Review on EMG Acquisition and Classification Techniques: Towards Zero Retraining in the Influence of User and Arm Position Independence

The surface electromyogram (EMG) is widely studied and applied in machine control. Recent methods of classifying hand gestures reported classification rates of over 95%. However, the majority of the studies made were performed on a single user, focusing solely on the gesture classification. These studies are restrictive in practical sense: either focusing on just gestures, multi-user compatibility, or rotation independence. The variations in EMG signals due to these conditions present a challenge to the practical application of EMG devices, often requiring repetitious training per application. To the best of our knowledge, there is little comprehensive review of works done in EMG classification in the combined influence of user-independence, rotation and hand exchange. Therefore, in this paper we present a review of works related to the practical issues of EMG with a focus on the EMG placement, and recent acquisition and computing techniques to reduce training. First, we provided an overview of existing electrode placement schemes. Secondly, we compared the techniques and results of single-subject against multi-subject, multi-position settings. As a conclusion, the study of EMG classification in this direction is relatively new. However the results are encouraging and strongly indicate that EMG classification in a broad range of people and tolerance towards arm orientation is possible, and can pave way for more flexible EMG devices

The Classification of EMG Signals with Zero Retraining in the Influence of User and Rotation Independence

The surface electromyogram (EMG) contains information directly related to muscle contraction and modern classification techniques can obtain near-zero error when identifying various gestures over the forearm. However, good results come at a compromise over the ease of use. Once the EMG classifier trained on a user is changed, the accuracy rate will be greatly reduced. Furthermore, changing the position of the forearm also causes drop in accuracy rate. Acknowledging the limitations of EMG classification, this study aims to investigate the EMG signals based on the gestures, and evaluate if there are any gestures which are inherently robust to these variations. The EMG of forearm gestures have been classified in the combined influence user independence, rotation independence and hand exchange independence. Experiment results on 20 participants indicated that truly independent classification can be achieved for most forearm gestures (up to 100%) in some arm positions. Hand exchange is also not feasible as the study has shown that the data field for both hands are fairly different. Out of the nine gestures under study, only the wrist extension was found to be truly independent of all the influences

A first approach to a taxonomy-based classification framework for hand grasps

Many solutions have been proposed to help amputated subjects regain the lost functionality. In order to interact with the outer world and objects that populate it, it is crucial for these subjects to being able to perform essential grasps. In this paper we propose a preliminary solution for the online classification of 8 basics hand grasps by considering physiological signals, namely Surface Electromyography (sEMG), exploiting a quantitative taxonomy of the considered movement. The hierarchical organization of the taxonomy allows a decomposition of the classification phase between couples of movement groups. The idea is that the closest to the roots the more hard is the classification, but on the meantime the miss-classification error is less problematic, since the two movements will be close to each other. The proposed solution is subject-independent, which means that signals from many different subjects are considered by the probabilistic framework to modelize the input signals. The information has been modeled offline by using a Gaussian Mixture Model (GMM), and then testen online on a unseen subject, by using a Gaussian-based classification. In order to be able to process the signal online, an accurate preprocessing phase is needed, in particular, we apply the Wavelet Transform (Wavelet Transform) to the Electromyography (EMG) signal. Thanks to this approach we are able to develop a robust and general solution, which can adapt quickly to new subjects, with no need of long and draining training phase. In this preliminary study we were able to reach a mean accuracy of 76.5%, reaching up to 97.29% in the higher levels

Real-time visual and EMG signals recognition to control dexterous prosthetic hand based on deep learning and machine learning

The revolution in prosthetic hands allows the evolution of a new generation of prostheses that increase artificial intelligence to control an adept hand. A suitable gripping and grasping action for different shapes of the objects is currently a challenging task of prosthetic hand design. The most artificial hands are based on electromyography signals. A novel approach has been proposed in this work using deep learning classification method for assorting items into seven gripping patterns based on EMG and image recognition. Hence, this approach conducting two scenarios; The first scenario is recording the EMG signals for five healthy participants for the basic hand movement (cylindrical, tip, spherical, lateral, palmar, and hook). Then three time-domain (standard deviation, mean absolute value, and the principal component analysis) are used to extract the EMG signal features. After that, the SVM is used to find the proper classes and achieve an accuracy that reaches 89%. The second scenario is collecting the 723 RGB images for 24 items and sorting them into seven classes, i.e., cylindrical, tip, spherical, lateral, palmar, hook, and full hand. The GoogLeNet algorithm is used for training based on 144 layers; these layers include the convolutional layers, ReLU activation layers, max-pooling layers, drop-out layers, and a softmax layer. The GoogLeNet achieves high training accuracy reaches 99%. Finally, the system is tested, and the experiments showed that the proposed visual hand based on the myoelectric control method (Vision-EMG) could significantly give recognition accuracy reaches 95%

