@inproceedings{7d9cb92d20534181b6f29eb74df86784,
title = "Computer vision with Microsoft Kinect for control of functional electrical stimulation: ANN classification of the grasping intentions",
abstract = "We present a method for recognizing intended grasp type based on data from the Microsoft Kinect. A computer vision algorithm estimates the vertical and the transversal distance of the hand from the center of the object and the hand orientation from the Kinect depth images. Based on this set of features in the reaching phase of grasp artificial neural network recognizes the intended grasp type. This is demonstrated with an example of a coffee cup on a working desk. Trained neural network classified the grasp with accuracy above 85\%. By adding this feature to the existing computer vision system for control of the functional electrical stimulation assisted grasping we facilitate the compliance between the applied electrical stimulation and the user intentions.",
keywords = "Computer vision, FES, Microsoft Kinect, neural networks, rehabilitation",
author = "{\v S}trbac, \{Matija D.\} and Popovi{\'c}, \{Dejan B.\}",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 12th Symposium on Neural Network Applications in Electrical Engineering, NEUREL 2014 ; Conference date: 25-11-2014 Through 27-11-2014",
year = "2014",
month = jan,
day = "15",
doi = "10.1109/NEUREL.2014.7011491",
language = "English",
series = "12th Symposium on Neural Network Applications in Electrical Engineering, NEUREL 2014 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "153--156",
editor = "Branimir Reljin and Srdan Stankovic",
booktitle = "12th Symposium on Neural Network Applications in Electrical Engineering, NEUREL 2014 - Proceedings",
address = "United States",
}