首页|Deep learning-based activity recognition and fine motor identification using 2D skeletons of cynomolgus monkeys

Deep learning-based activity recognition and fine motor identification using 2D skeletons of cynomolgus monkeys

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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.

Action recognitionFine motor identificationTwo-stream deep model2D skeletonNon-human primatesRett syndrome

Chuxi Li、Zifan Xiao、Yerong Li、Zhinan Chen、Xun Ji、Yiqun Liu、Shufei Feng、Zhen Zhang、Kaiming Zhang、Jianfeng Feng、Trevor W.Robbins、Shisheng Xiong、Yongchang Chen、Xiao Xiao

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School of Information Science and Technology Micro Nano System Center,Fudan University,Shanghai 200433,China

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

Kuang Yarning Honors School,Nanjing University,Nanjing,Jiangsu 210023,China

Shanghai Key Laboratory of Intelligent Information Processing,School of Computer Science,Fudan University,Shanghai 200433,China

State Key Laboratory of Primate Biomedical Research

Institute of Primate Translational Medicine,Kunming University of Science and Technology,Kunming,Yunnan 650500,China

New Vision World LLC.,Aliso Viejo,California 92656,USA

Behavioural and Clinical Neuroscience Institute,University of Cambridge,Cambridge,CB2 1TN,UK

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National Key R&D Program of ChinaNational Key R&D Program of ChinaNational Key R&D Program of ChinaNational Defense Science and Technology Innovation Special Zone Spark ProjectNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaInnovative Research Team of Highlevel Local Universities in ShanghaiScience and Technology Committee Rising-Star Program111 ProjectShanghai Municipal Science and Technology Major ProjectShanghai Center for Brain Science and Brain-Inspired Technology

2021ZD02028052019YFA07095042021ZD020090020-163-00-TS-009-152-0131900719U20A202278212500819QA1401400B180152018SHZDZX01

2023

10.24272/j.issn.2095-8137.2022.449

动物学研究
中国科学院昆明动物研究所 中国动物学会

动物学研究

CSTPCDCSCD
影响因子：0.582
ISSN：0254-5853
年,卷(期)：2023.44(5)
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