Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Real-Time Collected Data from Amputee <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> Real-Time Collected Data from Amputee <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> Real-Time Collected Data from Amputee <s> Accurate and computationally efficient means of classifying surface myoelectric signals has been the subject of considerable research effort in recent years. The aim of this paper is to classify myoelectric signals using new fuzzy clustering neural network (NN) architectures to control multifunction prostheses. This paper presents a comparative study of the classification accuracy of myoelectric signals using multilayered perceptron NN using back-propagation, conic section function NN, and new fuzzy clustering NNs (FCNNs). The myoelectric signals considered are used in classifying six upper-limb movements: elbow flexion, elbow extension, wrist pronation and wrist supination, grasp, and resting. The results suggest that FCNN can generalize better than other NN algorithms and help the user learn better and faster. This method has the potential of being very efficient in real-time applications. <s> BIB003 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Real-Time Collected Data from Amputee <s> This paper represents an ongoing investigation of dexterous and natural control of upper extremity prostheses using the myoelectric signal. The scheme described within uses a hidden Markov model (HMM) to process four channels of myoelectric signal, with the task of discriminating six classes of limb movement. The HMM-based approach is shown to be capable of higher classification accuracy than previous methods based upon multilayer perceptrons. The method does not require segmentation of the myoelectric signal data, allowing a continuous stream of class decisions to be delivered to a prosthetic device. Due to the fact that the classifier learns the muscle activation patterns for each desired class for each individual, a natural control actuation results. The continuous decision stream allows complex sequences of manipulation involving multiple joints to be performed without interruption. The computational complexity of the HMM in its operational mode is low, making it suitable for a real-time implementation. The low computational overhead associated with training the HMM also enables the possibility of adaptive classifier training while in use. <s> BIB004 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Real-Time Collected Data from Amputee <s> This paper presents a two-part study investigating the use of forearm surface electromyographic (EMG) signals for real-time control of a robotic arm. In the first part of the study, we explore and extend current classification-based paradigms for myoelectric control to obtain high accuracy (92-98%) on an eight-class offline classification problem, with up to 16 classifications/s. This offline study suggested that a high degree of control could be achieved with very little training time (under 10 min). The second part of this paper describes the design of an online control system for a robotic arm with 4 degrees of freedom. We evaluated the performance of the EMG-based real-time control system by comparing it with a keyboard-control baseline in a three-subject study for a variety of complex tasks. <s> BIB005 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Real-Time Collected Data from Amputee <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> BIB006 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Real-Time Collected Data from Amputee <s> Abstract In this paper, a solution is proposed to predict the finger joint angle using electromyography (EMG) towards application for partial-hand amputees with functional wrist. In the experimental paradigm, the subject was instructed to continuously move one finger (middle finger for able-bodied subjects and index finger for partial-hand amputees) up to the maximum angle of flexion and extension while the wrist was conducting seven different wrist motions. A switching regime, including one linear discriminant analysis (LDA) classifier and fourteen state-space models, was proposed to continuously decode finger joint angles. LDA classifier was used to recognize which static wrist motion that the subject was conducting and choose the corresponding two state-space models for decoding joint angles of the finger with two degrees of freedom (DOFs). The average classification error rate (CER) was 6.18%, demonstrating that these seven static wrist motions along with the continuous movement of the finger could be classified. To improve the classification performance, a preprocessing method, class-wise stationary subspace analysis (cwSSA), was firstly adopted to extract the stationary components from original EMG data. Consequently, the average CER was reduced by 1.82% ( p R 2 ) of the two joint angles of the finger across seven static wrist motions achieved 0.843. This result shows that the finger's joint angles can be continuously estimated well while the wrist was conducting different static motions simultaneously. The average accuracy of seven static wrist motions with and without cwSSA and the average estimation performance of the two joint angles of the finger prove