Surface EMG signals based elbow joint torque prediction

Identification of the innervation zone is widely used to optimize the accuracy and precision of noninvasive surface electromyography (EMG) signals because the EMG signal is strongly influenced by innervation zones. However, simply structured fusiform muscle, such as biceps brachii muscle, has been employed mainly due to the simplicity with which the propagation from raw EMG signals can be observed. In this study, the optimum electrode location (OEL), free from innervational influence, was investigated by the propagation pattern of action potentials for brachii muscles and more complicated deltoid muscle structures using an automatized signal analysis technique. The technique employed newly developed computer software with additional clinical uses and minimized subjective differences. EMG signals were recorded using surface array electrodes during voluntary isometric contractions obtained from 12 healthy male subjects. Peaks in EMG signals were detected and averaged for each muscle. The propagation patterns and OEL were examined from biceps brachii muscles for all subjects and from deltoid muscles for seven subjects. The estimated locations were partially confirmed by comparing the root mean squares of the EMG signals. These results show that propagation patterns and OEL could be estimated simply and automatically even from the surface EMG signals of deltoid muscles.

This paper proposes to monitor and recognize the fatigue state during football training by analyzing the surface electromyography (EMG) signals. The surface electromyography (EMG) signal is closely connected with the state during sports and training. First, power frequency interference, motion artifacts, and baseline drift in the surface electromyography (EMG) signal are removed; second, the authors extract 6 features: rectified average value (ARV), integrated electromyography myoelectric value (IEMG), root mean square of electromyography value (RMS), median frequency (MF), average power frequency (MPF), and electromyography power (TP) to represent the surface electromyography (EMG) signal; lastly, the extracted features are input into a one-class support vector machine to determine whether the player has been fatigued and are input into a weighted support vector machine to determine the degree of fatigue if the player has been fatigued. The experimental results show that more than 95% of the fatigue state can be recognized by surface electromyography (EMG) signal.

Relationships of Vibromyographic and Electromyographic Signals During Isometric Voluntary Contraction

This paper is motivated by works done in the area of robot-assisted stroke rehabilitation. The use of electromyographic (EMG) signal brings a new way of communication interface between user and robot. However, the EMG signal has to be transferred into useful information that serve as robot input. This paper presents a novel methodology for conversion of electromyographic (EMG) signal into estimated joint torque. Investigation of the proposed methodology covers human upper limb movement: shoulder flexion-extension, shoulder abduction-adduction, and elbow flexion-extension. Simulated annealing (SA) is implemented to obtain optimum model that maps EMG into estimated joint torque. General principle, design, and the implementation of SA for the problem are discussed in this paper. Experimentation was carried out to investigate the feasibility of the proposed algorithm. The results show that the algorithm is able to find optimum model that enables EMG to joint torque conversion.

Various myoelectric prostheses controlled by electromyography (EMG) signals have been developed. However, there have been few studies that provide fast and accurate methods to predict handgrip force from EMG signals. Rapid and precise handgrip force prediction is required, especially for the real-time control system of myoelectric prostheses. In this study, extreme learning machine (ELM) is applied to predict handgrip force from surface EMG signals of forearm muscles. Furthermore, ELM is compared with support vector machine (SVM) and multiple nonlinear regression (MNLR). The below 10 % of the surface EMG and handgrip force signals were cut away, and then the root mean square feature extracted from the modified surface EMG signals was taken as input vector for these three kinds of predicting mechanisms. For the testing dataset, ELM achieved a slightly larger root mean squared error than SVM did and a smaller one than MNLR did. Meanwhile, all three methods showed high correlation coefficients. For the total processing time, ELM and MNLR consumed much less time than SVM did. Experimental results demonstrate that ELM possesses a relatively good accuracy and little consumed time, although SVM is effective for handgrip force estimation in terms of accuracy. Overall, ELM has a promising potential for predicting handgrip force rapidly and precisely.

