Muscle fatigue is the decline in ability of a muscle to create force. Electromyography (EMG) is a medical technique for measuring muscle response to nervous stimulation. During a sustained muscle contraction, the power spectrum of the EMG shifts towards lower frequencies. These effects are due to muscle fatigue. Muscle fatigue is often a result of unhealthy work practice. In this research, the ...

Electromyography (EMG) is the study of the skeletal muscle function and the contraction of the skeletal muscle result in the generation of action potential in the individual muscle fibers, it from get information in the diagnosis of neuromuscular disorders. Suitable removal of baseline fluctuation of EMG signals is very important issue when recoding EMG signals as it may be degrade quality and ...

Electromyography (EMG) represents a method used for the acquisition of electrical activity produced by skeletal muscles. The following analysis based upon the digital signal processing methods can be used to find relation between their neurological activation and separate motor units firing with the typical frequency of 7-20 Hz. Signals obtained can be used for analysis of biomechanics of the h...

Electromyography is the study of muscle function through the electrical signals from the muscles. In surface electromyography the electrical signal is detected on the skin. The signal arises from ion exchanges across the muscle fibres’ membranes. The ion exchange in a motor unit, which is the smallest unit of excitation, produces a waveform that is called an action potential (AP). When a sustai...

Accurate muscle activity onset detection is an essential prerequisite for many applications of surface electromyogram (EMG). This study presents an unsupervised EMG learning framework based on a sequential Gaussian mixture model (GMM) to detect muscle activity onsets. The distribution of the logarithmic power of EMG signal was characterized by a two-component GMM in each frequency band, in whic...

To develop an advanced muscle–computer interface (MCI) based on surface electromyography (EMG) signal, the amplitude estimations of muscle activities, i.e., root mean square (RMS) and mean absolute value (MAV) are widely used as a convenient and accurate input for a recognition system. Their classification performance is comparable to an advanced and high computational timescale methods, i.e., ...

This doctoral thesis is about a rather new model-based signal processing methodology that is based on factor graphs and message-passing algorithms. Using this, we have developed exemplary signal processing algorithms for various biomedical applications. The main application to evaluate and demonstrate this new methodology was in the field of electromyographic (EMG) signal analysis. EMG signals ...

Surface electromyogram pattern recognition (EMG-PR) has been considered as a promising approach for predicting amputees’ motion intentions to control myoelectric prostheses. However, EMG recordings are mostly contaminated by various interferences, which decay the prediction of EMG-PR methods, thus affecting performance prosthesis. One common way solve this issue is improve quality signals via f...

The study of burst electromyographic (EMG) activity periods during muscles contraction and relaxation is an important challenging problem. It can find several applications like movement patterns analysis, human locomotion analysis neuromuscular pathologies diagnosis such as Parkinson disease. This paper proposes a new frame work for detecting the onset (start) / offset (end) EMG by segmenting s...