Multifingered computer controlled robot hands have been built by researchers to recreate human functionality. Our research is focused on the use of the Belgrade/USC robot hand as a prototype prosthesis for below-elbow amputees. A method has been developed that combines EMG and computer signals to control the phases of prehension. Simulation work presented here, based on pilot data, uses surface...

Background/Aims: This work focuses on recording, processing and interpretation of multi-channel surface EMG detected from the external anal sphincter muscle. The aim is to describe the information that can be extracted from signals recorded with such a technique. Methods: The recording of many signals from different locations on a muscle allows the extraction of additional information on muscle...

The electrical signals produced by the muscles and nerves are analyzed to assess the state of neuromuscular function in subjects with suspected neuromuscular disorders. The repetitive activation of several individual motor units (MUs) results in a superposed pulse train and constitutes the electromyogram (EMG) signal. The analysis of the EMG is based on its basic constituent i.e. motor-unit act...

In this research, surface electromyography (SEMG) signal analysis from the right rectus femoris muscle is performed during walk. Wavelet transform (WT) has been applied for removing noise from the surface SEMG. Gaussianity tests are conducted to understand changes in muscle contraction and to quantify the effectiveness of the noise removal process. Results show that the proposed method can effe...

OBJECTIVE Automatic decomposition of surface electromyographic (sEMG) signals into their constituent motor unit action potential trains (MUAPTs). METHODS A small five-pin sensor provides four channels of sEMG signals that are in turn processed by an enhanced artificial intelligence algorithm evolved from a previous proof-of-principle. We tested the technology on sEMG signals from five muscles...

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., ...

When the central nervous system (CNS) develops a muscular activation pattern to accomplish a particular isometric task, it clearly uses information concerning the external task requirements. These task requirements serve as inputs to neural transformations that output muscular activations. However, the nature of the inputs is not exactly known. Electromyographic (EMG) signals from eight muscles...

This paper presents a five-fingered underactuated prosthetic hand controlled by surface electromyographic (EMG) signals. The prosthetic hand control part is based on an EMG motion pattern classifier which combines variable learning rate (VLR) based neural network with parametric Autoregressive (AR) model and wavelet transform. This motion pattern classifier can successfully identify flexion and...

Numerous medical and biomechanical applications involve electromyogram (EMG) signal processing in real time. Amplitude analysis of the EMG often requires computation of the signal's linear envelope. For this purpose, several methods are commonly described in the literature; however, not all match the speed requirement of real-time applications. We introduce an implementation which accelerates t...