Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals

Cross-correlation between surface electromyogram (EMG) signals is commonly used as a means of quantifying EMG cross talk during voluntary activation. To examine the reliability of this method, the relationship between cross talk and the cross-correlation between surface EMG signals was examined by using model simulation. The simulation results illustrate an increase in cross talk with increasing subcutaneous fat thickness. The results also indicate that the cross-correlation function decays more rapidly with increasing distance from the active fibers than cross talk, which was defined as the normalized EMG amplitude during activation of a single muscle. The influence of common drive and short-term motor unit synchronization on the cross-correlation between surface EMG signals was also examined. While common drive did not alter the maximum value of the cross-correlation function, the correlation increased with increasing motor unit synchronization. It is concluded that cross-correlation analysis is not a suitable means of quantifying cross talk or of distinguishing between cross talk and coactivation during voluntary contraction. Furthermore, it is possible that a high correlation between surface EMG signals may reflect an association between motor unit firing times, for example due to motor unit synchronization.

Electromyogram (EMG) signals are biopotential signals, which occur as the result of electro-chemical circumstances dependent on contraction of muscles. In this study, a new method is presented to determine the motor unit action potentials of EMG signals recorded with surface electrodes. Compared to needle EMG signals, surface EMG signals have more active noise component. Results, which are gained by the process train including wavelet de-noising method for noise anlysis at first step and then ICA algorithms to provide independent components, form four different datas to determine the motor unit action potentials. Performed study, which is aimed to make ineffective the problems that make the analysis of EMG signals difficult and then to reach the desired results, provides simultaneous comparison possibility submitting four results. After the determination of the results, with the feedback between input and output, examination of accuracy ratio of results are possible and this examination eliminates the questions about the accuracy of the study.

The surface electromyogram (EMG) signal collected from multiple channels has frequently been investigated for use in controlling upper-limb prostheses. One common control method is EMG-based motion classification. Time and frequency features derived from the EMG have been investigated. We propose the use of EMG signal whitening as a preprocessing step in EMG-based motion classification. Whitening decorrelates the EMG signal, and has been shown to be advantageous in other EMG applications. In a ten-subject study of up to 11 motion classes and ten electrode channels, we found that whitening improved classification accuracy by approximately 5% when small window length durations (<100ms) were considered.

to the editor: The electromyogram (EMG) as an indirect measure of motor unit activity has proved invaluable in motor control studies. Increasingly, the application of frequency domain techniques to EMG signals has provided important insight into the structure and function of the human motor system.

Wearable e-health system, are frequently used for monitoring biomedical signals. These devices need to have advanced and applicable methods of feature selection and classifications for real time applications. Electromyogram (EMG) signal records the movement of the human muscle. EMG signal processing techniques aim to achieve the actual signal and among others, detect the state of signals related to positive and negative emotional expression. In our study, the data collected is from the facial muscle activity that is produced by the emotion of the facial expressions. The key challenge is in finding an accurate classification method of the measured signals. This paper investigates the promising techniques for the detection and classification of EMG signal using machine-learning theory. Here, we demonstrated Support Vector Machine (SVM) is an optimal method for classification of facial surface Electromyogram (sEMG) signal associated to pain dataset. The test results and the methods are able to analyze the patterns recognition of facial EMG signal classification. The result and the findings 99% accuracy with SVM method adds value on the classification algorithms of our EMG signal acquisitions platform.

Upper limb prosthesis control with pattern recognition (PR)- based on surface Electromyogram (EMG) signals has been heavily investigated in the literature. However, challenges related to signal non-stationarity and its impact by changing force levels, limb positions and many other factors all lead to poor system stability, prevent the widespread adoption of this technology. In this study, we propose an alternative modality based on Acoustic Myography (AMG), a recording of muscle vibrations at low frequencies, as a control signal to decipher the intended hand movements. A custom-built AMG armband, consisting of 4 microphones, has been developed in this study and evaluated on intact-limbed subjects performing six classes of hand and finger movements. Time domain and auto-regression (TD-AR) features, in addition to Bi long-short term memory (BiLSTM) deep learning classifier were utilized to perform the classification. An average classification accuracy of 85% was obtained, which shows the potential of using the developed AMG armband, with PR system for prostheses control.

This paper describes the estimation and analysis of variance distribution of surface electromyogram (EMG) signals based on a stochastic EMG model. With the assumption that EMG signals at a certain time follow Gaussian distribution, their variance is handled as a random variable that follows inverse gamma distribution, and noise superimposed onto this variance can be expressed accordingly. The paper proposes variance distribution estimation based on marginal likelihood maximization of EMG signals. A simulation experiment using artificially generated signals to verify its accuracy indicated that the method can estimate variance distribution with high accuracy for a wide range of variance distribution shaping. Analysis of variance distribution using measured EMG signals revealed the relationship between muscle force and variance distribution involving signal-dependent noise.

