Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Feature Extraction <s> A novel approach to the control of a multifunction prosthesis based on the classification of myoelectric patterns is described. It is shown that the myoelectric signal exhibits a deterministic structure during the initial phase of a muscle contraction. Features are extracted from several time segments of the myoelectric signal to preserve pattern structure. These features are then classified using an artificial neural network. The control signals are derived from natural contraction patterns which can be produced reliably with little subject training. The new control scheme increases the number of functions which can be controlled by a single channel of myoelectric signal but does so in a way which does not increase the effort required by the amputee. Results are presented to support this approach. > <s> BIB001 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Feature Extraction <s> This paper presents an electromyographic (EMG) pattern recognition method to identify motion commands for the control of a prosthetic arm by evidence accumulation based on artificial intelligence with multiple parameters. The integral absolute value, variance, autoregressive (AR) model coefficients, linear cepstrum coefficients, and adaptive cepstrum vector are extracted as feature parameters from several time segments of EMG signals. Pattern recognition is carried out through the evidence accumulation procedure using the distances measured with reference parameters. A fuzzy mapping function is designed to transform the distances for the application of the evidence accumulation method. Results are presented to support the feasibility of the suggested approach for EMG pattern recognition. <s> BIB002 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Feature Extraction <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> Feature Extraction <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> BIB004 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Feature Extraction <s> A method for the classification of finger movements for dexterous control of prosthetic hands is proposed. Previous research was mainly devoted to identify hand movements as these actions generate strong electromyography (EMG) signals recorded from the forearm. In contrast, in this paper, we assess the use of multichannel surface electromyography (sEMG) to classify individual and combined finger movements for dexterous prosthetic control. sEMG channels were recorded from ten intact-limbed and six below-elbow amputee persons. Offline processing was used to evaluate the classification performance. The results show that high classification accuracies can be achieved with a processing chain consisting of time domain-autoregression feature extraction, orthogonal fuzzy neighborhood discriminant analysis for feature reduction, and linear discriminant analysis for classification. We show that finger and thumb movements can be decoded accurately with high accuracy with latencies as short as 200 ms. Thumb abduction was decoded successfully with high accuracy for six amputee persons for the first time. We also found that subsets of six EMG channels provide accuracy values similar to those computed with the full set of EMG channels (98% accuracy over ten intact-limbed subjects for the classification of 15 classes of different finger movements and 90% accuracy over six amputee persons for the classification of 12 classes of individual finger movements). These accuracy values are higher than previous studies, whereas we typically employed half the number of EMG channels per identified movement. <s> BIB005 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Feature Extraction <s> This paper describes two novel proportional control algorithms for use with pattern recognition-based myoelectric control. The systems were designed to provide automatic configuration of motion-specific gains and to normalize the control space to the user's usable dynamic range. Class-specific normalization parameters were calculated using data collected during classifier training and require no additional user action or configuration. The new control schemes were compared to the standard method of deriving proportional control using a one degree of freedom Fitts' law test for each of the wrist flexion/extension, wrist pronation/supination and hand close/open degrees of freedom. Performance was evaluated using the Fitts' law throughput value as well as more descriptive metrics including path efficiency, overshoot, stopping distance and completion rate. The proposed normalization methods significantly outperformed the incumbent method in every performance category for able bodied subjects and nearly every category for amputee subjects. Furthermore, one proposed method significantly outperformed both other methods in throughput , yielding 21% and 40% improvement over the incumbent method for amputee and able bodied subjects, respectively. The proposed control schemes represent a computationally simple method of fundamentally improving myoelectric control users' ability to elicit robust, and controlled, proportional velocity commands. <s> BIB006 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Feature Extraction <s> Abstract Recent studies in Electromyogram (EMG) pattern recognition reveal a gap between research findings and a viable clinical implementation of myoelectric control strategies. One of the important factors contributing to the limited performance of such controllers in practice is the variation in the limb position associated with normal use as it results in different EMG patterns for the same movements when carried out at different positions. However, the end goal of the myoelectric control scheme is to allow amputees to control their prosthetics in an intuitive and accurate manner regardless of the limb position at which the movement is initiated. In an attempt to reduce the impact of limb position on EMG pattern recognition, this paper proposes a new feature extraction method that extracts a set of power spectrum characteristics directly from the time-domain. The end goal is to form a set of features invariant to limb position. Specifically, the proposed method estimates the spectral moments, spectral sparsity, spectral flux, irregularity factor, and signals power spectrum correlation. This is achieved through using Fourier transform properties to form invariants to amplification, translation and signal scaling, providing an efficient and accurate representation of the underlying EMG activity. Additionally, due to the inherent temporal structure of the EMG signal, the proposed