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by Keyword: Three-dimensional displays


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Aviles, A. I., Alsaleh, S., Montseny, E., Sobrevilla, P., Casals, A., (2016). A Deep-Neuro-Fuzzy approach for estimating the interaction forces in Robotic surgery FUZZ-IEEE IEEE International Conference on Fuzzy Systems , IEEE (Vancouver, Canada ) , 1113-1119

Fuzzy theory was motivated by the need to create human-like solutions that allow representing vagueness and uncertainty that exist in the real-world. These capabilities have been recently further enhanced by deep learning since it allows converting complex relation between data into knowledge. In this paper, we present a novel Deep-Neuro-Fuzzy strategy for unsupervised estimation of the interaction forces in Robotic Assisted Minimally Invasive scenarios. In our approach, the capability of Neuro-Fuzzy systems for handling visual uncertainty, as well as the inherent imprecision of real physical problems, is reinforced by the advantages provided by Deep Learning methods. Experiments conducted in a realistic setting have demonstrated the superior performance of the proposed approach over existing alternatives. More precisely, our method increased the accuracy of the force estimation and compared favorably to existing state of the art approaches, offering a percentage of improvement that ranges from about 35% to 85%.

Keywords: Estimation, Force, Machine learning, Robots, Three-dimensional displays, Uncertainty, Visualization


Aviles, A. I., Alsaleh, S., Sobrevilla, P., Casals, A., (2015). Sensorless force estimation using a neuro-vision-based approach for robotic-assisted surgery NER 2015 7th International IEEE/EMBS Conference on Neural Engineering , IEEE (Montpellier, France) , 86-89

This paper addresses the issue of lack of force feedback in robotic-assisted minimally invasive surgeries. Force is an important measure for surgeons in order to prevent intra-operative complications and tissue damage. Thus, an innovative neuro-vision based force estimation approach is proposed. Tissue surface displacement is first measured via minimization of an energy functional. A neuro approach is then used to establish a geometric-visual relation and estimate the applied force. The proposed approach eliminates the need of add-on sensors, carrying out biocompatibility studies and is applicable to tissues of any shape. Moreover, we provided an improvement from 15.14% to 56.16% over other approaches which demonstrate the potential of our proposal.

Keywords: Estimation, Force, Minimally invasive surgery, Robot sensing systems, Three-dimensional displays