CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 先进机器人
Prediction of natural guidewire rotation using an sEMG-based NARX neural network
Zhou, Xiao-Hu; Bian, Gui-Bin; Xie, Xiao-Liang; Hou, Zeng-Guang; Hao, Jian-Long
2017
Conference NameNeural Networks (IJCNN), 2017 International Joint Conference on
Conference Date2017
Conference PlaceAnchorage Alaska USA
AbstractFor the treatment of cardiovascular diseases, clinical
success of percutaneous coronary intervention is highly dependent on natural technical skills and dexterous manipulation
strategies of surgeons. However, the increasing used robotic surgical systems have been designed without considering manipulation
techniques, especially surgical behaviors and motion patterns.
This has driven research towards exploitation of natural manipulation skills in recent years. In this paper, natural guidewire
manipulations are analyzed and predicted using an sEMG-based
nonlinear autoregressive neural network with exogenous inputs.
The relationship between natural endovascular manipulation and
guidewire rotation is built through the network. Two experiments
at different rotational speed were performed to verify the effectiveness and robustness of the applied model. The experimental
results show that the average predictive root mean error of five
subjects is 15.61
at the low speed and 21.85at the high speed.
These favorable results could be of interest to improve existing
robotic surgical systems.

Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/19998
Collection复杂系统管理与控制国家重点实验室_先进机器人
Recommended Citation
GB/T 7714
Zhou, Xiao-Hu,Bian, Gui-Bin,Xie, Xiao-Liang,et al. Prediction of natural guidewire rotation using an sEMG-based NARX neural network[C],2017.
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