TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Baulig, Gerald
A1  - Gulde, Thomas
A1  - Curio, Cristóbal
ED  - Leal-Teixé, Laura
T1  - Adapting egocentric visual hand pose estimation towards a robot-controlled exoskeleton
T2  - Computer Vision – ECCV 2018 Workshops : Munich, Germany, September 8-14, 2018, proceedings. Part 4.- (Lecture notes in computer science ; 11134)
N2  - The basic idea behind a wearable robotic grasp assistancesystem is to support people that suffer from severe motor impairments in daily activities. Such a system needs to act mostly autonomously and according to the user’s intent. Vision-based hand pose estimation could be an integral part of a larger control and assistance framework. In this paper we evaluate the performance of egocentric monocular hand pose estimation for a robot-controlled hand exoskeleton in a simulation. For hand pose estimation we adopt a Convolutional Neural Network (CNN). We train and evaluate this network with computer graphics, created by our own data generator. In order to guide further design decisions we focus in our experiments on two egocentric camera viewpoints tested on synthetic data with the help of a 3D-scanned hand model, with and without an exoskeleton attached to it.We observe that hand pose estimation with a wrist-mounted camera performs more accurate than with a head-mounted camera in the context of our simulation. Further, a grasp assistance system attached to the hand alters visual appearance and can improve hand pose estimation. Our experiment provides useful insights for the integration of sensors into a context sensitive analysis framework for intelligent assistance.
Y1  - 2018
SN  - 978-3-030-11024-6
SB  - 978-3-030-11024-6
U6  - https://doi.org/10.1007/978-3-030-11024-6_16
DO  - https://doi.org/10.1007/978-3-030-11024-6_16
SP  - 241
EP  - 256
S1  - 16
PB  - Springer
CY  - Cham
ER  -