A Novel, Automated, and Real-Time Method for the Analysis of Non-Human Primate Behavioral Patterns Using a Depth Image Sensor  
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1 Robotics R&D Department, Korea Institute of Industrial Technology, Ansan 15588, Korea; spartan2 [EMAIL_ADDRESS]  
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2 National Primate Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Cheongju 28116, Korea; [EMAIL_ADDRESS] (K.K.); [EMAIL_ADDRESS] (S.-H.P.); [EMAIL_ADDRESS] (W.S.C.)  
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4 Skinetic Lab Inc., Seongnam 13487, Korea; [EMAIL_ADDRESS] (Y.K.); [EMAIL_ADDRESS] (M.H.)  
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4 Skinetic Lab Inc., Seongnam 13487, Korea; [EMAIL_ADDRESS] (Y.K.); [EMAIL_ADDRESS] (M.H.)  
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2 National Primate Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Cheongju 28116, Korea; [EMAIL_ADDRESS] (K.K.); [EMAIL_ADDRESS] (S.-H.P.); [EMAIL_ADDRESS] (W.S.C.)  
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2 National Primate Research Center, Korea Research Institute of Bioscience and Biotechnology, Cheongju 28116, Korea; [EMAIL_ADDRESS] (K.K.); [EMAIL_ADDRESS] (S.-H.P.); [EMAIL_ADDRESS] (W.S.C.)  
[PERSON]  
2 National Primate Research Center, Korea Research Institute of Bioscience and Biotechnology, Cheongju 28116, Korea; [EMAIL_ADDRESS] (K.-K.); [EMAIL_ADDRESS] (S.-H.P.); [EMAIL_ADDRESS] (W.S.C.)  
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limitations of conventional methods [16; 17; 18; 19]. There is a commercially available marker-based motion capture system with a very high accuracy; however, it is not applicable to non-human primates. Flexible skin prevents the tight, correct attachment of markers, and, even if attached, long and dense fur makes it difficult for the machine to detect markers [20]. Animals can also freely move their hands to remove markers attached to their skin as a result of their instinctive curiosity. To address these challenges, advanced markerless research methods have been developed. Markerless measurement methods based on deep neural networks (DNNs), which are calculation algorithms made by simple units consisting of one layer and then stacked in series to form deep networks, have been developed [21; 22; 23; 24; 25]. The generated skeleton model and multiple cameras were used to measure the movement of freely moving monkeys without using markers; in a recent study, motion capture was performed in a separate monkey studio with 62 cameras [9; 20]. These methods construct 3D images by overlapping 2D images captured by multiple cameras, and then measure behaviors by applying this to a pre-made skeleton model. These methods are limited in that researchers have to intervene during measurements to minimize the free joint movement of monkeys and to avoid one body part covering another. Research intervention as such affects reproducibility of the measurement. In addition, multiple cameras are required for multiple angles, and dissimilar to a home cage, a specific measurement cage is also required, incurring a high cost.  
The aim of this study was to develop an automated behavioral pattern analysis program for a non-human primate in a home cage based on a single depth image camera. From the depth images, it is possible to obtain spatial information about the orthogonal direction of the 2-D object plane, which is a very important factor in measuring the behavior of monkeys moving in three dimensions. We designed the program to automatically measure the monkey's behavior by tracking and calculating the center of the object and height information obtained from the depth images. There are three types of measurable behaviors: sitting, standing, and pacing. The duration of each behavior was measured using the automated program. To verify the reliability of the program, we measured the behaviors of a total of nine monkeys and compared the results with the manual measurements of two observers. In this method, a single depth image camera was used, and measurements were performed in individual home cages in which the monkeys originally resided.