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Force-feedback sensory substitution using supervised recurrent learning for robotic-assisted surgery
| dc.contributor.author | Avilés Rivero, Angélica |
| dc.contributor.author | Alsaleh, Samar M. |
| dc.contributor.author | Sobrevilla Frisón, Pilar |
| dc.contributor.author | Casals Gelpi, Alicia |
| dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Matemàtiques |
| dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial |
| dc.date.accessioned | 2016-04-08T09:40:02Z |
| dc.date.issued | 2015 |
| dc.identifier.citation | Avilés, 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. |
| dc.identifier.isbn | 9781424492695 |
| dc.identifier.uri | http://hdl.handle.net/2117/85399 |
| dc.description.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. |
| dc.format.extent | 4 p. |
| dc.language.iso | eng |
| dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| dc.subject | Àrees temàtiques de la UPC::Informàtica::Robòtica |
| dc.subject.lcsh | Biomechanics |
| dc.subject.lcsh | Robotics in medicine |
| dc.subject.other | Surgical robotics |
| dc.subject.other | Vision based force estimation |
| dc.title | Force-feedback sensory substitution using supervised recurrent learning for robotic-assisted surgery |
| dc.type | Conference report |
| dc.subject.lemac | Biomecànica |
| dc.subject.lemac | Robòtica en medicina |
| dc.contributor.group | Universitat Politècnica de Catalunya. GRINS - Grup de Recerca en Robòtica Intel·ligent i Sistemes |
| dc.identifier.doi | 10.1109/EMBC.2015.7318246 |
| dc.description.peerreviewed | Peer Reviewed |
| dc.relation.publisherversion | http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7318246 |
| dc.rights.access | Restricted access - publisher's policy |
| drac.iddocument | 17567001 |
| dc.description.version | Postprint (published version) |
| dc.date.lift | 10000-01-01 |
| upcommons.citation.author | Avilés, A., Alsaleh, S., Sobrevilla, P., Casals, A. |
| upcommons.citation.contributor | Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
| upcommons.citation.pubplace | Milan |
| upcommons.citation.published | true |
| upcommons.citation.publicationName | 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2015) |
| upcommons.citation.startingPage | 1 |
| upcommons.citation.endingPage | 4 |