that the proposed switching regime is effective for continuous estimation of the finger joint angles under different static wrist motions from EMG. <s> BIB007 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Real-Time Collected Data from Amputee <s> Abstract We propose a methodological study for the optimization of surface EMG (sEMG)-based hand gesture classification, effective to implement a human–computer interaction device for both healthy subjects and transradial amputees. The widely commonly used unsupervised Principal Component Analysis (PCA) approach was compared to the promising supervised common spatial pattern (CSP) methodology to identify the best classification strategy and the related tuning parameters. A low density array of sEMG sensors was built to record the muscular activity of the forearm and classify five different hand gestures. Twenty healthy subjects were recruited to compute optimized parameters for (“ within ” analysis) and to compare between (“ between ” analysis) the two strategies. The system was also tested on a transradial amputee subject, in order to assess the robustness of the optimization in recognizing disabled users’ gestures. Results show that RMS-WA/ANN is the best feature vector/classifier pair for the PCA approach (accuracy 88.81 ± 6.58%), whereas M-RMS-WA/ANN is the best pair for the CSP methodology (accuracy of 89.35 ± 6.16%). Statistical analysis on classification results shows no significant differences between the two strategies. Moreover we found out that the optimization computed for healthy subjects was proven to be sufficiently robust to be used on the amputee subject. This motivates further investigation of the proposed methodology on a larger sample of amputees. Our results are useful to boost EMG-based hand gesture recognition and constitute a step toward the definition of an efficient EMG-controlled system for amputees. <s> BIB008 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Real-Time Collected Data from Amputee <s> Research on pattern recognition for myoelectric control has usually focused on a small number of electromyography (EMG) channels because of better clinical acceptability and low computational load with respect to multi-channel EMG. However, recently, high density (HD) EMG technology has substantially improved, also in practical usability, and can thus be applied in myocontrol. HD EMG provides several closely spaced recordings in multiple locations over the skin surface. This study considered the use of HD EMG for controlling upper limb prostheses, based on pattern recognition. In general, robustness and reliability of classical pattern recognition systems are influenced by electrode shift in dons and doff, and by the presence of malfunctioning channels. The aim of this study is to propose a new approach to attenuate these issues. The HD EMG grid of electrodes is an ensemble of sensors that records data spatially correlated. The experimental variogram, which is a measure of the degree of spatial correlation, was used as feature for classification, contrary to previous approaches that are based on temporal or frequency features. The classification based on the variogram was tested on seven able-bodied subjects and one subject with amputation, for the classification of nine and seven classes, respectively. The performance of the proposed approach was comparable with the classic methods based on time-domain and autoregressive features (average classification accuracy over all methods ∼ 95% for nine classes). However, the new spatial features demonstrated lower sensitivity to electrode shift ( ± 1 cm) with respect to the classic features . When even just one channel was noisy, the classification accuracy dropped by ∼ 10% for all methods. However, the new method could be applied without any retraining to a subset of high-quality channels whereas the classic methods require retraining when some channels are omitted. In conclusion, the new spatial feature space proposed in this study improved the robustness to electrode number and shift in myocontrol with respect to previous approaches. <s> BIB009 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Real-Time Collected Data from Amputee <s> Surface electromyography (sEMG)-based pattern recognition studies have been widely used to improve the classification accuracy of upper limb gestures. Information extracted from multiple sensors of the sEMG recording sites can be used as inputs to control powered upper limb prostheses. However, usage of multiple EMG sensors on the prosthetic hand is not practical and makes it difficult for amputees due to electrode shift/movement, and often amputees feel discomfort in wearing sEMG sensor array. Instead, using fewer numbers of sensors would greatly improve the controllability of prosthetic devices and it would add dexterity and flexibility in their operation. In this paper, we propose a novel myoelectric control technique for identification of various gestures using the minimum number of sensors based on independent component analysis (ICA) and Icasso clustering. The proposed method is a model-based approach where a combination of source separation and Icasso clustering was utilized to improve the classification performance of independent finger movements for transradial