(A) Diagram of the workflow for the dog feedback experiment. Image acquisition and pose estimation were controlled by the DLC-Live! GUI. A DeepLabCut-live Processor was used to detect ‘rearing’ postures based on the likelihood and y-coordinate of the withers and elbows and turn an LED on or off accordingly. (B) An example jump sequence with LED status, labeled with keypoints measured offline using the DeepLabCut-live benchmarking tool. The images are cropped for visibility. (C) Example trajectories of the withers and elbows, locked to one jump sequence. Larger, transparent points represent the true trajectories – trajectories measured offline, from each image in the video. The smaller opaque points represent trajectories measured in real-time, in which the time of each point reflects the delay from image acquisition to pose estimation, with and without the Kalman filter forward prediction. Without forward prediction, estimated trajectories are somewhat delayed from the true trajectories. With the Kalman filter forward prediction, trajectories are less accurate but less delayed when keypoints exhibit rapid changes in position, such as during a rearing movement. (D) The delay from the time the dog first exhibited a rearing posture (from postures measured offline) to the time the LED was turned on or off. Each point represents a single instance of the detection of a transition to a rearing posture or out of a rearing posture.