015-Motion Tracker_ Came.pdf
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3.61 MB
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Author(s)
D’Mello, Sidney K.
Olney, Andrew M.
Kory Westlund, Jacqueline Marie
Date Issued
June 2015
Journal
PLOS ONE
Publisher
Public Library of Science
Citation
Westlund, 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.
Version
Final published version
Abstract
Researchers 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.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
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DOI of Published Version
https://doi.org/10.1371/journal.pone.0130293
