Pattern recognition for EMG based forearm orientation and contraction in myoelectric prosthetic hand

Abstract

Communication is an essential component of human nature. It connects humans, allowing them to learn, grow, col-laborate, and resolve conflicts. Several aspects of human society, relationships, and growth would be significantly hampered in the absence of efficient communication. Hand gesture recognition is a way to interact with technology that can be particularly useful for individuals with disabilities. This hand gesture recognition is mainly employed in sign language translation, healthcare, rehabilitation, prosthesis, and human-computer interaction (HCI). The high degree of dexterity is a main challenge for prosthetic limbs. In order to meet this challenge, hand gesture recognition is employed for the prosthetic limb, which can be used for rehabilitation. The objective of this article is to show the methodology for the recognition of hand gestures using Electromyography (EMG) signals. This article uses the pro-posed time domain feature extraction method called Absolute Fluctuation Analysis (AFA) along with the Root Mean Square (RMS) for the feature extraction method. Along with these feature extraction methods, repeated stratified K-fold cross validation is used for the validation of the classifiers such as the XGB classifier, the K-Nearest Neighbour (KNN) classifier, the Decision Tree classifier, the Random Forest classifier, and the SVM classifier, whose mean recognition accuracy is given by 93.26%, 87.42%, 85.26%, 92.23%, and 91.78%, respectively. The recognition accuracy of machine learning classifiers is being compared with state-of-the-art networks such as artificial neural net-works (ANN), long short-term memory (LSTM), bidirectional LSTM, gated recurrent units (GRU), and convolution-al neural networks (CNN), which provide recognition accuracy of 96.65%, 99.16%, 99.94%, and 99.99%, respectively.

Mapping Method of Human Arm Motion Based on Surface Electromyography Signals

“…The confusion matrix being trained by the CNN-ANN Hybrid Network Model [29][30][31] with multi-feature fusion is shown in Figure 13. Due to the fact that different channels of sEMG data correspond to different positions on the arm, convolution and pooling operations on the sEMG images are performed only in the length direction of the image, which allows extraction of temporal features from the signals and ensures the independence of data between different channels.…”

“…The confusion matrix being trained by the CNN-ANN Hybrid Network Model [29][30][31] with multi-feature fusion is shown in Figure 13. According to the confusion matrix, it is evident that clenching and relaxing movements are relatively easy to recognize, and the recognition rates of both movements are above 95%.…”