Please use this identifier to cite or link to this item: https://ir.swu.ac.th/jspui/handle/123456789/13075
ชื่อเรื่อง: A performance of modern gesture control device with application in pattern classification
ผู้แต่ง: Sueaseenak D.
Khawdee C.
Pakornsirikul N.
Sukjamsri C.
Keywords: Biomedical signal processing
Electromyography
Extraction
Feature extraction
Pattern recognition
Robotics
Absolute values
Feature extraction methods
MYO armband
Performance testing
Power density spectrum
Quantitative parameters
Scatter diagrams
Total harmonic distortion (THD)
Signal to noise ratio
วันที่เผยแพร่: 2017
บทคัดย่อ: This research aimed to propose the performance testing of a modern gesture control device called MYO armband with an application in feature extraction based on multi-channels EMG. The EMG signal was collected from the forearm muscles during 6 gestures including hand close, hand open, hand flexor, double tap, and normal hand position. In this research, we applied the well-known feature extraction method called mean absolute value (MAV). The EMG features were represented in scatter diagrams to explain their behaviors. The well-known quantitative parameters used to evaluate the performance of EMG feature included davies-bouldin criterion (DB index) and scattering criterion. To present the quality of EMG signal, the signal to noise ratio (SNR), total harmonic distortion (THD), and power density spectrum (PSD) were used. The results showed that the EMG signal quality and EMG features extracted by MYO armband was robust and effective since the quantitative parameters were higher than the conventional EMG measurement system. The result was promising. © 2017 IEEE.
URI: https://ir.swu.ac.th/jspui/handle/123456789/13075
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85022342358&doi=10.1109%2fICCAR.2017.7942732&partnerID=40&md5=b6189da2a9c40df5807fd4ae5610c948
Appears in Collections:Scopus 1983-2021

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