title: Force-feedback sensory substitution using supervised recurrent learning for robotic-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.
authors.forename: Angelica I
authors.initials: AI
authors.lastname: Aviles
authors.forename: Samar M
authors.initials: SM
authors.lastname: Alsaleh
authors.forename: Pilar
authors.initials: P
authors.lastname: Sobrevilla
authors.forename: Alicia
authors.initials: A
authors.lastname: Casals
mesh_terms: D005246:Feedback; D006801:Humans; D016571:Neural Networks, Computer; D065287:Robotic Surgical Procedures / Q000379:methods*; D000069553:Supervised Machine Learning