Force-feedback sensory substitution using supervised recurrent learning for robotic-assisted surgery

Avilés Rivero, Angélica; Alsaleh, Samar M.; Sobrevilla Frisón, Pilar; Casals Gelpi, Alicia

doi:10.1109/EMBC.2015.7318246

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Avilés Rivero, Angélica

Alsaleh, Samar M.

Sobrevilla Frisón, Pilar

Casals Gelpi, Alicia

Document typeConference report

Defense date2015

PublisherInstitute of Electrical and Electronics Engineers (IEEE)

Rights accessRestricted access - publisher's policy

Abstract

The lack of force feedback is considered one of the major limitations in Robot Assisted Minimally Invasive Surgeries. Since add-on sensors are not a practical solution for clinical environments, in this paper we present a force estimation approach that starts with the reconstruction of a 3D deformation structure of the tissue surface by minimizing an energy functional. A Recurrent Neural Network-Long Short Term Memory (RNN-LSTM) based architecture is then presented to accurately estimate the applied forces. According to the results, our solution offers long-term stability and shows a significant percentage of accuracy improvement, ranging from about 54% to 78%, over existing approaches.

CitationAvilés, A., Alsaleh, S., Sobrevilla, P., Casals, A. Force-feedback sensory substitution using supervised recurrent learning for robotic-assisted surgery. A: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. "2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2015)". Milan: Institute of Electrical and Electronics Engineers (IEEE), 2015, p. 1-4.

URIhttp://hdl.handle.net/2117/85399

DOI10.1109/EMBC.2015.7318246

ISBN9781424492695

Publisher versionhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7318246

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