There is ample evidence that electromyography (EMG) signals from the corrugator supercilii and zygomatic major muscles can provide valuable information for the assessment of subjective emotional experiences. Although previous research suggested that facial EMG data could be affected by crosstalk from adjacent facial muscles, it remains unproven whether such crosstalk occurs and, if so, how it can be reduced. To investigate this, we instructed participants (n = 29) to perform the facial actions of frowning, smiling, chewing, and speaking, in isolation and combination. During these actions, we measured facial EMG signals from the corrugator supercilii, zygomatic major, masseter, and suprahyoid muscles. We performed an independent component analysis (ICA) of the EMG data and removed crosstalk components. Speaking and chewing induced EMG activity in the masseter and suprahyoid muscles, as well as the zygomatic major muscle. The ICA-reconstructed EMG signals reduced the effects of speaking and chewing on zygomatic major activity, compared with the original signals. These data suggest that: (1) mouth actions could induce crosstalk in zygomatic major EMG signals, and (2) ICA can reduce the effects of such crosstalk.

Electromyography (EMG) signal is electrical manifestation of neuromuscular activation due to which physiological processes are accessible which cause the muscle to generate force and produce movement and help us to interact with the world. EMGs have large variation and nonstationary properties. There are two issues in the classification of EMG signals. One is the feature selection, and the other is classifier design. In EMGs diseases recognition, the first and the most important step is feature extraction. In this paper, we have selected Symlet of order five mother wavelet for EMG signal analysis and later we have selected eight features to classify EMG signals of isometric contraction for two different abnormalities namely ALS (Amyotrophic Lateral Sclerosis) which is coming under Neuropathy and Myopathy and the classification approach is termed as Muscular Atrophy Diagnostic Approach (MADA) by authors. From the experimental results, waveform length is the best feature comparing with the other features. Root mean square, spectrogram, kurtosis, entropy and power are other useful augmenting features.

One of the most important electrophysiological signal is the Electromyography (EMG) signal, which is widely used in medical and engineering studies. This signal contains a wealth of information about muscle functions. Therefore, the EMG signal is becoming increasingly important and has started to be used in many applications like finger movement rehabilitation. However, an advanced EMG signal analysis method is required for efficient usage of such applications. This signal analysis can include signal detection, decomposition, processing, and classification. There are many approaches in studying the EMG signals, however, one of the important factor of analyzing is to get the most efficient and effective features that can be extracted from the raw signal. This paper presents the best feature extraction set compared to previous studies. Where eighteen well-known features algorithm has been tested using the sequential forward searching (SFS) method to get excellent classification accuracy in a minimum processing time. Among these novel features only four combinations have been selected with perfect results, which are; Hjorth Time Domain parameters (HTD), Mean Absolute Value (MAV), Root Mean Square (RMS) and Wavelet Packet Transform (WPT). The superiority of this feature set has been proven experimentally, and the results show that the classification accuracy could reach up to 99% to recognize the individual and combined for ten classes of finger movements using only two EMG channels.

Human walking is an important and intuitive operation in daily life, but it is different for people missing this ability. Recognizing human gait cycle portions during walking is very useful for understanding the biomechanics of the muscles, pre-disease’s diagnosis, and designing lower limb prosthetics. The normal gait cycle is divided into stance and swing phases. In this work, benchmarking of several classification techniques is performed, based on electromyography (EMG) data collected from seven lower limb muscles, with the gait cycle phase used as a target vector to label the EMG data. The dataset is split into a training set used to build a statistical model using a specified classification technique, and a test set for purpose of testing a pre-generated statistical model. EMG signals are unfortunately normally corrupted with noise, which drastically reduces their classification performance. A median filter and root mean square (RMS) filter were thus applied to the raw EMG signals, and the performance of the classification techniques calculated in all cases for the raw EMG, EMG with median filter, and EMG with RMS filters for comparison purposes. The use of filters offered good enhancement in the classification process. The median filtered EMG signal performed best and gave higher accuracy than the raw EMG and RMS filtered EMG signals due to the technique’s efficacy in removing outliers. This work also offers a simple explanation of each classifier algorithm used, and a graphical feature selection was also applied to all seven muscles of interest to identify the muscle with the most influence on the gait cycle. The Rectus Femoris muscle shows the best activity separation between swing and stance phases, working mainly during the swing phase; this higher activity during the swing phase thus caused it to be identified as the muscle with the most influence over the normal gait cycle.

