Since the 1970s, electromyographic control of a prosthetic device has been attempted in a number of different ways. It was only until recently, that the classification of electromyographic signals was possible through the use of neural networks. With the advent of more advanced techniques like support vector machines this type of control is becoming more realistic. This would initiate a great s...

Electromyographic (EMG) signal provide a significant source of information for diagnosis, treatment and management of neuromuscular disorders.Neuromuscular diseases changes, the shape and characteristics of the motor unit action potentials (MUAPs). The MUAPs detected from myopathic patients are characterized by high frequency contents, low peak-to-peak amplitude and MUAPs neuropathic patients a...

1,3 Electronics and Communication Engineering, Swami Keshwanand Institute of Technology, Jaipur,(India) 2 Electronics and Communication Engineering, Sri Balaji College of Engineering & Technology, Jaipur,(India) ABSTRACT An approach for removal of the presence of ECG in electromyographic signals by means of time-variant harmonic modeling of the cardiac artifact. The amplitude and frequency time...

The study proposes a method for supervised classification of multi-channel surface electromyographic signals with the aim of controlling myoelectric prostheses. The representation space is based on the discrete wavelet transform (DWT) of each recorded EMG signal using unconstrained parameterization of the mother wavelet. The classification is performed with a support vector machine (SVM) approa...

Organophosphate (OP) compounds are present in household and agricultural pesticides as well as in nerve agents. The toxic effects of these chemicals result from their anticholinesterase activity, which disrupts nerve junctions and parasympathetic effector sites, leading to a variety of symptoms and possible death. When the anticholinesterase agents in OP compounds reach the neuromuscular juncti...

In recent years, the use of deep learning algorithms has become increasingly more prominent. Within the field of electromyography-based gesture recognition however, deep learning algorithms are seldom employed. This is due in part to the large quantity of data required for the network to train on. The data sparsity arises from the fact that it would take an unreasonable amount of time for a sin...

background: the time and frequency features of motor unit action potentials (muaps) extracted from electromyographic (emg) signal provide discriminative information for diagnosis and treatment of neuromuscular disorders. however, the results of conventional automatic diagnosis methods using muap features is not convincing yet. objective: the main goal in designing a muap characterization system...

The purpose of this study was to examine the effects of diverting activities on electromyographic amplitude and mean frequency. On three separate occasions, eleven men and eight women performed two bouts of fifty consecutive maximal concentric isokinetic muscle actions of the dominant leg extensors. Between these bouts, the subjects either solved math problems, performed contralateral dynamic c...

The overarching goal of this work is to develop a control algorithm that will allow an active prosthetic ankle to emulate its biological equivalent. The current convention for below-knee amputees is to use passive ankles. Previous work exists in active ankles under state machine control and active prosthetic elbows under electromygraphic (EMG) based control. In this paper, an investigation of m...

INTRODUCTION Analysis of continuous diaphragm electromyography (dEMG) has not been well studied. We describe a system of analyzing continuous dEMG using implanted electrodes. METHODS dEMG signal was acquired via two pairs of electrodes near the diaphragm motor points. Raw bursts of dEMG signal were compared to externally captured electrocardiogram (ECG) using adaptive filtering in order to re...