ICRA 2012 Paper Abstract


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 July 20, 2018

Keywords Cognitive Human-Robot Interaction


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.



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