TY - GEN
T1 - Study on physiological tremor recognition algorithm in the vascular interventional surgical robot
AU - Guo, Shuxiang
AU - Shen, Rui
AU - Xiao, Nan
AU - Bao, Xianqiang
AU - Yang, Cheng
AU - Cui, Jinxin
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/5
Y1 - 2018/10/5
N2 - With the development of surgical robotic technology, more and more requirements on safety property were raised upon the surgical robots. For master-slave control surgical robots, it's physiological hand tremor that influences the accuracy and success rate of the robot-assisted surgery. Focusing on the physiological tremor recognition and cancelling, this paper proposes a moving-window-least-square-support-vector-machine-based recognition algorithm and adaptive filter method to rectify the wrong operations caused by physiological tremor. The performance assessment was shown with the indicator of accuracy, which implies that the MWLSSVMAF reduces accuracy error of the tremor signal. The comparison between recognition results and surface electromyographic signal is conducted for assessing the classification accuracy rate. Also, some experiments on correction effects are carried out. The results indicate that the method proposed by our research possesses better classifying accuracy rate of 83% and that the secure property requests of vascular interventional surgical robot have been improved obviously.
AB - With the development of surgical robotic technology, more and more requirements on safety property were raised upon the surgical robots. For master-slave control surgical robots, it's physiological hand tremor that influences the accuracy and success rate of the robot-assisted surgery. Focusing on the physiological tremor recognition and cancelling, this paper proposes a moving-window-least-square-support-vector-machine-based recognition algorithm and adaptive filter method to rectify the wrong operations caused by physiological tremor. The performance assessment was shown with the indicator of accuracy, which implies that the MWLSSVMAF reduces accuracy error of the tremor signal. The comparison between recognition results and surface electromyographic signal is conducted for assessing the classification accuracy rate. Also, some experiments on correction effects are carried out. The results indicate that the method proposed by our research possesses better classifying accuracy rate of 83% and that the secure property requests of vascular interventional surgical robot have been improved obviously.
KW - Least Square Support Vector Machine (LS-SVM)
KW - Mass-spring-damping Model
KW - Physiological Tremor
KW - Tremor Recognition Algorithm
KW - Vascular Intervention Surgical Robot (VISR)
UR - http://www.scopus.com/inward/record.url?scp=85056332522&partnerID=8YFLogxK
U2 - 10.1109/ICMA.2018.8484573
DO - 10.1109/ICMA.2018.8484573
M3 - Conference contribution
AN - SCOPUS:85056332522
T3 - Proceedings of 2018 IEEE International Conference on Mechatronics and Automation, ICMA 2018
SP - 597
EP - 602
BT - Proceedings of 2018 IEEE International Conference on Mechatronics and Automation, ICMA 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Conference on Mechatronics and Automation, ICMA 2018
Y2 - 5 August 2018 through 8 August 2018
ER -