by Keyword: Microprocessors
Aviles, A. I., Alsaleh, S. M., Sobrevilla, P., Casals, A., (2015). Force-feedback sensory substitution using supervised recurrent learning for robotic-assisted surgery Engineering in Medicine and Biology Society (EMBC) 
37th Annual International Conference of the IEEE , IEEE (Milan, Italy) , 1-4
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.
JTD Keywords: Computer architecture, Estimation, Force, Microprocessors, Robot sensing systems, Surgery
