ICRA 2012 Paper Abstract

Close

Paper WeC210.5

LEE, HanJin (KIST), Kim, Keehoon (Korea Institute of Science and Technology), Park, Myoung Soo (Korea Institute of Science and Technology), Park, Jong Hyeon (Hanyang University), Oh, Sang-Rok (KIST)

Verification of a Fast Training Algorithm for Multi-Channel Semg Signal Classification Systems to Decode Human Hand Configuration

Scheduled for presentation during the Interactive Session "Interactive Session WeC-2" (WeC210), Wednesday, May 16, 2012, 15:00−15:30, Ballroom D

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on October 19, 2017

Keywords Cognitive Human-Robot Interaction

Abstract

In this study, we evaluated a fast training algorithm to decode human hand configuration from sEMG signals on the forearms of five subjects. Eight skin surface electrodes were placed on the forearm of each subject to detect the sEMG signals corresponding to four different hand configurations and relax state. The preamplifier, which has 100 - 10000 times amplification gain and a 15 - 500 Hz bandpass filter, was designed to amplify the signals and eliminate noise. In order to enhance the performance of the classifier, feature extraction using class information was developed. The randomly assigned non-update learning method guarantees high speed classifier learning. The algorithm has been verfied by experiments with five subjects.

 

 

Technical Content © IEEE Robotics & Automation Society

This site is protected by copyright and trademark laws under US and International law.
All rights reserved. © 2002-2017 PaperCept, Inc.
Page generated 2017-10-19  00:08:01 PST  Terms of use