An algorithm is proposed for processing and analyzing surface electromyography (SEMG) signals using wavelet transform and Higher Order Statistics (HOS). EMG signal acquires noise while travelling though different media. Wavelet denoising is performed in this research for initial EMG signal processing. With the appropriate choice of the Wavelet Function (WF), it is possible to remove interferenc...

Blind source separation (BSS) was performed to reduce the crosstalk in the surface electromyografic signals (SEMG) for the muscle force estimation applications. A convolutive mixture model was employed to separate the SEMG signals from two finger extensor muscles using a frequency-domain approach. It was assumed that the tension of each muscle varies independently and the independence of the SE...

This paper presents the development of a bioinstrumentation system for the acquisition and pre-processing of surface electromyographic (SEMG) signals, as well as the proposal of a myoelectric controller for leg prostheses, using algorithms for feature extraction and classification of myoelectric patterns. The implemented microcontrolled bioinstrumentation system is capable of recording up to fo...

Bibliographic searches identified 14 controlled and uncontrolled outcome evaluations of biofeedback-based treatments for temporomandibular disorders published since 1978. This literature includes two randomized controlled trials (RCTs) of each of three types of biofeedback treatment: (1) surface electromyographic (SEMG) training of the masticatory muscles, (2) SEMG training combined with adjunc...

This study has described and experimentally validated the differential electrodes surface electromyography (sEMG) model for tibialis anterior muscles during isometric contraction. This model has investigated the effect of pennation angle on the simulated sEMG signal. The results show that there is no significant effect of pennation angle in the range 0° to 20° to the single fibre action potenti...

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...

Muscle activity monitoring in dynamic conditions is a crucial need different scenarios, ranging from sport to rehabilitation science and applied physiology. The acquisition of surface electromyographic (sEMG) signals by means grids electrodes (High-Density sEMG, HD-sEMG) allows obtaining relevant information on muscle function recruitment strategies. During conditions, this possibility demands ...

In this paper we propose a method for human joint torque estimation with surfaceelectromyogram (sEMG) during dynamic joint movements. sEMG measurement is non-invasive and costs less than external force measurement. Typical torque estimation methods from EMG are available for isometric movements. Our method works also during dynamic movement and outputs the torque timehistory data. Their propose...

Wide use of surface electromyograph (sEMG) has been made for efficient recognition of finger gestures due to its convenient to use and distinguishing signal patterns along finger movements. For high classification accuracy, it is important to have a consistent feature of sEMG signal feature for each finger gesture of a number. However, feature of sEMG signal for identical finger gesture can be ...

We have developed a novel algorithm for sEMG feature extraction and classification. It is based on a hybrid network composed of spiking and artificial neurons. The spiking neuron layer with mutual inhibition was assigned as feature extractor. We demonstrate that the classification accuracy of the proposed model could reach high values comparable with existing sEMG interface systems. Moreover, t...