Título:
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Force-feedback sensory substitution using supervised recurrent learning for robotic-assisted surgery
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Autor/a:
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Avilés Rivero, Angélica; Alsaleh, Samar M.; Sobrevilla Frisón, Pilar; Casals Gelpi, Alicia
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Otros autores:
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Universitat Politècnica de Catalunya. Departament de Matemàtiques; Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial; Universitat Politècnica de Catalunya. GRINS - Grup de Recerca en Robòtica Intel·ligent i Sistemes |
Abstract:
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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. |
Abstract:
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Peer Reviewed |
Materia(s):
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-Àrees temàtiques de la UPC::Informàtica::Robòtica -Biomechanics -Robotics in medicine -Surgical robotics -Vision based force estimation -Biomecànica -Robòtica en medicina |
Derechos:
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Tipo de documento:
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Artículo - Versión publicada Objeto de conferencia |
Editor:
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Institute of Electrical and Electronics Engineers (IEEE)
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Compartir:
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