@inproceedings{903105e17fc847ec89cbb361319eeb3d,
title = "Arm motion reconstruction via feature clustering in joint angle space",
abstract = "We hypothesize that a set of movemes can be used to reconstruct bio mechanically realistic movements. Using parameters from a reaching and grasping task we create a representative three-dimensional motion. From this motion we extract features from the joint angle space. We believe that the physiological importance of these features makes them worth investigating as possible movemes. Machine learning techniques are employed to cluster similar features. The clusters are then used to recursively reconstruct the motion trajectory. Even with only twenty clusters, the average trajectory reconstruction error in Cartesian space is less than 1% of the dynamic range of motion. Our ability to create and analyze realistic motions may be crucial to both future BMI experiments where a desired signal is not available and our understanding of motor control.",
author = "Jack DiGiovanna and Sanchez, {Justin C.} and Fregly, {B. J.} and Principe, {Jose C.}",
note = "Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; International Joint Conference on Neural Networks 2006, IJCNN '06 ; Conference date: 16-07-2006 Through 21-07-2006",
year = "2006",
doi = "10.1109/ijcnn.2006.247120",
language = "English (US)",
isbn = "0780394909",
series = "IEEE International Conference on Neural Networks - Conference Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4678--4683",
booktitle = "International Joint Conference on Neural Networks 2006, IJCNN '06",
}