Motion Tracker: Camera-Based Monitoring of Bodily Movements Using Motion Silhouettes
Motion Tracker: Camera-Based Monitoring of Bodily Movements Using Motion Silhouettes
复制标题
运动跟踪器：使用运动轮廓基于摄像头的身体运动监控
作者：
J. K. Westlund;S. D’Mello;A. Olney
认知和情感科学的研究人员研究思想和感觉是如何在身体反应系统中反映出来的，包括外围生理学、面部特征和身体运动。沿着这条研究路线的一个具体问题是认知和影响是如何在一般身体运动的动态中表现出来的。廉价、非侵入性、便携、可扩展和易于校准的运动跟踪系统可以加速这一领域的进展。为此，本文提出并验证了运动跟踪器，这是一个简单而有效的软件程序，它使用已建立的计算机视觉技术来估计一个人从从事任务的人的视频中移动的数量（可从http://jakory.com/motion-tracker/下载）。该系统可与任何商用相机和现有视频一起工作，从而提供廉价、非侵入性、潜在的便携式和可扩展的身体运动估计。在完成一项32分钟的计算机化任务（研究1）时，在安装了压力传感器的椅子上（r = 0.720）和背部（r = 0.695）记录下的运动与运动追踪器估计的身体运动之间存在很强的相关性（r = 0.720）。在受试者内部，座位（r = 0.606）和背部（r = 0.507）的相互关系也很强。在研究2中，运动追踪器的运动估计与手腕上佩戴的加速度计记录的运动之间的受试者之间的相关性也很强（rs = .801,）。679，和。681)，而人们做三个简短的动作（例如，挥手）。最后，在研究3中，当运动追踪器的估计与一个人坐在桌子旁时用Kinect追踪的头部运动相关时，受试者内部的相互关系很高（r = .855）。讨论了该系统的最佳实践建议、限制和计划扩展。
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.
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