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Learning Skill Characteristics From Manipulations

文献类型：期刊论文

作者Zhou, Xiao-Hu2; Xie, Xiao-Liang2; Liu, Shi-Qi2; Ni, Zhen-Liang1; Zhou, Yan-Jie1; Li, Rui-Qi1; Gui, Mei-Jiang1; Fan, Chen-Chen1; Feng, Zhen-Qiu2; Bian, Gui-Bin2
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2022-03-25
页码15
ISSN号2162-237X
关键词Surgery Sensors In vivo Task analysis Arteries Measurement Sensor phenomena and characterization Ensemble learning in vivo porcine studies percutaneous coronary intervention skill characteristics wavelet packet decomposition (WPD)
DOI10.1109/TNNLS.2022.3160159
通讯作者Zhou, Xiao-Hu(xiaohu.zhou@ia.ac.cn) ; Xie, Xiao-Liang(xiaoliang.xie@ia.ac.cn)
英文摘要Percutaneous coronary intervention (PCI) has increasingly become the main treatment for coronary artery disease. The procedure requires high experienced skills and dexterous manipulations. However, there are few techniques to model PCI skill so far. In this study, a learning framework with local and ensemble learning is proposed to learn skill characteristics of different skill-level subjects from their PCI manipulations. Ten interventional cardiologists (four experts and six novices) were recruited to deliver a medical guidewire to two target arteries on a porcine model for in vivo studies. Simultaneously, translation and twist manipulations of thumb, forefinger, and wrist are acquired with electromagnetic (EM) and fiber-optic bend (FOB) sensors, respectively. These behavior data are then processed with wavelet packet decomposition (WPD) under 1-10 levels for feature extraction. The feature vectors are further fed into three candidate individual classifiers in the local learning layer. Furthermore, the local learning results from different manipulation behaviors are fused in the ensemble learning layer with three rule-based ensemble learning algorithms. In subject-dependent skill characteristics learning, the ensemble learning can achieve 100% accuracy, significantly outperforming the best local result (90%). Furthermore, ensemble learning can also maintain 73% accuracy in subject-independent schemes. These promising results demonstrate the great potential of the proposed method to facilitate skill learning in surgical robotics and skill assessment in clinical practice.
WOS关键词FEATURE-EXTRACTION ; NEURAL-NETWORK ; SURGERY ; SIMULATOR ; FORCE ; GLOVE
资助项目National Key Research and Development Program of China[2019YFB1311700] ; National Natural Science Foundation of China[62003343] ; National Natural Science Foundation of China[62073325] ; National Natural Science Foundation of China[U1913601] ; National Natural Science Foundation of China[61720106012] ; National Natural Science Foundation of China[U20A20224] ; National Natural Science Foundation of China[U1913210] ; Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS)[2020140] ; Strategic Priority Research Program of CAS[XDB32040000]
WOS研究方向Computer Science ; Engineering
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000777301700001
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China ; Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS) ; Strategic Priority Research Program of CAS
源URL[http://ir.ia.ac.cn/handle/173211/48183]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队
通讯作者Zhou, Xiao-Hu; Xie, Xiao-Liang
作者单位1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
3.Macau Univ Sci & Technol, Inst Syst Engn, MUST CASIA Joint Lab Intelligence Sci & Technol, Taipa, Macau, Peoples R China
4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
5.CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Zhou, Xiao-Hu,Xie, Xiao-Liang,Liu, Shi-Qi,et al. Learning Skill Characteristics From Manipulations[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2022:15.
APA Zhou, Xiao-Hu.,Xie, Xiao-Liang.,Liu, Shi-Qi.,Ni, Zhen-Liang.,Zhou, Yan-Jie.,...&Hou, Zeng-Guang.(2022).Learning Skill Characteristics From Manipulations.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,15.
MLA Zhou, Xiao-Hu,et al."Learning Skill Characteristics From Manipulations".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022):15.

入库方式： OAI收割

来源：自动化研究所

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