SEMG signal classification with novel feature extraction using different machine learning approaches

The abundant movement information carried by the surface electromyography (sEMG) signal can comprehensively reflect the muscle movement patterns on the surface of the human body, and the characteristics of physiological signals involving movement information have promoted the vigorous development in the field of human-computer interaction in machine learning. This paper takes subtle gestures as the research objects, and proposes a subtle gestures recognition system that uses two-channel sEMG signal and utilizes Quadratic Discriminant Analysis (QDA) and Linear Discriminant Analysis (LDA) which are parameter estimation classifiers based on probability density. First, combining the raw sEMG signal and the envelope sEMG signal, the number of channels for sensors and corresponding position of the muscle groups are preferably selected. Then, we propose to use the Pearson correlation coefficient to optimize the four types of features extracted in the time domain, frequency domain, time-frequency domain and AR model parameters, which effectively ensures the uniqueness of each gesture and eliminates individual differences. Finally, this paper compares four classifiers of QDA, LDA, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), and explores the pattern recognition classifier suitable for subtle actions. The experiment collects 9 kinds of subtle gestures from 8 subjects. The results show that the system can use only two channels of sEMG signal to recognize 9 kinds of subtle gestures which include multi-finger clicking or pinching or holding objects, and the most suitable classifiers QDA and LDA have the average recognition rates of 95.79% and 95.01%, respectively.

Atrophy is one of the most common consequences of muscle disorder. This could be a result of both myopathy and neuropathy. Muscle atrophy becomes more possible as people age. As a result of this disorder, the amount and size of muscle fibers decrease, therefore a person cannot produce high amount of force in his/her muscles, leading to difficulties in handling daily activities. The main purpose of this research is to find a way to predict this disorder. In this study the force classification was used for the atrophy disorder detection. The results show that different classifiers and features from the proposed ones, work for this purpose. To approach this goal, data were collected by recording surface EMG (sEMG) signals. Processing the recorded signals, best features with respect to more accuracy and less calculation complexity were selected and reported. After extracting the features from each patient with using different types of classifiers including LDA (Linear Discriminant Analysis), QDA (Quadratic Discriminant Analysis) and SVM (Support Vector Machine), the best approach to separate normal and atrophic people was investigated. It is found that unlike the proposed features such as MAV (Mean Absolute Value), SSC (Slope Sign Change) and WL (Waveform Length) in upper limb movement classification, three features WL, WAMP (Wilson Amplitude) (time domain features) and MNP (Mean Power) (frequency domain feature) show better performance for atrophy characterization. The results show that these features well predict the detection of biceps atrophy.

Comparative analysis of SVM and Naive Bayes classifier for the SEMG signal classification

Single-channel surface electromyography signal classification with variational mode decomposition and entropy feature for lower limb movements recognition

A physiological measures-based method for detecting inattention in drivers using machine learning approach

Feature extraction from an recorded surface electromyography (sEMG) signal plays an important role in identifying and quantifying the characteristics of muscle activities. These features can be used for various applications like muscle function assessment, muscle fatigue detection, etc. Common features extracted from sEMG signal are time-domain or frequency-domain features. However, features which are sensitive to uncertainties in the signal like noise, movement artifacts, and outliers should be avoided. Autocorrelation function (ACF), which is a measure of similarity between a signal and its time delayed version, is considered in this work as a feature to overcome the impact of noise, artifacts, and outliers. An artificial neural network (ANN) is developed to differentiate between fatigue and non-fatigue conditions using the calculated ACF from sEMG segments. The performance of an ANN model that can be adapted by means of various regularization methods was investigated. The proposed ANN model achieved an accuracy of about 97.62 %, a precision of about 95.50 % and a sensitivity of about 100 % in the classification of fatigue and non-fatigue sEMG segments, outperforming k-means and linear support vector machine approaches that served as references.

GLEAM: A multimodal deep learning framework for chronic lower back pain detection using EEG and sEMG signals.

