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This article is presented by:
SOUMYA RANJAN MOHAPATRA
AE&I
ELECTRO MYOGRAPHY
Agenda
Introduction
Electromyography (EMG) Signals
EMG Signal Processing
Classification
Experimental Results
Implementation
Conclusion
Future Work
Questions
Introduction
Numerous technological advances in prosthetic hands
Greater degrees of freedom
Continue to function as pincers
The purpose of this thesis was to implement a program that could perform real-time feature extraction and classification of prehensile EMG signals for the following grasps:
Electromyography (Emg) Signals: Myoelectric Energy Detection
Motor units control groups of muscle fibers
Brain recruits motor units to innervate muscles for movement
Myoelectric energy produced as motor units activate
Surface electrodes detect myoelectric energy
Electromyography (EMG) Signals: EMG Amplification
SENIAM Recommends
Pre-gelled Ag/Ag-Cl
Bipolar
0.8” inter-electrode distance
0.4” wide
Surface electrodes
Pre-gelled Ag/Ag-Cl
1” inter-electrode distance
0.875” wide
EMG amplification device
(Saksit Siriprayoonsak, 2005)
4 bipolar channels
1 reference channel
