A Wearable and Portable Real-Time Control Gesture Recognition System for Bionic Manipulator

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.

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

Amputation of the upper limb brings heavy burden to amputees, reduces their quality of life, and limits their performance in activities of daily life. The realization of natural control for prosthetic hands is crucial to improving the quality of life of amputees. Surface electromyography (sEMG) signal is one of the most widely used biological signals for the prediction of upper limb motor intention, which is an essential element of the control systems of prosthetic hands. The conversion of sEMG signals into effective control signals often requires a lot of computational power and complex process. Existing commercial prosthetic hands can only provide natural control for very few active degrees of freedom. Deep learning (DL) has performed surprisingly well in the development of intelligent systems in recent years. The significant improvement of hardware equipment and the continuous emergence of large data sets of sEMG have also boosted the DL research in sEMG signal processing. DL can effectively improve the accuracy of sEMG pattern recognition and reduce the influence of interference factors. This paper analyzes the applicability and efficiency of DL in sEMG-based gesture recognition and reviews the key techniques of DL-based sEMG pattern recognition for the prosthetic hand, including signal acquisition, signal preprocessing, feature extraction, classification of patterns, post-processing, and performance evaluation. Finally, the current challenges and future prospects in clinical application of these techniques are outlined and discussed.

Surface electromyography (sEMG) signals reflect the local electrical activity of muscle fibers and the synergistic action of the overall muscle group, making them useful for gesture control of myoelectric manipulators. In recent years, deep learning methods have increasingly been applied to sEMG gesture recognition due to their powerful automatic feature extraction capabilities. sEMG signals contain rich local details and global patterns, but single-scale convolutional networks are limited in their ability to capture both comprehensively, which restricts model performance. This paper proposes a deep learning model based on multi-scale feature fusion-MS-CLSTM (MS Block-ResCBAM-Bi-LSTM). The MS Block extracts local details, global patterns, and inter-channel correlations in sEMG signals using convolutional kernels of different scales. The ResCBAM, which integrates CBAM and Simple-ResNet, enhances attention to key gesture information while alleviating overfitting issues common in small-sample datasets. Experimental results demonstrate that the MS-CLSTM model achieves recognition accuracies of 86.66% and 83.27% on the Ninapro DB2 and DB4 datasets, respectively, and the accuracy can reach 89% in real-time myoelectric manipulator gesture prediction experiments. The proposed model exhibits superior performance in sEMG gesture recognition tasks, offering an effective solution for applications in prosthetic hand control, robotic control, and other human-computer interaction fields.

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

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

At the present time, since mobile devices such as tablet-type PCs and smart phones have widely penetrated into our daily lives, an authentication method that prevents shoulder surfing attacks comes to be important. We are investigating a new user authentication method for mobile devices that uses surface electromyogram (s-EMG) signals, not screen touching. The s-EMG signals, which are generated by the electrical activity of muscle fibers during contraction, can be detected over the skin surface, and muscle movement can be differentiated by analyzing the s-EMG signals. Taking advantage of the characteristics, we proposed a method that uses a list of gestures as a password in the previous study. In order to realize this method, we have to prepare a sufficient number of gestures that are used to compose passwords. In this paper, we adopted fingerspelling as candidates of such gestures. We measured s-EMG signals of manual kana of The Japanese Sign Language syllabary and evaluated their potential as the important element of the user authentication method.

This work presents a non-invasive method to identify the ingestive behavior in ruminants using Surface Electromyography (sEMG) of the masseter muscle. It was evaluate whether the rumination and intake process could be distinguished using sEMG signal features and machine learning techniques. To collect chewing sEMG signal, superficial Ag/AgCl electrodes were placed on two ruminant animals masseter muscle and the data was sampled during eating with an analog-to-digital converter. Three segmentation techniques was explored and applied to automatically subdivide the chewing movement signal. Five classifiers (k-nn, LDA, SVM, NB and DT) were evaluated using four features (RMS, SSC, ZC and WL) extracted from the signal. We found and accuracy over 93% using a fixed length segmentation method and k-nn for classification with \( k > 7 \). Future works may explores the implementation of time series analysis to improve the classifier performance.

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

Selection of suitable features plays a pivotal role in Electromyography pattern recognition (EMG-PR) based system designing. Time-domain features are widely used in EMG-PR based application and show improved proficiency in the development of rehabilitation robotics. Even though, the performance of existing features is not satisfactory. In this study, we proposed four novel time-domain features obtained by using first-order differentiation of original surface electromyogram (sEMG) signals feature. Here, sEMG signals were acquired from ten healthy volunteers with the help of myotrace400 device for six different arm movements. The data acquisition and pre-processing stage were carried out followed by the feature extraction process for better classification results. Four different classifiers namely, k-nearest neighbors (KNN), Linear discriminant analysis (LDA), Quadratic discriminant analysis (QDA) and Medium tree (MT) classifiers were utilized for the performance evaluation of proposed and conventional features. Experimental results demonstrate that proposed features extracted by using first-order differentiation of sEMG signals feature attained better classification accuracy with MT classifier as compared to the feature extracted from original sEMG signals with the conventional features. The accuracy of proposed feature based on first-order differentiation improved up to 6%. The results indicate that proposed features may be considered for developing the EMG-PR based system designing.

Surface electromyography (sEMG) is a non-invasive technique widely used to assess muscle activation and function. This study was aimed to investigate the relationship between sEMG signal features and hand grip strength in healthy individuals across different ages and genders. A total of 280 participants (122 males, 158 females), aged 20 years and above, were categorized into six age groups. Grip strength was measured using a digital hand dynamometer, and sEMG signals of four upper-limb muscles, biceps brachii, triceps brachii, brachioradialis, and flexor carpi ulnaris, were recorded at the same time. Root mean square (RMS) features were extracted from the sEMG signals to represent muscle activation. Pearson correlations were performed to analyze the relationship between RMS values and grip force. Results show a strong positive correlation between sEMG features and grip strength. These findings suggest that RMS-based sEMG analysis may provide a reliable, non-invasive indicator of grip strength and could aid in early detection of muscular decline in clinical or aging populations.

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

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

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.