Real-time identification of electromyographic signals from hand movement

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dc.contributor.authorSappat, Assawapong
dc.contributor.authorMahaphonchaikul, Kritsanaphan
dc.contributor.authorSangworasil, Manas
dc.contributor.authorPintavirooj, Chuchat
dc.contributor.authorTuantranont, Adisorn
dc.date.accessioned2026-08-06T10:04:48Z
dc.date.available2026-08-06T10:04:48Z
dc.date.issued2012-10-02
dc.description.abstractIn this work, we have demonstrated a novel on-line technology for real-time acquisition and identification of electromyographic (EMG) signals from hand movement. EMG signal were measured using standard surface electrodes from forearm muscles at three major points including Wrist extensor, Flexor Carpi Radialis and Wrist Flexor groups, respectively. The EMG acquisition system consists of an instrumentation amplifier, filter circuit, isolator, an amplifier with gain adjustment and a commercial embedded system called FiO board. The commercial FiO embedded system is interfaced with the computer and EMG is represented, analyzed and stored in real-time on computer by Simulink program. EMG signals are identified by RMS and SD feature extraction methods and k-mean clustering algorithm. The result revealed that both RMS and SD can be used with k-mean clustering algorithm to obtain the distinct Euclidean distance characteristic of EMG signal for each movement. The minimum Euclidean distance with RMS and SD for each hand movement uniquely occurs at a distinct Euclidean distances between real EMG data and extracted features. © 2012 IEEE.
dc.identifier.citation2012 9th International Conference on Electrical Engineering Electronics Computer Telecommunications and Information Technology Ecti Con 2012, 2012
dc.identifier.doi10.1109/ECTICon.2012.6254259
dc.identifier.other2-s2.0-84866762040
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/4354
dc.source2012 9th International Conference on Electrical Engineering Electronics Computer Telecommunications and Information Technology Ecti Con 2012
dc.subjectEMG
dc.subjectfeature extraction
dc.subjecthand movement
dc.subjectidentification
dc.subjectk-mean
dc.titleReal-time identification of electromyographic signals from hand movement
dc.typeConference Paper

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