Force-feedback sensory substitution using supervised recurrent learning for robotic-assisted surgery
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hdl:2117/85399
Document typeConference report
Defense date2015
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessRestricted access - publisher's policy
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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.
ISBN9781424492695
Publisher versionhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7318246
Collections
- Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial - Ponències/Comunicacions de congressos [1.515]
- Departament de Matemàtiques - Ponències/Comunicacions de congressos [1.096]
- GRINS - Grup de Recerca en Robòtica Intel·ligent i Sistemes - Ponències/Comunicacions de congressos [73]
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