FFT Analysis and Motion Classification of EMG Signals

Denoising and Analysis of Surface EMG Signals

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...

Classification and Diagnosis of Myopathy from EMG Signals*

We present a methodology to predict the presence of myopathy (muscle disease) from intramuscular electromyography (EMG) signals. By evaluating the shape and frequency of electrical action potentials produced by muscular fibers and captured in EMG measurements, a physician can often detect both the presence and the severity of such disorders. However, EMG measurements can vary significantly acro...

Design and Performance Analysis of Artificial Neural Network for Hand Motion Detection from EMG Signals

Submitted: Jan 9, 2013; Accepted: Feb 17, 2013; Published: Jul 27, 2013 Abstract: Besides prosthetic device control and neuromuscular disease identification, electromyography (EMG) signals can also be applied in the field of human computer interaction (HCI) system. This article represents the classification of Electromygraphy (EMG) signal for the detection of different predefined hand motions (...

Nonlinear Analysis of Surface EMG Signals

Classification of EMG signals using wavelet neural network.

An accurate and computationally efficient means of classifying electromyographic (EMG) signal patterns has been the subject of considerable research effort in recent years. Quantitative analysis of EMG signals provides an important source of information for the diagnosis of neuromuscular disorders. Following the recent development of computer-aided EMG equipment, different methodologies in the ...