Fig. 8
From: Machine learning-based model for behavioural analysis in rodents applied to the forced swim test
Dataset construction. To build the dataset the video is split into 3-s sub-videos and each sequence of frames composes a single data; every frame is subtracted from the first frame of the video containing no rodent; then, the ROI (blue line) of the cylinder is wrapped into a rectangle of standard dimension (LxH). Finally, the resulting 3D-tensor of dimension TxLxH is normalized. The related portion of the video was manually evaluated by two annotators to recognize immobility, swimming, and climbing behaviours (A). The final dataset is composed of the three interest behaviours (B).
