Abstract
BackgroundThis research has established a method for using single channel surface electromyogram (sEMG) recorded from the forearm to identify individual finger flexion. The technique uses the volume conduction properties of the tissues and uses the magnitude and density of the singularities in the signal as a measure of strength of the muscle activity.MethodsSEMG was recorded from the flexor digitorum superficialis muscle during four different finger flexions. Based on the volume conduction properties of the tissues, sEMG was decomposed into wavelet maxima and grouped into four groups based on their magnitude. The mean magnitude and the density of each group were the inputs to the twin support vector machines (TSVM). The algorithm was tested on 11 able-bodied and one trans-radial amputated volunteer to determine the accuracy, sensitivity and specificity. The system was also tested to determine inter-experimental variations and variations due to difference in the electrode location.ResultsAccuracy and sensitivity of identification of finger actions from single channel sEMG signal was 93% and 94% for able-bodied and 81% and 84% for trans-radial amputated respectively, and there was only a small inter-experimental variation.ConclusionsVolume conduction properties based sEMG analysis provides a suitable basis for identifying finger flexions from single channel sEMG. The reported system requires supervised training and automatic classification.
Highlights
This research has established a method for using single channel surface electromyogram recorded from the forearm to identify individual finger flexion
This requires the classification of surface electromyogram (sEMG) signals to identify the desired finger movements and obtain the command for controlling the prosthetic hand
The results show that the average accuracy of the detection of flexion of four classes of fingers performed based on the bilateral learning was found to be 81.87 (± 13.54)% from sEMG electrode location 2 (Figure 1b)
Summary
This research has established a method for using single channel surface electromyogram (sEMG) recorded from the forearm to identify individual finger flexion. Surface electromyogram (sEMG) is the non-invasive recording of the electrical activity of the muscle. SEMG of the residual muscles becomes an obvious choice for natural control of the prosthetic hand. This requires the classification of sEMG signals to identify the desired finger movements and obtain the command for controlling the prosthetic hand. The examiner gave on-screen and oral commands to the participant to perform the action without any fixed order of the fingers.
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