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Li C, Xiao Z, Li Y, Chen Z, Ji X, Liu Y, Feng S, Zhang Z, Zhang K, Feng J, Robbins TW, Xiong S, Chen Y, Xiao X. Deep learning-based activity recognition and fine motor identification using 2D skeletons of cynomolgus monkeys. Zool Res 2023; 44:967-980. [PMID: 37721106 PMCID: PMC10559098 DOI: 10.24272/j.issn.2095-8137.2022.449] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 09/14/2023] [Indexed: 09/19/2023] Open
Abstract
Video-based action recognition is becoming a vital tool in clinical research and neuroscientific study for disorder detection and prediction. However, action recognition currently used in non-human primate (NHP) research relies heavily on intense manual labor and lacks standardized assessment. In this work, we established two standard benchmark datasets of NHPs in the laboratory: MonkeyinLab (MiL), which includes 13 categories of actions and postures, and MiL2D, which includes sequences of two-dimensional (2D) skeleton features. Furthermore, based on recent methodological advances in deep learning and skeleton visualization, we introduced the MonkeyMonitorKit (MonKit) toolbox for automatic action recognition, posture estimation, and identification of fine motor activity in monkeys. Using the datasets and MonKit, we evaluated the daily behaviors of wild-type cynomolgus monkeys within their home cages and experimental environments and compared these observations with the behaviors exhibited by cynomolgus monkeys possessing mutations in the MECP2 gene as a disease model of Rett syndrome (RTT). MonKit was used to assess motor function, stereotyped behaviors, and depressive phenotypes, with the outcomes compared with human manual detection. MonKit established consistent criteria for identifying behavior in NHPs with high accuracy and efficiency, thus providing a novel and comprehensive tool for assessing phenotypic behavior in monkeys.
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Affiliation(s)
- Chuxi Li
- School of Information Science and Technology Micro Nano System Center, Fudan University, Shanghai 200433, China
| | - Zifan Xiao
- Department of Anesthesiology, Huashan Hospital
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education
- Behavioral and Cognitive Neuroscience Center, Institute of Science and Technology for Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
| | - Yerong Li
- School of Information Science and Technology Micro Nano System Center, Fudan University, Shanghai 200433, China
| | - Zhinan Chen
- School of Information Science and Technology Micro Nano System Center, Fudan University, Shanghai 200433, China
| | - Xun Ji
- Kuang Yaming Honors School, Nanjing University, Nanjing, Jiangsu 210023, China
| | - Yiqun Liu
- Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai 200433, China
| | - Shufei Feng
- State Key Laboratory of Primate Biomedical Research
- Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, Yunnan 650500, China
| | - Zhen Zhang
- State Key Laboratory of Primate Biomedical Research
- Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, Yunnan 650500, China
| | - Kaiming Zhang
- New Vision World LLC., Aliso Viejo, California 92656, USA
| | - Jianfeng Feng
- Department of Anesthesiology, Huashan Hospital
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education
- Behavioral and Cognitive Neuroscience Center, Institute of Science and Technology for Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
| | - Trevor W Robbins
- Department of Anesthesiology, Huashan Hospital
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education
- Behavioral and Cognitive Neuroscience Center, Institute of Science and Technology for Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 1TN, UK
| | - Shisheng Xiong
- School of Information Science and Technology Micro Nano System Center, Fudan University, Shanghai 200433, China. E-mail:
| | - Yongchang Chen
- State Key Laboratory of Primate Biomedical Research
- Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, Yunnan 650500, China
| | - Xiao Xiao
- Department of Anesthesiology, Huashan Hospital
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education
- Behavioral and Cognitive Neuroscience Center, Institute of Science and Technology for Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China. E-mail:
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