Ingestive Pattern Recognition on Cattle Using EMG Segmentation and Feature Extraction

The dexterity and coordinated movement of the hand play a very important role in people’s daily life and interpersonal communication. For patients with hand amputation, there are many inconveniences in life. However, prosthetic limb can help them to carry out daily life in accordance with their own intention to meet the basic grasp and interpersonal gesture needs of daily life, which can greatly improve the quality of life of patients. The bionic manipulator is such a humanoid prosthetic limb for the rehabilitation of amputees and is controlled by the surface electromyography (sEMG) signals on the surface of the human body. In this paper, a wearable and portable gesture recognition system based on pattern recognition algorithm is established to control the bionic manipulator in real time. We enhanced the accuracy in each step of signal acquisition and processing. By analyzing the mechanism and characteristics of sEMG signals, we determined the gestures to be identified, the areas of muscle, the number of electrodes, and the placement positions. Our self-developed acquisition device was used to acquire 4-channel forearm sEMG signals and data preprocessing, window sliding, feature extraction, gesture classification were conducted.

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

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

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.

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

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

Patterns of Jaw Reflexes Induced by Incisal and Molar Pressure Stimulation in Relation to Background Levels of Jaw-Clenching Force in Humans.

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.

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) 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 aim of this study was to verify if patients with arthrogenous temporomandibular disorders (TMD) with different Magnetic Resonance Imaging (MRI) diagnoses had some objective differences in the surface electromyography (sEMG) characteristics of their masticatory muscles. Twenty-four TMD patients were categorized according to the RDC/TMD [1]; MRI classified patients with disk displacement (DD, mean age 22 years, SD 5; M/F: 3/6), and osteoarthrosis and/or disk displacement (OA, mean age 37 years, SD 10; M/F: 4/11). sEMG of the right and left masseter and anterior temporalis muscles was performed according to a standardized protocol, recording teeth clenching on either cotton rolls or occlusal surfaces [2]. EMG data were compared to those collected in control subjects of similar age and sex, and EMG z-scores were computed. The comparison of EMG z-scores and MRI scores between the 2 patient groups using the Mann-Whitney test was statistically significant (P < 0.05): the patients with OA had larger scores than the patients with only DD. The linear correlation analysis run between the EMG and the MRI scores found significant correlations in both patient groups. The EMG characteristics allowed to well differentiate patients with MRI diagnosis of DD or OA. The objective recording of the masticatory muscle function and dysfunction through sEMG can be a first simple and low cost diagnostic approach to TMD patients.

Recently, deep learning has become a promising technique for constructing gesture recognition classifiers from surface electromyography (sEMG) signals in human–computer interaction. In this paper, we propose a gesture recognition method with sEMG signals based on a deep multi-parallel convolutional neural network (CNN), which solves the problem that traditional machine learning methods may lose too much useful information during feature extraction. CNNs provide an efficient way to constrain the complexity of feedforward neural networks by weight sharing and restriction to local connections. Sophisticated feature extraction is to be avoided and hand gestures are to be classified directly. A multi-parallel and multi-convolution layer convolution structure is proposed to classify hand gestures. Experiment results show that in comparison with five traditional machine learning methods, the proposed method could achieve higher accuracy.

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

Introduction: Aging impacts masticatory and swallowing functions due to muscle atrophy, neural degeneration, and reduced endurance. This study evaluated age-related differences in masticatory muscle function using the 6-min mastication test (6MMT) with surface electromyography (SEMG).Methodology: The study included two groups: healthy young adults (18–35 years) and healthy older adults (60 years and above). Muscle activity of the masseter and submental muscles was recorded using electrodes during mastication. Participants were instructed to chew bubble gum in synchronization with a metronome set at 80 bpm, with verbal reinforcement provided every minute.Results: Results revealed significant age-related differences in the masseter muscle for all parameters and in the submental muscle for peak values. Older adults demonstrated reduced masticatory muscle performance, attributed to sarcopenia, neural degeneration, and increased noncontractile tissue. Within-group analysis showed progressive changes in submental muscle mean average and power values over the 6-min task, reflecting fatigue, while no significant differences were observed in masseter muscle parameters in healthy young adults, possibly due to a warm-up effect or compensatory motor unit recruitment.Conclusion: The study demonstrated that masseter muscle activity significantly declines with age, with large effect sizes observed for all the parameters. Additionally, fatigue-related reductions in submental muscle parameters were observed across both age groups. These findings suggest that age-related and task-induced fatigue differentially affect masticatory muscles, highlighting the need for tailored interventions to support oral function in older adults.