Knowledge Commons of Institute of Automation,CAS
Recognition of Endovascular Manipulations using Recurrent Neural Networks | |
Li, Rui-Qi1,2![]() ![]() ![]() ![]() ![]() | |
2019 | |
会议名称 | 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) |
会议日期 | 7.23-7.27 |
会议地点 | 德国 |
摘要 | The ability to accurately recognize elementary surgical gestures is a stepping stone to automated surgical assessment and surgical training. In this paper, a long short-term memory (LSTM) recurrent neural network is applied to the task of recognizing six typical manipulations in percutaneous coronary intervention (PCI). The manipulation mentioned above is referring to the atomic surgical operation, also called surgeme in many research. Instead of using the video data or kinematic data of surgical instruments, we propose to use the kinematic data of the operator's hand acquired by our wearable data glove to recognize the manipulations. To establish a baseline for comparison, a method based on Hidden Markov Model (HMM) is applied because HMM is frequently used in the tasks of surgical sequence learning. Two cross-validation schemes are used in our experiments, they both illustrate that our LSTM-based |
收录类别 | EI |
语种 | 英语 |
七大方向——子方向分类 | 机器人感知与决策 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/46619 |
专题 | 复杂系统认知与决策实验室_先进机器人 |
通讯作者 | Hou, Zeng-Guang |
作者单位 | 1.State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. 2.University of Chinese Academy of Sciences, Beijing 100049, China. 3.CAS Center for Excellence in Brain Science and Intelligence Technology, Beijing 100190, 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 | Li, Rui-Qi,Zhou, Xiao-Hu,Bian, Gui-Bin,et al. Recognition of Endovascular Manipulations using Recurrent Neural Networks[C],2019. |
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Recognition of Endov(990KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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