amputee subjects. Two sEMG sensor combinations were investigated based on the muscle morphology and Icasso clustering and compared to Sequential Forward Selection (SFS) and greedy search algorithm. The performance of the proposed method has been validated with five transradial amputees, which reports a higher classification accuracy ( > 95%). The outcome of this study encourages possible extension of the proposed approach to real time prosthetic applications. <s> BIB010 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Real-Time Collected Data from Amputee <s> Previous studies have showed that arm position variations would significantly degrade the classification performance of myoelectric pattern-recognition-based prosthetic control, and the cascade classifier (CC) and multiposition classifier (MPC) have been proposed to minimize such degradation in offline scenarios. However, it remains unknown whether these proposed approaches could also perform well in the clinical use of a multifunctional prosthesis control. In this study, the online effect of arm position variation on motion identification was evaluated by using a motion-test environment (MTE) developed to mimic the real-time control of myoelectric prostheses. The performance of different classifier configurations in reducing the impact of arm position variation was investigated using four real-time metrics based on dataset obtained from transradial amputees. The results of this study showed that, compared to the commonly used motion classification method, the CC and MPC configurations improved the real-time performance across seven classes of movements in five different arm positions (8.7% and 12.7% increments of motion completion rate, resp.). The results also indicated that high offline classification accuracy might not ensure good real-time performance under variable arm positions, which necessitated the investigation of the real-time control performance to gain proper insight on the clinical implementation of EMG-pattern-recognition-based controllers for limb amputees. <s> BIB011
Several research studies on able-bodied and hand amputees have been done to evaluate the consequences of arm position variation on EMG-PR classification performance. Offline classification accuracy/errors have shown that arm variation affects the classification performance. To reduce such effect of arm variation, various classification techniques BIB011 have been proposed. Similarly, classification accuracy identifies the desired movements from several classes of motion. Some of the previous research studies had shown that offline classification has not a good correlation with real-time performance of EMG-PR BIB011 control of the prosthesis. However, some of the recent classification accuracies on the real-time performance of amputee data are explained in this section. Nearly all EMG-PR control for real-time collected data from hand amputee followed the same stages to operate. The features are extracted from pre-processed EMG data. From the extracted data, the feature is usually selected for training and control set. Then, the classification technique is applied for training classifiers and control set classifiers. The general algorithm of this whole system is shown in Figure 2 . In 2003, Karlik et al. BIB003 conducted a study on the classification of myoelectric signals for precise overall control of multifunction prosthesis using Fuzzy clustering neural network architecture. The author also compared the accuracy of multi-layer perceptron (MLP) having a back-propagation algorithm and the new fuzzy clustering neural networks (FCCN). The fuzzy clustering involved the division of input data into several fuzzy parts that intersect each other and thus defined by membership grade [0,1]. An algorithm was proposed to implement these fuzzy clustering that minimised the cost function. A comparative assessment shows that using FCNN provides more reliable results than MLP. The FCNN achieved 98% accuracy with half training time than that of MLP. Later, in 2005, a promising method by Chan and Englehart BIB004 added into the row of the continuous controllers. The new method followed a hidden Markov model (HMM) as the data segment classifier. The HMM classifier was a suitable probabilistic approach for pattern recognition at that time due to the resilience to sequential myosignal variations. For a four-channel six function design, HMM has more performance accuracy (94.63%) than MLP with a good level of robustness and quick response. In 2003, Karlik et al. BIB003 conducted a study on the classification of myoelectric signals for precise overall control of multifunction prosthesis using Fuzzy clustering neural network architecture. The author also compared the accuracy of multi-layer perceptron (MLP) having a back-propagation algorithm and the new fuzzy clustering neural networks (FCCN). The fuzzy clustering involved the division of input data into several fuzzy parts that intersect each other and thus defined by membership grade [0,1]. An algorithm was proposed to implement these fuzzy clustering that minimised the cost function. A comparative assessment shows that using FCNN provides more reliable results than MLP. The FCNN achieved 98% accuracy with half training time than that of MLP. Later, in 