Deep learning for surface electromyography artifact contamination type detection

PurposeSurface electromyography (sEMG) is vulnerable to environmental interference, low recognition rate and poor stability. Electrocardiogram (ECG) signals with rich information were introduced into sEMG to improve the recognition rate of fatigue assessment in the process of rehabilitation.MethodsTwenty subjects performed 150 min of Pilates rehabilitation exercise. Twenty subjects performed 150 min of Pilates rehabilitation exercise. ECG and sEMG signals were collected at the same time. Aftering necessary preprocessing, the classification model of improved particle swarm optimization support vector machine base on sEMG and ECG data fusion was established to identify three different fatigue states (Relaxed, Transition, Tired). The model effects of different classification algorithms (BPNN, KNN, LDA) and different fused data types were compared.ResultsIPSO-SVM had obvious advantages in the classification effect of sEMG and ECG signals, the average recognition rate was 87.83%. The recognition rates of sEMG and ECG fusion feature classification models were 94.25%, 92.25%, 94.25%. The recognition accuracy and model performance was significantly improved.ConclusionThe sEMG and ECG signal after feature fusion form a complementary mechanism. At the same time, IPOS-SVM can accurately detect the fatigue state in the process of Pilates rehabilitation. On the same model, the recognition effect of fusion of sEMG and ECG(Relaxed: 98.75%, Transition:92.25%, Tired:94.25%) is better than that of only using sEMG signal or ECGsignal. This study establishes technical support for establishing relevant man–machine devices and improving the safety of Pilates rehabilitation.

Accurate prediction of lower limb joint angles is essential for enabling natural human-exoskeleton interaction in rehabilitation robotics. This study proposes a novel framework for knee joint angle prediction using surface electromyography (sEMG) signals, integrating an XGBoost-driven feature selection algorithm and a multiattention hybrid-enhanced long short-term memory (LSTM) network. First, sEMG signals were acquired from healthy participants during dynamic lower limb movements. After preprocessing, temporal and spectral features were extracted, after which the eXtreme Gradient Boosting (XGBoost) algorithm was applied to eliminate redundant features, reducing input dimensionality while maintaining predictive accuracy. Finally, the reduced features were fed into the proposed model, which leverages hybrid attention mechanisms to enhance temporal dependencies and feature relevance. The experimental results validate that the XGBoost-driven feature selection framework significantly minimizes redundancy in sEMG feature extraction. When evaluating the performance of joint angle prediction, the mean absolute error (MAE), root mean square error (RMSE), adjusted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R}^{{2}}$ </tex-math></inline-formula>, and Pearson correlation coefficient (CC) of the proposed model were 2.47°, 3.55°, 0.95, and 0.98, outperforming traditional machine learning (ML) algorithms and the benchmarks CNN, LSTM, TCN, and CNN-BiLSTM. The framework’s superior computational efficiency and prediction accuracy highlight its potential for real-time implementation in exoskeleton systems, addressing critical limitations in existing control paradigms. This advancement paves the way for adaptive human-robot collaboration in clinical rehabilitation settings.

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

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

In response to the need for an exoskeleton to quickly identify the wearer’s movement mode in the mixed control mode, this paper studies the impact of different feature parameters of the surface electromyography (sEMG) signal on the accuracy of human motion pattern recognition using multilayer perceptrons and long short-term memory (LSTM) neural networks. The sEMG signals are extracted from the seven common human motion patterns in daily life, and the time domain and frequency domain features are extracted to build a feature parameter dataset for training the classifier. Recognition of human lower extremity movement patterns based on multilayer perceptrons and the LSTM neural network were carried out, and the final recognition accuracy rates of different feature parameters and different classifier model parameters were compared in the process of establishing the dataset. The experimental results show that the best accuracy rate of human motion pattern recognition using multilayer perceptrons is 95.53%, and the best accuracy rate of human motion pattern recognition using the LSTM neural network is 96.57%.

Freely available dataset of long-lasting surface electromyographic signals during walking

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

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

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

The variation in the distributions of recorded data between individuals leads to low classification accuracy. To address this issue, we introduce a multimodal fusion convolutional neural network (MFCNN). This network extracts common information from surface electromyography (sEMG) and accelerometer signals of different subjects using a two-stream convolutional neural network (CNN). To enhance the classification accuracy of a particular subject, a fine-tuning approach was implemented. The performance of the proposed method was assessed in four different scenarios, which include intersubject classification, intersubject classification when training data from multiple subjects, fine-tuned intersubject classification, and fine-tuned intersubject classification when training data from multiple subjects. The results demonstrate that in the intersubject scenario, when multiple subjects are available for training, the MFCNN achieves higher classification accuracy ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${p} &lt; 0.05$ </tex-math></inline-formula> ) than other neural networks and support vector machines (SVMs) that use sEMG signals [neural network (NN) and SVM], accelerometer signals (accNN and accSVM), sEMG and accelerometer signals [multimodal fusion nerual network (MFNN) and multimodal fusion support vector machine (MFSVM)] as inputs, as well as a CNN that uses sEMG signals as input after fine-tuning. Furthermore, compared with an MFCNN model trained with data from a single subject and an accCNN model trained with data from a single subject or multiple subjects, an MFCNN trained with multiple subjects demonstrated better performance on new subjects after fine-tuning ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${p} &lt; 0.05$ </tex-math></inline-formula> ). This method can learn common features among different subjects and improve the performance of classification among subjects. Our proposed method demonstrates the innovation of using a multimodal fusion approach and two-stream CNN to improve intersubject classification accuracy in upper limb movements.

