Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Myoelectric Classification <s> An investigation was conducted into the feasibility of applying a nearest neighbor algorithm to the problem of recognizing electromyographic (EMG) signal patterns for prosthesis control. A nearest neighbor algorithm correctly identified arm motions as belonging to one of six pattern classes from 72 to 100 percent of the time. A condensed nearest neighbor classifier was constructed to determine what minimum number of vectors was necessary in the look-up table. <s> BIB001 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Myoelectric Classification <s> A variety of EMG features have been evaluated for control of myoelectric upper extremity prostheses. Movement class discrimination, robustness, and computational complexity of these features have been investigated for different time window sizes and noise levels. The measurements include novel application of the Davies-Bouldin index, a measure of cluster separability, and the K-nearest neighbor nonparametric classifier. The features evaluated are the integral of average value, the variance, the number of zero crossings, the Willison amplitude, the v-order and log detectors, and autoregressive model parameters. A new feature, the EMG Histogram, is introduced and shown to be the most effective of the group. The experiments were done on the data acquired from the residual biceps and triceps muscle of an above-elbow amputee. <s> BIB002 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Myoelectric Classification <s> This work represents an ongoing investigation of dexterous and natural control of powered upper limbs using the myoelectric signal. When approached as a pattern recognition problem, the success of a myoelectric control scheme depends largely on the classification accuracy. A novel approach is described that demonstrates greater accuracy than in previous work. Fundamental to the success of this method is the use of a wavelet-based feature set, reduced in dimension by principal components analysis. Further, it is shown that four channels of myoelectric data greatly improve the classification accuracy, as compared to one or two channels. It is demonstrated that exceptionally accurate performance is possible using the steady-state myoelectric signal. Exploiting these successes, a robust online classifier is constructed, which produces class decisions on a continuous stream of data. Although in its preliminary stages of development, this scheme promises a more natural and efficient means of myoelectric control than one based on discrete, transient bursts of activity. <s> BIB003 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Myoelectric Classification <s> Classifying the motion of the five fingers of the hand using non-invasive bio-signal readings from the forearm is still an unsolved research challenge. Its solution is relevant to hands-free remote control devices, on-stage live performances, consumer entertainment, the video game industry, and most importantly the design of hand prosthetics for amputees. This paper proposes a solution that uses the continuous wavelet transform (CWT) decompositions of electromyography (EMG) signals from the forearm muscles, and Support Vector Machines (SVM) classification. The resulting design is a low cost, low power and low complexity portable embedded system that is strapped to the arm, where it collects EMG signals, classifies them in real-time, and sends the resulting class labels via Bluetooth to a remote interface. These labels are then converted into musical instrument digital interface (MIDI) commands that can be used to control any MIDI-controllable device. While the design is still at the prototype stage at best, it provides a proof-of-concept of non-invasive finger motion classification solely based on EMG readings from the forearm muscles. Experimental simulation of the expected system achieved 91% accuracy. <s> BIB004 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Myoelectric Classification <s> This paper aims at dealing with a critical issue for electromyography (EMG) recognition. The issue is related to the stability of an EMG-based prosthesis control. Traditional EMG recognition systems receive EMG patterns and send them into classifiers directly, which generally results in unstable situations if the classes of some of the input EMG patterns are not included in the training of the classifiers. The EMG patterns whose class labels are not defined in the training phase are called non-target patterns. There should be a filter and this filter should be able to reject all non-target EMG patterns. As such, only target EMG patterns are fed into classifier, thus achieving a high-accuracy EMG classification. To this end, we propose in this paper a one-class classification-based non-target EMG pattern filtering scheme. By introducing a novel one-class classifier, called support vector data description (SVDD), into the filtering scheme, the goal mentioned above can easily be achieved. SVDD is a powerful machine learning technique. It can be built on a single class and find a flexible boundary to enclose the target class by using the so-called kernel trick. In experiments, we will show that if the filtering scheme is not performed, the traditional EMG classification system suffers from unstable situations. Contrarily, the whole classification system will achieve satisfactory and stable performance no matter what the input EMG patterns are target or non-target ones, if the proposed filtering scheme is embedded. <s> BIB005 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Myoelectric Classification <s> Electromyographic control is a technique that involve with the detection, processing and classification of the electromyography signal that could be applied in human-assisting robots, prosthesis application or rehabilitation devices. This paper reviews recent research and development electromyographic control systems with an emphasis on pattern recognition control for prosthesis application. Various methods used in the different stages of the pattern recognition based control system are discussed in details. <s> BIB006 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Myoelectric Classification <s> ABSTRACT The choice of a myoelectric or body-powered upper-limb prosthesis can be determined using factors including control, function, feedback, cosmesis, and rejection. Although body-powered and myoelectric control strategies offer unique functions, many prosthesis users must choose one. A systematic review