2005, a promising method by Chan and Englehart BIB004 added into the row of the continuous controllers. The new method followed a hidden Markov model (HMM) as the data segment classifier. The HMM classifier was a suitable probabilistic approach for pattern recognition at that time due to the resilience to sequential myosignal variations. For a four-channel six function design, HMM has more performance accuracy (94.63%) than MLP with a good level of robustness and quick response. Al-Timemy et al. BIB006 proposed a process for the classification of finger motions for dexterous control of the myoelectric prosthesis. The myosignal was recorded from six traumatic below-elbow amputees. TD-AR features were used to extract useful information from the segmented EMG window. To find the best match of features reduction (to reduce computational power) and classifiers, two different features reduction tools (PCA and orthogonal fuzzy neighbourhood discriminative approach (OFNDA)) and classifiers (LDA and SVM) were combined to make four different forms. The results show the high accuracy with OFNDA and LDA. Furthermore, the studies show that feature reduction plays an important role than a classifier to achieve high accuracy with multi EMG channels. In 2013, Pan et al. proposed a solution for partial hand amputees with a functional wrist to predict the finger joint angle using EMG BIB007 . The Al-Timemy et al. BIB006 proposed a process for the classification of finger motions for dexterous control of the myoelectric prosthesis. The myosignal was recorded from six traumatic below-elbow amputees. TD-AR features were used to extract useful information from the segmented EMG window. To find the best match of features reduction (to reduce computational power) and classifiers, two different features reduction tools (PCA and orthogonal fuzzy neighbourhood discriminative approach (OFNDA)) and classifiers (LDA and SVM) were combined to make four different forms. The results show the high accuracy with OFNDA and LDA. Furthermore, the studies show that feature reduction plays an important role than a classifier to achieve high accuracy with multi EMG channels. In 2013, Pan et al. proposed a solution for partial hand amputees with a functional wrist to predict the finger joint angle using EMG BIB007 . The experiment was performed on two amputees. EMG signal was recorded from eight targeted muscles and was sampled at 2000 Hz frequency. TD feature sets were fed to the LDA classifier to identify seven different static wrist positions. A switching rule, including LDA classifier and 14 state-space models, was proposed for continuous decoding of finger joint angles. The average classification error rate (CER) was 6.18%, which demonstrates that forearm movements and the continuous movement of the finger can be easily classified. Similarly, in 2016, Ganesh et al. proposed the combination of ICA and Icasso to minimise the number of EMG sensors and increased robustness of myoelectric control BIB010 . Early in 1993, BIB001 an experiment was performed on one amputee using the Hudgins feature set. These features classified using ANN classifier with one EMG channel proved that the EMG signal shows deterministic structure during the beginning of muscle contraction. Riillo et al. BIB008 proposed an optimization methodology of sEMG based hand gesture classification using pre-processing techniques such as PCA (unsupervised) and CSP (supervised). One trans-radial amputee (right-hand below-elbow amputee) participated in the experiment. TD features were extracted from segmented data (using overlapped windows). Similarly, three classifiers (LDA, SVM and ANN) BIB008 were tested by assessing the average accuracies of each time window. The study shows that the best results obtained for the real-time system were using the ANN classifier. Another research work extended the classification control using a support vector machine (SVM), obtained high accuracy (92-98%) with less training time BIB005 . Stango et al. BIB009 used the SVM classification technique, followed by variogram features. The variogram is a measurable degree of spatial correlation. The experiment was performed on one trans-radial traumatic amputees. The main purpose of this experiment was to analyse the spatial features of HD EMG-PR for myoelectric control. Variogram features maintain a good classification accuracy without retraining even if the EMG channel is eliminated during the experiment phase. Hence, the study shows spatial proposed improved the robustness of EMG-PR. In [68] , the effectiveness of using twin SVM (TSVM) in multi-class prosthetic control with unbalanced datasets was demonstrated with the RMS value (feature). The summary of the comparison of some of the EMG-PR classifiers using real-time amputee data is shown in Table 1 . All the achieved accuracy was demonstrated only in ideal research settings. Among the many classifiers ( Figure 1 ) in myoelectric control, LDA seems to be widely used classifiers. On the other hand, SVM and KNN, due to their kernel trick characteristics and non-parametric nature BIB002 , respectively, have equally been used widely. Though the better performance was achieved with many classifiers, high-density surface EMG is impractical to use as a source for real-time control. 