@inproceedings{ea77e2e0fa5841fb94bfbde0ea0a3417,
title = "A Novel Intraoperative Force Estimation Method via Electrical Bioimpedance Sensing",
abstract = "During minimally invasive robotic surgery (MIRS), intraoperative force sensing on the surgical tool plays an important role to mitigate the risk of inadvertent tissue damage. This study proposes a novel method for estimating the force exerted by the surgical tool tip based on the electrical bioimpedance (EBI) of the contacting tissue. When the surgical tool is pressing on the tissue, the tissue is deformed corresponding to the exerted force. Meanwhile, the measured EBI value changes accordingly since tissue deformations change the contact area between the tool and the tissue. An ex-vivo experimental study was performed and different machine learning methods were tested for correlating the exerted force and the EBI values. The experimental results show that a Feed-forward Multi-layer Neural Network (F-MNN) can provide good results in terms of efficiency and accuracy. The exerted force on the tissue can be accurately estimated with a median error of 0.072 N and with about 7 ms testing time. In addition, the proposed method has significant advantages over other techniques since it requires little hardware modification and allows fast and seamless integration with the existing surgical robotic system.",
keywords = "Force sensing, electrical bioimpedance, robot-assisted surgery",
author = "Zhuoqi Cheng and Mattos, {Leonardo S.}",
year = "2023",
doi = "10.1007/978-3-031-32606-6_25",
language = "English",
isbn = "978-3-031-32605-9",
series = "Mechanisms and Machine Science",
publisher = "Springer",
pages = "213–220",
editor = "Tadej Petri{\v c} and Ude, { Ale{\v s}} and Leon {\v Z}lajpah",
booktitle = "Advances in Service and Industrial Robotics",
address = "Germany",
note = "32nd International Conference on Robotics in Alpe-Adria-Danube Region, RAAD ; Conference date: 14-06-2023 Through 16-06-2023",
}