method is applied on the global segments of EMG data as well as the sliced segments using multiple overlapped windows. The performance of the proposed features is tested on EMG data collected from eleven subjects, while implementing eight classes of movements, each at five different limb positions. Practical results indicate that the proposed feature set can achieve significant reduction in classification error rates, in comparison to other methods, with ≈ 8 % error on average across all subjects and limb positions. A real-time implementation and demonstration is also provided and made available as a video supplement (see Appendix A ). <s> BIB007 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Feature Extraction <s> The non-stationary property of electromyography (EMG) signals in real life settings usually hinders the clinical application of the myoelectric pattern recognition for prosthesis control. The classical EMG pattern recognition approach consists of two separate steps: training and testing, without considering the changes between training and testing data induced by electrode shift, fatigue, impedance changes and psychological factors, and often results in performance degradation. The aim of this study was to develop an adaptive myoelectric pattern recognition system, aiming to retrain the classifier online with the testing data without supervision, providing a self-correction mechanism for suppressing misclassifications. This paper presents an adaptive unsupervised classifier based on support vector machine (SVM) to improve the classification performance. Experimental data from 15 healthy subjects were used to evaluate performance. Preliminary study on intra-session and inter-session EMG data was conducted to verify the performance of the unsupervised adaptive SVM classifier. The unsupervised adaptive SVM classifier outperformed the conventional SVM by 3.3% and 8.0% for the combination of time-domain and autoregressive features in the intra-session and inter-session tests, respectively. The proposed approach is capable of incorporating the useful information in testing data to the classification model by taking into account the overtime changes in the testing data with respect to the training data to retrain the original classifier, therefore providing a self-correction mechanism for suppressing misclassifications. <s> BIB008 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Feature Extraction <s> We investigate the problem of achieving robust control of hand prostheses by the electromyogram (EMG) of transradial amputees in the presence of variable force levels, as these variations can have a substantial impact on the robustness of the control of the prostheses. We also propose a novel set of features that aim at reducing the impact of force level variations on the prosthesis controlled by amputees. These features characterize the EMG activity by means of the orientation between a set of spectral moments descriptors extracted from the EMG signal and a nonlinearly mapped version of it. At the same time, our feature extraction method processes the EMG signals directly from the time-domain to reduce computational cost. The performance of the proposed features is tested on EMG data collected from nine transradial amputees performing six classes of movements each with three force levels. Our results indicate that the proposed features can achieve significant reductions in classification error rates in comparison to other well-known feature extraction methods, achieving improvements of $\approx 6\hbox{\%}$ to 8% in the average classification performance across all subjects and force levels, when training with all forces. <s> BIB009 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Feature Extraction <s> The extraction of the accurate and efficient descriptors of muscular activity plays an important role in tackling the challenging problem of myoelectric control of powered prostheses. In this paper, we present a new feature extraction framework that aims to give an enhanced representation of muscular activities through increasing the amount of information that can be extracted from individual and combined electromyogram (EMG) channels. We propose to use time-domain descriptors (TDDs) in estimating the EMG signal power spectrum characteristics; a step that preserves the computational power required for the construction of spectral features. Subsequently, TDD is used in a process that involves: 1) representing the temporal evolution of the EMG signals by progressively tracking the correlation between the TDD extracted from each analysis time window and a nonlinearly mapped version of it across the same EMG channel and 2) representing the spatial coherence between the different EMG channels, which is achieved by calculating the correlation between the TDD extracted from the differences of all possible combinations of pairs of channels and their nonlinearly mapped versions. The proposed temporal-spatial descriptors (TSDs) are validated on multiple sparse and high-density (HD) EMG data sets collected from a number of intact-limbed and amputees performing a large number of hand and finger movements. Classification results showed significant reductions in the achieved error rates in comparison to other methods, with the improvement of at least 8% on average across all subjects. Additionally, the proposed TSDs achieved significantly well in problems with HD-EMG with average classification errors of <5% across all subjects using windows lengths of 50 ms only. <s> BIB010 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Feature Extraction <s> Abstract The primary purpose of this research is to move a 5-DoF Aideepen ROT3U robotic arm in real-time based on the surface Electromyography (sEMG) signal obtained from a wireless Myo gesture armband to distinguish seven hand movements. The pattern recognition system is employed to analyze these gestures and consists of three main parts: segmentation, feature extraction, and classification. Overlap technique is chosen for segmenting portion of the signal. Six-time domain features, namely, Mean Absolute Value (MAV), Waveform Length (WL), Root Mean Square (RMS), Autoregressive Coefficients (AR), Zero Crossings (ZC), Slope Sign Changes (SSC) are extracted from each segment. While the Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), and K-Nearest Neighbor (K-NN) classifiers are employed in the classification of the seven hand movements. Moreover, a comparison between their performance is carried out to obtain optimum accuracy. The proposed system is tested on datasets extracted from six healthy subjects and the results showed that the SVM