Murine Motion Behavior Recognition Based on DeepLabCut and Convolutional Long Short-Term Memory Network

Abstract

Murine behavior recognition is widely used in biology, neuroscience, pharmacology, and other aspects of research, and provides a basis for judging the psychological and physiological state of mice. To solve the problem whereby traditional behavior recognition methods only model behavioral changes in mice over time or space, we propose a symmetrical algorithm that can capture spatiotemporal information based on behavioral changes. The algorithm first uses the improved DeepLabCut keypoint detection algorithm to locate the nose, left ear, right ear, and tail root of the mouse, and then uses the ConvLSTM network to extract spatiotemporal information from the keypoint feature map sequence to classify five behaviors of mice: walking straight, resting, grooming, standing upright, and turning. We developed a murine keypoint detection and behavior recognition dataset, and experiments showed that the method achieved a percentage of correct keypoints (PCK) of 87±1% at three scales and against four backgrounds, while the classification accuracy for the five kinds of behaviors reached 93±1%. The proposed method is thus accurate for keypoint detection and behavior recognition, and is a useful tool for murine motion behavior recognition.