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

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

Details

Category

Contributed papers (Oral)

Theme

08. Biomechanics and Robotics

Sessions

08:30 - 10:00 | Wed 26 Aug | Brown 1 | 8.1

Computer-Assisted Surgery

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.

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