The EMG signals acquired from acupoint Fu Tu (a motor point in rectus femoris) were reported to be more stable and reproducible than those from the middle point between greater trochanter to tibio-femoral junction of knee in standing position. It was different from the concept of better surface EMG signal from muscle belly than motor point.For studying whether the motor point is a better surface electrode location for stability and reproducibility of EMG signal, 6 normal subjects were examined at right quadriceps in isometric and isokinetic contractions by Cybex 340 dynamometer. The active electrodes of surface electromyography (S-EMG) were placed at midpoint of the rectus femoris and the motor point of rectus femoris for comparison. The integrated EMG (IEMG) signals were recorded and stored by Myosystem 1000. The result in this study showed that surface integrated EMG (S-IEMG) signals from the midpoint of the muscle was bigger in amplitude, more stable in repetitive measurements in within day as well as in between days.The results do not support the motor

In human-machine interaction systems, continuous movement estimation methods occupy an important position because they are more natural and intuitive than pattern-recognition methods. Essentially, arm position is decided by the shoulder and elbow joint angles. However, the various deformations of muscles around the shoulder and elbow often lead to difficulties in sensor fixation, which results in a loss of synchronization between the surface electromyography (sEMG) signals and joint angles. In order to accurately estimate movement angles using sEMG in situations where the sEMG is not synchronized with joint angles, we utilized a bi-directional long short-term memory (Bi-LSTM) network rather than other deep learning methods to estimate non-dominant arm movements, based on the sEMG signal from the dominant arm. This estimation protocol was designed to avoid a multiplicity of sensors and to simulate more complicated loss of synchronization problems). The performance of the Bi-LSTM was compared with multilayer perceptrons (MLPs), convolutional neural networks (CNNs), and a long short-term memory network (LSTM). The Pearson correlation coefficient (cc) between the estimated and target joint angle sequences was calculated to evaluate the performance of each neural network. The Wilcoxon signed-rank results showed that the Bi-LSTM model significantly outperformed the MLP, CNN, and LSTM models (tested with completely untrained newly recorded free movements).

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.

Prosthetic hands help patients gain greater confidence in daily life, and enabling automatic control of prosthetic hands using real biosignals is an essential task. This paper proposes a prosthetic hand control system based on surface electromyography (sEMG) signals. In particular, the sEMG signals are collected in real time corresponding to each hand gesture and pre-processed to eliminate noise components. A deep learning network is then employed to recognize hand gestures from the pre-processed sEMG signals. Recognized gestures are subsequently used to control the prosthetic hand to perform the corresponding movements. The proposed deep learning network achieves a recognition accuracy of 98.15 % for hand gestures from sEMG signals. Furthermore, the experimental results demonstrate that the proposed system can control the prosthetic hand with an accuracy of up to 96.97 % and a variance of 3.2194 across multiple subjects. These results suggest that the proposed system holds great potential for real-time prosthetic hand control based on sEMG signals, thereby supporting patients in gaining confidence in social interactions.

Traditional machine learning models are common to classify Electrocardiogram (ECG) signals. In comparison, ECG prediction with such models is rare. The proposed work aims to predict the RR Interval (RRI) for a 10 sec. Normal Sinus Rhythm (NSR) ECG signals by designing and developing a Long Short Term Memory (LSTM)-Recurrent Neural Network (RNN) based model. The ECG signals from the Chapman University and Shaoxing People's hospital have 10 sec. of sample data of each patient used in this study. Signal processing techniques to detect the R-peaks and RRI were calculated further and provided as the model's input. LSTM-RNN model based on univariate time series predicted the RRI for the subsequent 20 ECG cycles. The model underwent 500 epochs of training before the accurate ECG prediction. The model's error value started at 0.8 and ended at 0.000001, which provided a good fit for the predicted data. The LSTM-RNN model predicted the RRI from the 10th to the 30th ECG cycle based on 9 RRI. The model's error rate decreased exponentially as the epoch's number increased, stating the model's accuracy, helping to determine the intra-individual Heart Rate Variability (HRV).