In recent years, low-cost, low-power myoelectric control systems such as the Myo armband from Thalmic Labs have become available and unlocked tremendous possibilities for myoelectric controlled applications. However, due to the embedded system constraints, such sEMG control devices typically samples sEMG signals at a lower frequency. It is in doubt whether existing sEMG feature extraction methods are still valid on such low-resolution sEMG data. In addition, the feature extraction algorithms implemented on embedded devices must have low computational complexity in order to meet the real-time requirement. This paper aims to investigate effective features for low-resolution EMG pattern recognition. In particular, a set of novel computational efficient space-domain (SD) features (referred to as simple SD (SSD) features) have been developed to exploit the spatial relationships of sEMG signals recorded from the sensor array on the Myo armband. The proposed SSD feature set was evaluated with a linear discriminant analysis (LDA)-based classifier on a 9-gesture dataset. The experimental results indicate that using the SSD features increased the classification accuracy by 5% compared to using Hudgins' time-domain features.

Surface electromyogram (sEMG) signals have been used to control multifunctional prosthetic hands. Researchers usually focused on the use of several channels with sEMG signals to identify more hand motions without limiting the number of sEMG sensors. However, the residual muscles of an amputee are limited. Therefore, the point of a successful recognition system is to decrease the channels of sEMG signals to classify more hand motions. To achieve this goal, we proposed a hand motion recognition system, in which three channels of sEMG signals can classify nine hand motions. In this recognition system, the time domain features, root mean square ratio (RMSR) and autoregressive (AR) model, were selected to extract the features of the sEMG signals as compared with the time-frequency domain features. Furthermore, the linear discriminant analysis (LDA) was adopted as the classifier. Consequently, the average accuracy rate of the presented system was 91.46%. Therefore, the proposed algorithms in this paper can be reasonably feasible for prosthetic hands.

Recognizing the breathing resistances of wearing respirators from respiratory and sEMG signals with artificial neural networks

A discriminant bispectrum feature for surface electromyogram signal classification

Surface electromyography (sEMG) signal is widely used in lower limb exoskeleton system because it can reflect human motion intention. Aiming at the problem of low decoding accuracy of joint motion angle of lower limb exoskeleton robot, a knee motion angle prediction method based on sEMG signal is proposed. Firstly, in order to solve the problem of high time efficiency and insufficient stability of single time domain feature extraction method for sEMG signal, the eigenvalues are extracted from time domain, frequency domain and time-frequency domain respectively, and the prediction error of different channel sEMG signal feature parameter combination model is analyzed. Then, an innovative particle swarm optimization LSTM deep learning algorithm (PSO-LSTM) is proposed to establish the mapping model between sEMG signal and knee joint angle, and determine the PSO-LSTM model corresponding to the optimal feature parameter combination to realize the accurate prediction of knee joint angle. Finally, the experimental results show that the predicted results of the model are highly consistent with the real angle values, which is helpful to improve the human-computer cooperation of the lower limb exoskeleton system.KeywordssEMG signalLower extremity exoskeleton robotIntention perceptionPSO-LSTM algorithm

This paper attempts to explore the feasibility of classifying relaxed and stressful mental states based on two-channel prefrontal EEG signal from 35 healthy human subjects. Specific objective of this paper is to explore the best choice of features and compare the performance of various feature classification algorithms suitable for this purpose. Here, we included different bivariate features in time domain and frequency domain and compared the classification performance of artificial neural network, linear discriminant analysis, quadratic discriminant analysis (QDA), K nearest neighbour and support vector machine algorithms. Common spatial patterns (CSP) algorithm was used successfully for feature reduction. Best classification performance (99.69%) was observed with the QDA classifier taking cross-correlation estimate as feature. We also explored the effect of combining different kinds of features, effect of varying the number of features on classifier performance, robustness of the chosen methods against in inter-individual variability and the feasibility of developing subject-independent classifiers.

This paper proposes a pattern recognition and classification algorithm based on a circular structure that can reflect the characteristics of the sEMG (surface electromyogram) signal measured in the arm without putting the placement limitation of electrodes. In order to recognize the same pattern at all times despite the electrode locations, the data acquisition of the circular structure is proposed so that all sEMG channels can be connected to one another. For the performance verification of the sEMG pattern recognition and classification using the developed algorithm, several experiments are conducted. First, although there are no differences in the sEMG signals themselves, the similar patterns are much better identified in the case of the circular structure algorithm than that of conventional linear ones. Second, a comparative analysis is shown with the supervised learning schemes such as MLP, CNN, and LSTM. In the results, the classification recognition accuracy of the circular structure is above 98% in all postures. It is much higher than the results obtained when the linear structure is used. The recognition difference between the circular and linear structures was the biggest with about 4% when the MLP network was used.

Continuous prediction of knee and ankle joint angles based on sEMG and hip joint angle fusion signals by NRBO-LSTM network