BACKGROUND This paper describes the "EMG Driven Force Estimator (EMGD-FE)", a Matlab® graphical user interface (GUI) application that estimates skeletal muscle forces from electromyography (EMG) signals. Muscle forces are obtained by numerically integrating a system of ordinary differential equations (ODEs) that simulates Hill-type muscle dynamics and that utilises EMG signals as input. In the ...

Surface electromyography is the technique for measuring levels of muscle activity. When a muscle contracts, electrical activity generated as action potentials propagate along the muscle fibers. There are two types of Surface EMG (SEMG)-Static scanning SEMG and Dynamic SEMG. In Dynamic SEMG, electrodes are attached to the skin and muscle activity is measured and graphed as the patient moves thro...

The study of electromyographic (EMG) signals has gained increased attention in the last decades since the proper analysis and processing of these signals can be instrumental for the diagnosis of neuromuscular diseases and the adaptive control of prosthetic devices. As a consequence, various pattern recognition approaches, consisting of different modules for feature extraction and classification...

In order to analyse surface electromyography (EMG) signals, it is necessary to extract the features based on a time or frequency domain. These approaches are based on the mathematical assumption of signal stationarity. Stationarity of EMG signals is thoroughly examined, especially in isotonic contractions. According to research, conflicting results have been identified depending on varying wind...

The electromyography (EMG) signal is a bioelectrical signal variation, generated in muscles during voluntary or involuntary muscle activities. The muscle activities such as contraction or relaxation are always controlled by the nervous system. The EMG signal is a complicated biomedical signal due to anatomical/physiological properties of the muscles and its noisy environment. In this paper, a c...

The entropy of electromyography (EMG) signals suggests a possible tool for the clinical assessment of low back pain (LBP). However, a general physiological mechanism for entropy and pain and/or dysfunction following exercise intervention is still unknown. This comparative study investigated the differences between entropy levels of the EMG signals and the slope of median frequency (MF) based on...

Among the potential biological signals for human-machine interactions (brain, nerve, and muscle signals), electromyography (EMG) widely used in clinical setting can be obtained non-invasively as motor commands to control movements. The aim of this study was to develop a model for continuous and simultaneous decoding of multi-joint dynamic arm movements based on multi-channel surface EMG signals...

Intramuscular electromyographic (EMG) signals are detected with needles or wires inserted into muscles. With respect to non-invasive techniques, intramuscular electromyography has high selectivity for individual motor unit action potentials and is thus used to measure motor unit activity. Decomposition of intramuscular signals into individual motor unit action potentials consists in detection a...

Cross-correlation between surface electromyogram (EMG) signals is commonly used as a means of quantifying EMG cross talk during voluntary activation. To examine the reliability of this method, the relationship between cross talk and the cross-correlation between surface EMG signals was examined by using model simulation. The simulation results illustrate an increase in cross talk with increasin...

This paper focuses on the development of an automatic system for sleep analysis. The system proposed in this paper combines two phases needed in sleep analysis. In a first step, an artefact detection system selects the polysomnographic signals (EEG, EOG, EMG) that are not corrupted by artefacts. In a second step, relevant features are extracted from the selected signals and classified using a n...