Presentation

Force-Feedback Sensory Substitution Using Supervised Recurrent Learning for Robotic-Assisted Surgery

Alicia Casals • Angelica Ivone Aviles • Pilar Sobrevilla • Samar Alsaleh

08:30 - 08:45 | Wednesday 26 August 2015 | Brown 1

Summary

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.