Body Motion Segmentation in Non-Human Primate Based on Gaussian Process Hidden Semi-Markov Model

Abstract

Understanding the nature of nonverbal communication (eye contacts, face expressions, body postures, hand gestures, body motions, etc...) is one of the core issue in behavioral neuroscience. In this study, we demonstrated the data-driven dynamical segmentation of the body expressions in free moving small non-human primate, common marmoset. We developed a new marker-less 3D motion tracking system optimized to marmoset. Then, we proposed unsupervised segmentation using a Gaussian process-hidden semi-Markov model (GP-HSMM). As a result, we succeeded to classify three types of marmoset feeding behavior (high position feeding, low position feeding, and low position feeding with hands) only based on body parts positions, face direction, and body angle information. This result suggested that proposing system could represent high versatility to quantify the animal nonverbal body expressions without qualitative teacher labels.