Hand Gesture Classification Using Emg Signal

The art of gesture recognition involves identification and classification of gestures. A gesture is any reproducible action or a sequence of actions. There are lots of techniques and algorithms to recognize gestures. In the project, gestures are recognized using biological signals generated by the human body. There are many biological signals that can be used for gesture recognition. Some of them are Electroencephalogram (EEG), Electrocardiogram (ECG), and Electromyogram (EMG). EMG signals are generally used because they have good signal strength (in the order of mV). Thus we use emg signal as the acquisition of EMG signals is easy and less complex ascompared to the above mentioned signals. Five different gestures such as Six features such as . root mean square, mean, standard deviation, variance, maximum and minimum values are extracted from the emg signals. The classifier used under the study is SVM , giving classification accuracy of 96.8%

Three-Way Analysis of Spectrospatial Electromyography Data : Classification and Interpretation

Classifying multivariate electromyography (EMG) data is an important problem in prosthesis control as well as in neurophysiological studies and diagnosis. With modern high-density EMG sensor technology, it is possible to capture the rich spectrospatial structure of the myoelectric activity. We hypothesize that multi-way machine learning methods can efficiently utilize this structure in classification as well as reveal interesting patterns in it. To this end, we investigate the suitability of existing three-way classification methods to EMG-based hand movement classification in spectrospatial domain, as well as extend these methods by sparsification and regularization. We propose to use Fourier-domain independent component analysis as preprocessing to improve classification and interpretability of the results. In high-density EMG experiments on hand movements across 10 subjects, three-way classification yielded higher average performance compared with state-of-the art classification based on temporal features, suggesting that the three-way analysis approach can efficiently utilize detailed spectrospatial information of high-density EMG. Phase and amplitude patterns of features selected by the classifier in finger-movement data were found to be consistent with known physiology. Thus, our approach can accurately resolve hand and finger movements on the basis of detailed spectrospatial information, and at the same time allows for physiological interpretation of the results.Peer reviewe

Knee Joint Angle Prediction Based on Muscle Synergy Theory and Generalized Regression Neural Network

Continuous joint motion estimation plays an important part in accomplishing more compliant and safer human-machine interaction (HMI). Surface electromyogram (sEMG) signals, which contain abundant motion information, can be used as a source for continuous joint motion estimation. In this paper, a knee joint angle prediction system based on muscle synergy theory and generalized regression neural network (GRNN) was proposed. The wavelet transform threshold method was used for sEMG signals and angle trajectories denoising. The time-domain features wave-length extracted from four-channel sEMG signals were decomposed into a synergy matrix and an activation coefficient matrix by using nonnegative matrix factorization based on muscle synergy theory. A GRNN based on golden-section search was employed to build the activation model mapping from the activation coefficients to the knee joint angles, so as to realize the continuous knee joint angle estimation. The experimental results show that the average coefficient of determination is 0.933. In addition, a user graphic interface based on the Java platform was designed to display the dynamic sEMG data and predicted knee joint angles in real time

Causes of Performance Degradation in Non-invasive Electromyographic Pattern Recognition in Upper Limb Prostheses

Surface Electromyography (EMG)-based pattern recognition methods have been investigated over the past years as a means of controlling upper limb prostheses. Despite the very good reported performance of myoelectric controlled prosthetic hands in lab conditions, real-time performance in everyday life conditions is not as robust and reliable, explaining the limited clinical use of pattern recognition control. The main reason behind the instability of myoelectric pattern recognition control is that EMG signals are non-stationary in real-life environments and present a lot of variability over time and across subjects, hence affecting the system's performance. This can be the result of one or many combined changes, such as muscle fatigue, electrode displacement, difference in arm posture, user adaptation on the device over time and inter-subject singularity. In this paper an extensive literature review is performed to present the causes of the drift of EMG signals, ways of detecting them and possible techniques to counteract for their effects in the application of upper limb prostheses. The suggested techniques are organized in a table that can be used to recognize possible problems in the clinical application of EMG-based pattern recognition methods for upper limb prosthesis applications and state-of-the-art methods to deal with such problems