achieved higher system accuracy with 95.26% compared to LDA with an accuracy of 92.58%, and 86.41% accuracy achieved by K-NN. <s> BIB011 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Feature Extraction <s> Upper-limb amputation imposes significant burden on amputees thereby restricting them from fully exploring their environments during activities of daily living. The use of intelligent learning algorithm for electromyogram-pattern recognition (EMG-PR)-based control in upper-limb prostheses is considered as an important clinical option. Though the existing EMG-PR prostheses could discriminate multiple degrees of freedom (DOF) limb movements, their transition to clinically viable option is still being challenged by some confounding factors. Toward realizing a clinically viable multiple DOF prostheses, this paper first explored the principles and dynamics of the existing intelligently driven EMG-PR-based prostheses control scheme. Then, investigations on core issues including variation in muscle contraction force, electrode shift, and subject mobility affecting the existing EMG-PR prosthetic control scheme were reported. For instance, variation in muscle contraction force and subject mobility led to degradation in the performance of the EMG-PR controlled prostheses with approximately 17.00% and 8.98% error values, respectively, which are still challenging issues among others. Thus, this paper reports core issues and best practices with respect to intelligent EMG-PR controlled prosthesis, the major challenges in implementing adaptively robust control scheme and provides future research directions that may result in the clinical realization of intuitively dexterous multiple DOF EMG-PR-based prostheses in the near future. <s> BIB012 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Feature Extraction <s> Electromyography pattern recognition (EMG-PR) is extensively recognized in human–machine interactive applications such as prosthesis control and rehabilitation devices. Conventional time-domain (TD) features have been shown to produce a decent performance in upper limb movements’ classification. However, performance limitation exists in terms of classification accuracy. Hence, a novel feature set extraction on time derivative moments is proposed to improve the performance of EMG-PR in upper limb motion classification. A standardized and benchmark data base NinaPro (subdatabase2) has been used for examination of the proposed feature set. A total of eight intact subjects (sub1–sub8) have been selected for eight grasping motion's EMG signal collection. For classification of features, three classification techniques (linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machine (SVM)) have been applied. The average improvisation with respect to the conventional TD features that was achieved for LDA, QDA, and SVM are 8.08%, 7.95%, and 6.41%, respectively. <s> BIB013
Generally, EMG features are extracted in the form of time-domain (TD), frequency domain (FD) and time-frequency domain (TFD). In the TD, the features are extracted from the variations of signal amplitude with time BIB008 as per the muscular conditions. Unlike time features, the frequency domain uses the power spectrum density of the myosignals for extraction BIB006 . On the other hand, the combined features of time and frequency domain are used for time-frequency extraction (examples such as short Fourier transform and wavelets). The studies based on feature extractions proposed across TD, FD and TFD shows the best results using the TD EMG feature. Hudgins BIB001 proposed the four different time-domain features (MAV, WL, ZC, SSC) BIB011 for feature extraction from EMG, and it is the most adopted one to date in the field of myoelectric pattern recognition BIB012 . Willison amplitude (WAMP) BIB002 , Autoregressive (AR) model parameters BIB005 and time domain-auto regression (TD-AR) are also used to extract feature information. In comparison to other feature extraction methods, such as Fourier transform and Wavelet Transforms (WT), TD-AR features have achieved higher classification performance for the detection of hand movements with EMG signals BIB005 . Lui and Huang BIB004 implemented a fourth-order AR model for the EMG feature extraction and showed better classification performance. This approach only includes the trained data (EMG pattern classes) and rejects all untrained data of the classifier. Some of the recently developed features are Wavelet packet transform (WPT) based features BIB012 , short-time Fourier transform (STFT) BIB003 and EMG synergies by matrix factorization analysis  . STFT comparing to TD and fractal domain features state EMG signals better relationships with different muscles. Recently, time-dependent power spectral descriptors (TD-PSD) BIB012 were proposed, which consists of feature sets (wavelength ration, sparseness, irregularity factor and spectral moments (first, second and fourth)). TD-PSD with force level training shows more robustness of pattern recognition against force variation than most of the other feature extraction methods BIB007 , such as reduced spectral moments by Vuskovic and Du (VD-MOM), AR+RMS, TD, wavelet and discrete Fourier transform (DFT) BIB009 . Khushaba BIB010 proposed a temporal-spatial descriptor (TSDs). EMG features set collected from several intact-limbed and amputees are accepted on multiple sparse and high-density (HD) for executing multiple degrees of freedom (hand and finger movements). Time-derivative moments (TDM) based feature extraction BIB013 is a novel feature set extraction proposed to enhance the performance of EMG-PR in upper limb motion classification. Furthermore, most of the previous studies had focused on time-domain features to reduce computational difficulty. In addition, it does not require additional levels of data transformation  . Usually, after feature extraction, dimensionality reduction (DR) is applied. DR is the process of removing the number of arbitrary variables under consideration by locating a group of key variables. When the information is liberally dispersed (scattered) due to the EMG classification, there may be a problem caused by the large variance of the EMG signal. Thus, dimensionality reduction methods can unite this information more effectively and solve the problem of feature dimension. Dimensionality reduction thus helps in saving the computational cost and reduces the level of system complexity  . Uncorrelated linear discriminant analysis (ULDA), principle component analysis (PCA), and orthogonal fuzzy neighbourhood discriminative approach (OFNDA) are common dimensionality reduction (DR) techniques used to reduce the feature space.