EMG signal based finger movement recognition for prosthetic hand control

Abstract

Electromyography (EMG) signal can be defined as a measure of electrical activity produced by skeletal muscles. It can be used in handling electronic devices or prosthesis. If we are able recognize the hand gesture captured using EMG signal with greater reliability and classification rate, it could serve a good purpose for handling the prosthesis and to provide the good quality of life to amputees and disabled people. In this paper, we have worked on recognizing the 9 classes of individual and combined finger movement captured using 2 channel EMG sensor. We have used two different classification techniques such as Artificial Neural Network (ANN), and k-nearest neighbors (KNN), to classify the test samples. Seven time domain features a) Mean absolute value, b) root mean square, c) variance, d) waveform length, e) number of zero crossing, f) complexity, g) mobility have been used to uniquely represent the EMG channel data. Tuning parameters like number of hidden layers, learning constant and number of neighbors have been determined from the experimental results to achieve the better classification results. Classification accuracy has been selected as a metric to evaluate the performance of each classifier.

Analysis of surface electromyography for hand gesure classification

“…There are large number of features have been proposed and studied for both sEMG and IMU sensors for detecting activities and gestures. Hence, we adopted some of well-established features [3], [15], [21][22][23][24][25]. A set of statistical features was obtained from EMG signal which are mean, mean absolute value, standard deviation, variance, skewness, number of zero crosses and root mean square.…”

“…The electrodes are placed at the forearm which is near to flexor carpi radialis longus muscle as shown in Figure 2. It is based on most researchers due to ability to get high recognition rate from this muscle [3], [21][22][23]. The bipolar configuration was used to improve the signal to noise ratio (SnR) of the sEMG signal reading.…”

Hand gesture recognition using machine learning and the Myo armband

“…The intracellular action potentials that form an EMG occur at random intervals [1][2][3][4][5][6][7]. Therefore, at any moment, the EMG may be either positive or negative.…”

“…To find the k closest neighbors to X, we need a training set  and a distance function. The set  = {(Z (1) ,Y (1) ),…, (Z…”

“…Electromyography (EMG) is a measure of the electrical activity in the skeletal muscles of the human body [1][2][3]. The anatomy and physiology of a muscle can be modeled as follows.…”