This paper describes a new approach for the visual pose estimation of an uncertain robotic manipulator using ANFIS (Artificial Neuro-Fuzzy Inference System) and two uncalibrated cameras. The main emphasis of this work is on the ability to estimate the positioning accuracy and repeatability of a low-cost robotic arm with unknown parameters under uncalibrated vision system. The vision system is composed of two cameras; installed on the top and on the lateral side of the robot, respectively. These two cameras need no calibration; thus, they can be installed in any position and orientation with just the condition that the end-effector of the robot must remain always visible. A red-colored feature point is fixed on the end of the third robotic arm link. In this study, captured image data via two fixed-cameras vision system are used as the sensor feedback for the position tracking of an uncertain robotic arm. LabVolt R5150 manipulator in our laboratory is used as case study. The visual estimation system is trained using ANFIS with subtractive clustering method in MATLAB. In MATLAB, the robot, feature point and cameras are simulated as physical behaviors. To get the required data for ANFIS, the manipulator was maneuvered within its workspace using forward kinematics and the feature point image coordinates were acquired with the two cameras. Simulation experiments show that the location of the robotic arm can be trained in ANFIS using two uncalibrated cameras; and problems for computational complexity and calibration requirement of multi-view geometry can be eliminated. Observing Mean Square Error (MSE), Root Mean Square Error (RMSE), Error Mean and Standard Deviation Errors, the performance of the proposed approach is efficient for using as visual feedback in uncertain robotic manipulator. Further, the proposed approach using ANFIS and uncalibrated vision system has better in flexibility, user-friendly manner and computational concepts over conventional techniques.

The aim of this randomized controlled crossover study was to investigate the effect of a bout of unaccustomed eccentric exercise (ECC) followed by a consecutive bout of the same intensity on local muscle blood flow, amplitude, and frequency of the electromyographic (EMG) signal from the exercised tibialis anterior muscle. Sixteen healthy male participants (age, 25.7 (0.6) years; body mass index 24.8 (1) kg·m(-2) participated in this study. Two identical bouts of high-intensity ECC were performed on the tibialis anterior muscle 7 days apart. Control sessions involving no exercise were performed 4 weeks either before or after the exercise sessions. Changes in local total blood flow [ΔtHb], EMG root mean square, and median power frequency were recorded during isometric maximum voluntary contraction of ankle dorsiflexion. Measurements were performed before, immediately after, and the day after both ECCs (ECC1 and ECC2). The participants rested quietly in a chair in the control session. Eccentric exercise 1 led to a significant decrease in [ΔtHb] on the day after (p ≤ 0.05), whereas ECC2 did not. Median power frequency decreased significantly in ECC2 compared with ECC1 (p < 0.01). Root mean square was unchanged in all the instants. The present study showed that adaptation is depicted in the local muscle blood flow and the frequency contents of the EMG after an unaccustomed ECC inducing muscle soreness. These alterations provide a potential mechanism for a rapid adaptation, which decreases susceptibility of the muscle to develop further soreness in the subsequent ECC bout.

Feasibility of using EMG driven neuromusculoskeletal model for prediction of dynamic movement of the elbow

Human elbow joint torque is linearly encoded in electromyographic signals from multiple muscles