Bioelectric signals are used to measure electrical potential, but there are different types of signals. The electromyography (EMG) is a type of bioelectric signal used to monitor and recode the electrical activity of the muscles. The current work aims to model and reproduce surface EMG (SEMG) signals using an artificial neural network. Such research can aid studies into life enhancement for those suffering from damage or disease affecting their nervous system. The SEMG signal is collected from the surface above the bicep muscle through dynamic (concentric and eccentric) contraction with various loads. In this paper, we use time domain features to analyze the relationship between the amplitude of SEMG signals and the load. We extract some features (e.g., mean absolute value, root mean square, variance and standard deviation) from the collected SEMG signals to estimate the bicep’ muscle force for the various loads. Further, we use the R-squared value to depict the correlation between the SEMG amplitude and the muscle loads by linear fitting. The best performance the ANN model with 60 hidden neurons for three loads used (3 kg, 5 kg and 7 kg) has given a mean square error of 1.145, 1.3659 and 1.4238, respectively. The R-squared observed are 0.9993, 0.99999 and 0.99999 for predicting (reproduction step) of smooth SEMG signals.

Pattern-recognition-based myoelectric control systems are not yet widely available due to their limited robustness in real-life situations. Some postprocessing methods were introduced to improve the robustness in previous studies, but there is lack of investigation into movement transition phases. This article presents a novel postprocessing method based on movement pattern transition (MPT) detection. An image-based index is used to quantify the similarity of adjacent feature matrices from high-density surface electromyogram (EMG) signals. MPT detection is implemented by applying a double threshold to the calculated index. The proposed postprocessing method is used to rectify the EMG pattern recognition decisions from the classifier by incorporating the detected information. Two representative testing schemes are used to verify the robustness of the proposed method against force level variation and consecutive nonstop task performance. The proposed method achieved mean classification accuracy improvements of 7.33% and 10.91% with respect to the baseline performance of a raw classifier (without any postprocessing) in the two testing schemes. It also outperformed other common postprocessing methods ( p < 0.05). Considering both the accuracy improvement and time efficiency for rapid responses to MPT, the proposed method could be a potential option for postprocessing to enhance the robustness of myoelectric control.

Despite not recording directly from neural cells, the surface electromyogram (EMG) signal contains information on the neural drive to muscles, i.e., the spike trains of motor neurons. Using this property, myoelectric control consists of the recording of EMG signals for extracting control signals to command external devices, such as hand prostheses. In commercial control systems, the intensity of muscle activity is extracted from the EMG and used for single degrees of freedom activation (direct control). Over the past 60 years, academic research has progressed to more sophisticated approaches but, surprisingly, none of these academic achievements has been implemented in commercial systems so far. We provide an overview of both commercial and academic myoelectric control systems and we analyze their performance with respect to the characteristics of the ideal myocontroller. Classic and relatively novel academic methods are described, including techniques for simultaneous and proportional control of multiple degrees of freedom and the use of individual motor neuron spike trains for direct control. The conclusion is that the gap between industry and academia is due to the relatively small functional improvement in daily situations that academic systems offer, despite the promising laboratory results, at the expense of a substantial reduction in robustness. None of the systems so far proposed in the literature fulfills all the important criteria needed for widespread acceptance by the patients, i.e. intuitive, closed-loop, adaptive, and robust real-time ( 200 ms delay) control, minimal number of recording electrodes with low sensitivity to repositioning, minimal training, limited complexity and low consumption. Nonetheless, in recent years, important efforts have been invested in matching these criteria, with relevant steps forwards.

Assessment of low back muscle fatigue by surface EMG signal analysis: methodological aspects

This paper presents the estimation and analysis of surface electromyogram (EMG) signals during fatiguing contractions based on a stochastic EMG model. In the model, the probability distribution of EMG signals is assumed to be a mixture of Gaussians with the same mean but different variances, facilitating the representation of the variance distribution of EMG signals. The paper proposes a continuous estimation method for variance distribution parameters using a sliding window, enabling the evaluation of the time-varying stochastic properties of EMG signals. Estimation experiments were conducted on six healthy young adults to analyze changes in EMG variance distribution with the progression of muscle fatigue. The results reveal the linear and nonlinear relationships between muscle fatigue and variance distribution parameters.

Accurate and real-time estimation of force from surface electromyogram (EMG) signals enables a variety of applications. We developed and validated new approaches for selecting subsets of high-density (HD) EMG channels for improved and lower-dimensionality force estimation. First, a large dataset was recorded from a number of participants performing isometric contractions in different postures, while simultaneously recording HD-EMG channels and ground-truth force. The EMG signals were acquired from three linear surface electrode arrays, each with eight monopolar channels, and were placed on the long head and short head of the biceps brachii and brachioradialis. After data collection and pre-processing, fast orthogonal search (FOS) was employed for force estimation. To select a subset of channels, principal component analysis (PCA) in the frequency domain and a novel index called the power-correlation ratio (PCR), which maximizes the spectral power while minimizing similarity to other channels, were used. These approaches were compared to channel selection using time-domain PCA. We selected one, two, and three channels per muscle from the original seven differential channels to reduce the redundancy and correlation in the dataset. In the best case, we achieved an approximate improvement of for force estimation while reducing the dimensionality by for a subset of three channels.

The aim of this work was to reduce ECG artifacts from surface electromyogram (EMG) signals collected from lumbar muscles with the blind source separation technique based on independent component analysis (ICA). Using four EMG signals collected above erector spinal lumbar muscles from 27 subjects, the proposed method fail in separating the sources. However, when considering a single channel of EMG and the same one time-shifted by one sample, the FastICA allowed reducing the signal to ECG noise ratio.

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.