was conducted to determine differences between myoelectric and body-powered prostheses to inform evidence-based clinical practice regarding prescription of these devices and training of users. A search of 9 databases identified 462 unique publications. Ultimately, 31 of them were included and 11 empirical evidence statements were developed. Conflicting evidence has been found in terms of the relative functional performance of body-powered and myoelectric prostheses. Body-powered prostheses have been shown to have advantages in durability, training time, frequency of adjustment, maintenance, and feedback; however, they could still benefit from improvements of control. Myoelectric prostheses have been shown to improve cosmesis and phantom-limb pain and are more accepted for light-intensity work. Currently, evidence is insufficient to conclude that either system provides a significant general advantage. Prosthetic selection should be based on a patient’s individual needs and include personal preferences, prosthetic experience, and functional needs. This work demonstrates that there is a lack of empirical evidence regarding functional differences in upper-limb prostheses. <s> BIB007 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Myoelectric Classification <s> This paper presents a literature review on pattern recognition of electromyography (EMG) signals and its applications. The EMG technology is introduced and the most relevant aspects for the design of an EMG-based system are highlighted, including signal acquisition and filtering. EMG-based systems have been used with relative success to control upper- and lower-limb prostheses, electronic devices and machines, and for monitoring human behavior. Nevertheless, the existing systems are still inadequate and are often abandoned by their users, prompting for further research. Besides controlling prostheses, EMG technology is also beneficial for the development of machine learning-based devices that can capture the intention of able-bodied users by detecting their gestures, opening the way for new human-machine interaction (HMI) modalities. This paper also reviews the current feature extraction techniques, including signal processing and data dimensionality reduction. Novel classification methods and approaches for detecting non-trained gestures are discussed. Finally, current applications are reviewed, through the comparison of different EMG systems and discussion of their advantages and drawbacks. <s> BIB008
The next stage, followed by feature extraction, is feature classification. The information gathered during feature extraction is fed into the classification stage. A classifier should be able to classify the pattern efficiently in less time to meet the real-time constraints of the prosthesis. Notably, only a few numbers of studies have compared the potentiality of classifiers to meet real-time control. The myosignals pattern classification for explicit movements is more focused on the extraction of activities from arm muscles. For amputees, due to their amputations, only a few muscles will be present in the residual limb to extract the feature of signals. For instance, in the case of transhumeral amputation, the availability of forearm muscles is completely unavailable. Most of the pattern recognition studies are, therefore, concentrated on trans-radial control. However, several studies tried to classify finger movements using multiple features and classifiers, such as multi-layer perceptron and neural network. Usually, in EMG-PR, the classifier performs well on trained (actions) classification patterns. To improve the robustness of PR systems, the untrained classification pattern is also equally important. Furthermore, from the past few years, the detection of untrained actions (novelty detection) has also been studied. In order to solve the problem of novelty detection BIB008 , different methods were proposed and studied, such as the ensemble of one-class support vector data description (SVDD) BIB005 , and modified boosted random forests BIB008 . Although various classification methods are available, classification algorithms include two main trends: Statistical (LDA and SVM) and Neural (MLP and ANN) BIB004 . The conventional PR method also depended on KNN and LDA algorithms to categorise arm motions into different pattern classes. LDA scheme is one of the most adopted classifiers in the implementation of myoelectric control. LDA classifier has shown high classification accuracy, and it is very simple to implement BIB006 . In KNN feasibility and precision check of classifying features have done for diverse time windows such as EMG histogram and noise levels BIB001 BIB002 . In 2001, instead of concentrating fully on the classifier like KNN or LDA, some authors demonstrated that the pattern classification and its accuracy are more deeply depend on the selection of features BIB003 BIB007 . As the input data are suddenly changing myosignals, they have the downside of immediately switching the control from rest to contraction. This prohibits changeover of the feature set from one to another in less time and an efficient manner. Moreover, it delays the coordination between multiple tasks utilizing large degrees of freedom in real-time. As a solution, the wavelet packet-based feature set can classify myoelectric activity in real-time where the data streams are continuously exhibiting superior performance. A research work then started on the KNN classifier with a genetic algorithm, upon the trans-radial muscles having the potential to control a multi-fingered prosthetic hand. Another classification technique mostly used for the EMG signal classification is an artificial neural network (ANN). An ANN is easily trainable and has the capability of modelling both linear and non-linear data BIB006 . The SVM classifier, due to its kernel-based characteristics, has gained wide application in the field of myoelectric control. SVM is most popular for performing classification as well as regression using machine learning tasks. SVM is an advanced statistical learning approach providing an accurate and optimal solution in a short time. The different classifiers used in various works of literature have been reported in Tables 1-3 . Although a lot of developments and progress were made in the field of EMG-PR, the development of a DOF prosthesis that could aid in simultaneous prosthetic control is still a challenge. Based on the literature studies, it indicates that classification accuracy can be increased with appropriate use of EMG channel and feature set.