Muscular fitness is not only the ability of the body to adapt to work and the environment but also the operational ability of physical behavior. We speculated whether research could be conducted on the theory of muscular fitness and its qualitative/quantitative relationship based on muscular fitness and exercise physiology from the perspective of muscular endurance and muscular exploration. This study used standing long jumps as a standard metric for physical fitness to identify the bottom 20% groups. The experiment involved eight freshmen from the bottom 20% groups, and the pre-tests of the participants’ electromyography (EMG) signals under different exercise intensities were measured and after performing a set of intensive exercises for post-tests. The signal’ characteristics measured in time and frequency domains were analyzed to find the correlation between them and the participants’ muscular fitness. Weighted squats were chosen as the strength movements, which were separated into an exercise experiment and a force plate experiment. Both experiments included three different exercise intensities: 8 repetition maximum (RM), 18RM, and 28RM. The EMG signals were captured and analyzed in both time and the frequency domains. Finally, paired sample tests were performed to determine the difference of features under different exercise intensities. The comparison of readings before and after intensive exercises shows that, for the exercised experiment, a significant difference in the mean absolute (MAV), the variance of EMG (VAR), the root mean square value (RMS), and the average amplitude of change (AAC) was observed under 8RM. Under 18RM, MAV, VAR, and AAC showed a significant difference. In the force plate experiment, RMS, AAC, mean frequency (MNF), and median frequency (MDF) showed a statistically significant difference under the intensity of 18RM. As for intensity under 28RM, MAV, VAR, RMS, and AAC also showed significant difference.

Quantitative assessment of rotator cuff muscle activity is important in the treatment of shoulder disorders. However, the known methods for assessing rotator cuff muscle activity thus far have been inaccurate, invasive, and inconvenient. (1) Does the activity of the deltoid, supraspinatus, and infraspinatus muscles measured using ultrasound shear wave elastography have a linear correlation with muscle activity assessed using generally used methods, including isokinetic dynamometry and electromyography? (2) Does the activity of the deltoid, supraspinatus, and infraspinatus muscles measured using shear wave elastography show good intraobserver and interobserver reliability? Twelve volunteers participated in intrasession reliability experiments. They were asked to perform isometric abduction, external rotation, and scaption contractions (defined as elevation of the arm within the plane of the scapula with neutral arm rotation) gradually increased from 0% to 75% of maximal voluntary contraction. The joint torque, electromyographic activity, and shear elastic modulus were synchronously measured in the middeltoid, supraspinatus, and infraspinatus muscles. The validity of the elastic modulus value was assessed using regression analysis between normalized torque and electromyographic root mean square values. For intraobserver and interobserver reliability measurements, repeated experiments were performed with the same protocol. The shear elastic modulus and normalized joint torque with isokinetic dynamometry showed a linear relationship in all muscles (deltoid, supraspinatus, and infraspinatus) and each of the ultrasonography planes (longitudinal and transverse) (mean R > 0.8 and p < 0.001 for all measurements). For the supraspinatus muscle, the mean slope of the relationship between shear elastic modulus in the longitudinal plane and the normalized joint torque during scaption contraction was 1.28 ± 0.39 kPa/%MVC (mean R = 0.93 ± 0.21, p < 0.001). Furthermore, similar results were obtained in relation to electromyography root mean square values (mean R > 0.8 and p < 0.001 in all measurements). For the supraspinatus muscle, the mean slope of the relationship between shear elastic modulus in the longitudinal plane and electromyographic (EMG) root mean square was 0.96 ± 0.27 kPa/%EMG (mean R = 0.91 ± 0.08, p < 0.001). The intraobserver and interobserver reliabilities were excellent in all positions (abduction, external rotation, and scaption) and in both the longitudinal and transverse ultrasonography planes (all intraclass correlation coefficients are > 0.85). Shoulder muscle activity can be noninvasively evaluated with ultrasound shear wave elastography. Clinician and scientists should consider the application of this technique in cases in which evaluation of shoulder muscle activity is required. The next step after this study will be to check the shear elastic modulus of rotator cuff muscle in patients with rotator cuff tear. We plan to evaluate the correlation between shear elastic modulus and joint torque according to tear size and fatty infiltration status of rotator cuff muscle. Shear wave electrography can be used to measure various tissue elasticities in both static and dynamic modes. It may be a useful tool to evaluate pre- and postoperative rotator cuff muscle activity in a relatively simple manner. Shoulder function after reverse total shoulder arthroplasty associated with deltoid muscle activity also may be evaluated. Changes in tissue tightness in shoulder disorders caused by increase soft tissue stiffness (ie, adhesive capsulitis and glenohumeral internal rotation deficit) can be evaluated.