Knowledge Commons of Institute of Automation,CAS
Learning Skill Characteristics From Manipulations | |
Zhou, Xiao-Hu1; Xie, Xiao-Liang1; Liu, Shi-Qi1; Ni, Zhen-Liang2; Zhou, Yan-Jie2; Li, Rui-Qi2; Gui, Mei-Jiang2; Fan, Chen-Chen2; Feng, Zhen-Qiu1; Bian, Gui-Bin1; Hou, Zeng-Guang1,3,4,5 | |
发表期刊 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS |
ISSN | 2162-237X |
2022-03-25 | |
页码 | 15 |
通讯作者 | Zhou, Xiao-Hu(xiaohu.zhou@ia.ac.cn) ; Xie, Xiao-Liang(xiaoliang.xie@ia.ac.cn) |
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. |
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) |
DOI | 10.1109/TNNLS.2022.3160159 |
关键词[WOS] | FEATURE-EXTRACTION ; NEURAL-NETWORK ; SURGERY ; SIMULATOR ; FORCE ; GLOVE |
收录类别 | SCI |
语种 | 英语 |
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] |
项目资助者 | 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 |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000777301700001 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 智能控制 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48184 |
专题 | 复杂系统认知与决策实验室_先进机器人 |
通讯作者 | Zhou, Xiao-Hu; Xie, Xiao-Liang |
作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 5.Macau Univ Sci & Technol, Inst Syst Engn, MUST CASIA Joint Lab Intelligence Sci & Technol, Taipa, Macau, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
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. |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论