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A recurrent convolutional neural network approach for sensorless force estimation in robotic surgery

Author
Marbán, A.; Srinivasan, V.; Samek, W.; Fernandez, J.; Casals, A.
Type of activity
Journal article
Journal
Biomedical signal processing and control
Date of publication
2019-04
Volume
50
First page
134
Last page
150
DOI
https://doi.org/10.1016/j.bspc.2019.01.011 Open in new window
Project funding
Distributed control strategies and human-robot cooperation in health care settings
Repository
http://hdl.handle.net/2117/129232 Open in new window
URL
https://www.sciencedirect.com/science/article/abs/pii/S1746809419300114 Open in new window
Abstract
Providing force feedback as relevant information in current Robot-Assisted Minimally Invasive Surgery systems constitutes a technological challenge due to the constraints imposed by the surgical environment. In this context, force estimation techniques represent a potential solution, enabling to sense the interaction forces between the surgical instruments and soft-tissues. Specifically, if visual feedback is available for observing soft-tissues’ deformation, this feedback can be used to estim...
Citation
Marbán, A. [et al.]. A recurrent convolutional neural network approach for sensorless force estimation in robotic surgery. "Biomedical signal processing and control", Abril 2019, vol. 50, p. 134-150.
Keywords
Convolutional neural networks, Force estimation, LSTM networks, Robotic surgery
Group of research
CREB - Biomedical Engineering Research Centre
GRINS - Intelligent Robots and Systems

Participants