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For each video activity, the number of segments M = 7.
More specifically, a video activity is eventually characterized by a time series of BoW features.
For each video activity, time-dependent BoW features are extracted based on the learned BoW model.
Video activity was logged to collect engagement data (coverage and commitment), and a knowledge and performance test were administered.
Since each image frame of a video activity is represented by a covariance descriptor (see Section 3.2), a most straightforward way would be to directly treat the video activity as a bag of covariance descriptors.
Instead, in our case, each video activity is treated as a temporal sequence (time series) of bags of covariance descriptors.
Similar(30)
Multiple regression on video-activity score.
Our preliminary conclusion is that our video- activity score assesses physical activity but not specific information related to hyperactivity.
We successfully demonstrated that our video- activity score assessed physical activity with r = 0.81 to the movement rating in the eyes of two independent observers.
We interpret our findings that our video- activity score assesses physical activity (see movement ratings), which is mainly driven by age.
Our video- activity score represents a first attempt to retain an activity score based on webcam footage.
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