Finally, in Study 3 the within-subject cross-correlation was high ( r =. 681) while people performed three brief actions (e.g., waving). 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 =. Within-subject cross-correlations were also strong for both the seat ( r =.606) 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). 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 =. 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. 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 ). Progress in this area can be accelerated by inexpensive, non-intrusive, portable, scalable, and easy to calibrate movement tracking systems.
One specific question along this line of research is how cognition and affect are manifested in the dynamics of general body movements. 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.
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