A k-Nearest Neighbor Based Algorithm for Human Arm Movements Recognition Using EMG Signals
In a human–robot interface, the prediction of motion, which is based on context information of a task, has the potential to improve the robustness and reliability of motion classification to control human-assisting manipulators. The electromyography (EMG) signals can be used as a control source...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
College of Engineering, University of Basrah
2010-12-01
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| Series: | Iraqi Journal for Electrical and Electronic Engineering |
| Online Access: | http://ijeee.org/volums/volume6/IJEEE6PDF/Paper627.pdf |
| Summary: | In a human–robot interface, the prediction of
motion, which is based on context information of a task,
has the potential to improve the robustness and reliability
of motion classification to control human-assisting
manipulators. The electromyography (EMG) signals can
be used as a control source of artificial arm after it has
been processed. The objective of this work is to achieve
better classification with multiple parameters using KNearest
Neighbor for different movements of a prosthetic
arm. A K- Nearest Neighbor (K-NN) rule is one of the
simplest and the most important methods in pattern
recognition. The proposed structure is simulated using
MATLAB Ver. R2009a, and satisfied results are obtained
by comparing with conventional method of recognition
using Artificial Neural Network(ANN), that explains the
ability of the proposed structure to recognize the
movements of human arm based EMG signals. Results
show the proposed technique achieved a uniformly good
performance with respect to ANN in term of time which is
important in recognition systems, better accuracy in
recognition when applied to lower SNR signal . |
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| ISSN: | 1814-5892 2078-6069 |