Towards retrieving force feedback in robotic-assisted surgery: a supervised neuro-recurrent-vision approach

Robotic-assisted minimally invasive surgeries have gained a lot of popularity over conventional procedures as they offer many benefits to both surgeons and patients. Nonetheless, they still suffer from some limitations that affect their outcome. One of them is the lack of force feedback which restricts the surgeon's sense of touch and might reduce precision during a procedure. To overcome this limitation, we propose a novel force estimation approach that combines a vision based solution with supervised learning to estimate the applied force and provide the surgeon with a suitable representation of it. The proposed solution starts with extracting the geometry of motion of the heart's surface by minimizing an energy functional to recover its 3D deformable structure. A deep network, based on a LSTM-RNN architecture, is then used to learn the relationship between the extracted visual-geometric information and the applied force, and to find accurate mapping between the two. Our proposed force estimation solution avoids the drawbacks usually associated with force sensing devices, such as biocompatibility and integration issues. We evaluate our approach on phantom and realistic tissues in which we report an average root-mean square error of 0.02 N.Peer ReviewedPostprint (author's final draft

When Kernel Methods meet Feature Learning: Log-Covariance Network for Action Recognition from Skeletal Data

Human action recognition from skeletal data is a hot research topic and important in many open domain applications of computer vision, thanks to recently introduced 3D sensors. In the literature, naive methods simply transfer off-the-shelf techniques from video to the skeletal representation. However, the current state-of-the-art is contended between to different paradigms: kernel-based methods and feature learning with (recurrent) neural networks. Both approaches show strong performances, yet they exhibit heavy, but complementary, drawbacks. Motivated by this fact, our work aims at combining together the best of the two paradigms, by proposing an approach where a shallow network is fed with a covariance representation. Our intuition is that, as long as the dynamics is effectively modeled, there is no need for the classification network to be deep nor recurrent in order to score favorably. We validate this hypothesis in a broad experimental analysis over 6 publicly available datasets.Comment: 2017 IEEE Computer Vision and Pattern Recognition (CVPR) Workshop