Gesture recognition framework for upper-limb prosthetics using entropy features from electromyographic signals and a Gaussian kernel SVM classifier

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

Wavelet denoising algorithms have been received considerable attention in the removal of noises of surface electromyography (sEMG) signal. Wavelet denoising algorithms proposed by Donoho's method is more often used in sEMG signal. However, Donoho's method is limited especially for multifunction myoelectric control. It does not only remove noises but it also removes some important part of sEMG signals. This study proposes an improved threshold estimation method. Six modified threshold estimation methods associated with the selected thresholding rescaling are evaluated. SEMG signal from six hand motions with additive WGN at various signal-to-noise ratios (SNRs) were applied to evaluate the efficient of method. Features of the estimated signal are sent to classification task. Evaluations of the performance of these algorithms are mean squared error (MSE) and classification rate. The results show that Global Scale Modified Universal (GSMU) method provides better performance than traditional Donoho's method. It produces sEMG signals that remain important information of the original sEMG signal and can eliminate lots of noises. The average MSE are 0.0024 at 20 dB SNR, low noise, and 0.074 at 0 dB, high noise. The accuracy of hand movement recognition of sEMG signal that estimates from GSMU is improved. It improves 1 to 4% of the classification accuracy depend on level of noise. In addition, performance of level dependent method is better than the others rescaling method. In the experiment, GSMU threshold estimation method is an efficient method for producing useful sEMG signal without noise and improving the application of hand movement recognition.

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

This paper investigates the output‐constrained control problem for physical human–robot interaction using electromyography signals. An interaction force estimation method based on surface electromyography (sEMG) signals is introduced, where random forest is provided to extract interaction force information from sEMG signals. Using sEMG, the interaction force between the robotic and human operator can be separated from that in the environment. An adaptive optimal tracking controller based on robot velocity prediction error feedback is designed, in which an asymmetric time‐varying integral barrier Lyapunov function is used to deal with dynamic spatial constraints. Finally, stability analysis is provided, and the effectiveness is verified by experiments based on a 7‐degree‐of‐freedom Franka Panda robot.

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

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

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.

Physical actions classification of surface electromyography (sEMG) signal is required in applications like prosthesis, and robotic control etc. In this paper, tunable-Q factor wavelet transform (TQWT) based algorithm is proposed for the classification of physical actions such as clapping, hugging, bowing, handshaking, standing, running, jumping, waving, seating, and walking. sEMG signal is decomposed into sub-bands by TQWT. Various features are extracted from each different band and statistical analysis is performed. These features are fed into multi-class least squares support vector machine classifier using two non-linear kernel functions, morlet wavelet function, and radial basis function. The proposed method is an attempt for classifying physical actions using TQWT and its performance and results are promising and have high classification accuracy of 97.74% for sub-band eight with morlet kernel function.

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.

Wearable devices are widely utilized in the field of health monitoring. Given the real-time requirements of wearable devices for dynamic tracking of muscle fatigue, and addressing the issues of prolonged computation time and failure to extract fatigue-related modal information when applying the variational mode decomposition (VMD) algorithm to surface electromyography (sEMG) signals, this paper proposes the fast VMD (FVMD) algorithm. The objective was to rapidly decompose fatigue-related modal information and extract a complete fatigue curve to alert users. The proposed algorithm is an engineering acceleration of the VMD algorithm. FVMD extends the original signal and applies the Fourier transform to convert the time-domain variational problem into the frequency domain. The optimization problem was constrained to the positive frequency range to simplify calculations while leveraging the unilateral spectrum characteristics to streamline optimization and focus on narrowband modes. The alternating direction method of multipliers framework was employed to decompose the problem into subproblems solvable in closed form, with modal updates inspired by Wiener filtering. The Lagrange multipliers were iteratively updated, and convergence criteria were established to ensure stability. The time-domain signal was reconstructed via the inverse Fourier transform. According to the experimental results, the proposed algorithm exhibits a substantial improvement in processing time compared to the original algorithm and other enhanced algorithms. Compared with other VMD variant algorithms using the same experimental data, the fast recursive VMD (FRVMD) algorithm takes 9.93 s. In contrast, the FVMD method can complete the same task in a shorter time. An evaluation metric was used to select the muscle fatigue modal component most correlated with the original signal and extract the muscle fatigue curve. The FVMD algorithm enhances computational efficiency, overcoming the computational limitations of wearable devices, and provides reliable technical support for real-time muscle fatigue quantification and early warning in scenarios such as sports rehabilitation and occupational health.

This study explores the integration of surface electromyography (sEMG) signals for precise servo control in real-time hand movement detection. It also further utilizes the feedback system designed to measure effects of workout exercises on forearm sEMG signals and delves into how different time and types of workouts affect forearm muscle response. Medical electrodes captured sEMG signals from forearm muscles and converted them into digital signals using an Arduino UNO R3 microcontroller board and an Olimex EMG shield to monitor hand pose variations. The digital signals were then processed to calculate the root mean square (RMS) voltage, which was then used to control the position of a servo motor. The implementation of a proportional-derivative (PD) controller further enhanced the accuracy and stability of the servo movements. Then the feedback system was used to measure how sEMG signals vary due to hand opening and closing before and after a range of workout exercises with varying time and workout types, and the results were compared to reach the conclusion that both workout time and type influences how forearm muscles respond to hand closing and opening. Future work may explore the effect of more time and types of workouts on muscle response, expand the range of test subjects, and improve the accuracy and stability of the test system

The main objective of this work is to establish a framework for processing and evaluating the lower limb electromyography (EMG) signals ready to be fed to a rehabilitation robot. We design and build a knee rehabilitation robot that works with surface EMG (sEMG) signals. In our device, the muscle forces are estimated from sEMG signals using several machine learning techniques, i.e. support vector machine (SVM), support vector regression (SVR) and random forest (RF). In order to improve the estimation accuracy, we devise genetic algorithm (GA) for parameter optimisation and feature extraction within the proposed methods. At the same time, a load cell and a wearable inertial measurement unit (IMU) are mounted on the robot to measure the muscle force and knee joint angle, respectively. Various performance measures have been employed to assess the performance of the proposed system. Our extensive experiments and comparison with related works revealed a high estimation accuracy of 98.67% for lower limb muscles. The main advantage of the proposed techniques is high estimation accuracy leading to improved performance of the therapy while muscle models become especially sensitive to the tendon stiffness and the slack length.Graphical