Varieties of noises are major problem in recognition Electromyography (EMG) signal. Hence, methods to remove noise become most significant EMG signal analysis. White Gaussian (WGN) is used represent interference this paper. Generally, WGN difficult be removed using typical filtering and solutions limited. In addition, removal an important step before performing feature extraction, which EMG-based recognition. This research aimed present a novel that tolerate with WGN. As result, algorithm...

Specialized myoelectric sensors have been used in prosthetics for decades, but, with recent advancements wearable sensors, wireless communication and embedded technologies, electromyographic (EMG) armbands are now commercially available the general public. Due to physical, processing, cost constraints, however, these typically sample EMG signals at a lower frequency (e.g., 200 Hz Myo armband) than their clinical counterparts. It remains unclear whether existing feature extraction methods,...

The increasing amount of data in electromyographic (EMG) signal research has greatly increased the importance developing advanced analysis and machine learning techniques which are better able to handle “big data”. Consequently, more applications EMG pattern recognition have been developed. This paper begins with a brief introduction main factors that expand resources into era big data, followed by recent progress existing shared sets. Next, we provide review development methods can be...

Nowadays, analysis of electromyography (EMG) signal using wavelet transform is one the most powerful processing tools. It widely used in EMG recognition system. In this study, we have investigated usefulness extraction features from multiple-level decomposition signal. Different levels various mother wavelets were to obtain useful resolution components Optimal component (sub-signal) was selected and then reconstruction information done. Noise unwanted parts eliminated throughout process. The...

Recently, wavelet analysis has proved to be one of the most powerful signal processing tools for surface electromyography (sEMG) signals. It been widely used in sEMG pattern classification both clinical and engineering applications. This study investigated usefulness extracting features from multiple-level decomposition reconstruction. A suitable based function was yield useful resolution components signal. The optimal component selected then its reconstruction carried out. Throughout this...

The increasing amount of data in biomechanics research has greatly increased the importance developing advanced multivariate analysis and machine learning techniques, which are better able to handle "big data". Consequently, advances science methods will expand knowledge for testing new hypotheses about biomechanical risk factors associated with walking running gait-related musculoskeletal injury. This paper begins a brief introduction an automated three-dimensional (3D) gait collection...

Females have a two-fold risk of developing knee osteoarthritis (OA) as compared to their male counterparts and atypical walking gait biomechanics are also considered factor in the aetiology OA. However, few studies investigated sex-related differences mechanics for patients with OA those, conflicting results been reported. Therefore, this study was designed examine kinematics (1) between female subjects without (2) healthy gender-matched counterparts.One hundred (45 males 55 females) 43 (18...

The analysis of EMG signals can be generally divided into three main issues, i.e., muscle force, geometry and fatigue. Recently, there are no universal indices that applied for all issues. In this paper, we modify the global fatigue indices, namely mean frequency (MNF) median (MDF), to used as a force index. Due drawback MNF MDF it has non-linear relationship between feature value, especially in large muscles cyclic dynamic contractions. A time-dependence (TD-MNF TD-MDF) is computed...

Existing research on myoelectric control systems primarily focuses extracting discriminative characteristics of the electromyographic (EMG) signal by designing handcrafted features. Recently,however, deep learning techniques have been applied to challenging task EMG-based gesture recognition. The adoption these slowly shifts focus from feature engineering learning. Nevertheless, black-box nature makes it hard understand type information learned network and how relates Additionally, due high...

Despite a historical focus on prosthetics, the incorporation of electromyography (EMG) sensors into less obtrusive wearable designs has recently gained attention as potential human–computer interaction scheme for general consumer use. Because consumers are more used to wrist-worn devices, this article presents comprehensive and systematic investigation feasibility hand gesture recognition using EMG signals recorded at wrist. A direct comparison signal information quality is conducted between...

Female runners have a two-fold risk of sustaining certain running-related injuries as compared to their male counterparts. Thus, comprehensive understanding the sex-related differences in running kinematics is necessary. However, previous studies either used discrete time point variables and inferential statistics and/or relatively small subject numbers. Therefore, first purpose this study was use principal component analysis (PCA) method along with support vector machine (SVM) classifier...

The probability density function (pdf) of an electromyography (EMG) signal provides useful information for choosing appropriate feature extraction technique. pdf is influenced by many factors, including the level contraction force, muscle type, and noise. In this paper, we investigated pdfs noisy EMG signals artificially contaminated with five different noise types: 1) Electrocardiography (ECG) interference; 2) spurious background spikes; 3) white Gaussian noise; 4) motion artifact; 5) power...

The success of biological signal pattern recognition depends crucially on the selection relevant features. Across and imaging modalities, a large number features have been proposed, leading to feature redundancy need for optimal set identification. A further complication is that, due inherent variability, even same classification problem different datasets can display variations in respective sets, casting doubts generalizability Here, we approach this by leveraging topological tools create...

Fractal analysis of stride interval time series is a useful tool in human gait research which could be used as marker for adaptability, disorder, and fall risk among patients with movement disorders. This study designed to systematically comprehensively investigate two practical aspects fractal significantly affect the outcome: length parameters algorithm. The Hurst exponent, scaling and/or dimension are computed from both simulated experimental data using three methods, namely detrended...

The aim of this study was to investigate and select the wavelet function that is optimum denoise surface electromyography (sEMG) signal for multifunction myoelectric control. Wavelet denoising algorithm has been used find optimal removing white Gaussian noise (WGN) at various signal-to-noise ratios (SNRs) from sEMG signals. A total 53 functions were in evaluation denoised performance. wavelets are Daubechies, Symlets, Coiflet, BiorSplines, ReverseBior, Discrete Meyer. Universal thresholding...