Wavelets and Self-organising Maps in Electromyogram (emg) Analysis
نویسندگان
چکیده
Wavelets are a powerful tool for biomedical signal processing. Wavelets are used for the processing of signals that are non-stationary and time varying. The EMG signal contains transient signals related to muscle activity. EMG signals have typically many transient components, which are very interesting to isolate and classify according to their physiological significance. Wavelet based denoising is used to isolate coordinated muscle activity of the shoulder of a volunteer subject related to certain movements that appear during driving a car. The reconstructed de-noised signals show clearly the muscle activity. Because of the wavelet denoising, accurate observation of activity that is not possible with conventional filtering, becomes possible. That means small activity peaks covered by the screen of noise are now observable. Wavelet coefficients can be used as features for identifying fatigue or estimating type of movement. Flexible Classification methods based on Self-Organising Maps are used to identify car driver fatigue.
منابع مشابه
Removing ECG Artifact from the Surface EMG Signal Using Adaptive Subtraction Technique
Background: The electrocardiogram artifact is a major contamination in the electromyogram signals when electromyogram signal is recorded from upper trunk muscles and because of that the contaminated electromyogram is not useful.Objective: Removing electrocardiogram contamination from electromyogram signals.Methods: In this paper, the clean electromyogram signal, electrocardiogram artifact and e...
متن کاملEMG Myopathy Signal Detection Using Wavelet Transform and Neural Network Techniques
This paper recognises signals from two sources, where one is a normal person and the other is a myopathy patient. The signals under experimentation were acquired from the branchial biceps (BB) muscles using a needle electrode. This paper focuses on the technique of Wavelet Transformation (WT) to extract the features from an EMG (Electromyogram) signal. It includes decomposing the EMG signal int...
متن کاملCSE - 791 FPGA Circuits and Applications Fall 2009 Project Report on Signal Processing and Pattern Recognition using Continuous Wavelets Under guidance of Prof . Fred Schlereth By Ronak Gandhi
Goal This work aims at designing and implementing FPGA based module to process and perform pattern recognition on EMG (Electromyography) signals that are received from human muscular movements that are otherwise complex to analyze on some standard methods. Purpose On the completion of this work we want to gain proficiency in following areas Studying the available algorithms for processing...
متن کاملComparative Study of Different EMG Signal decomposition Techniques
EMG signals are electromyogram signals generated by firing of MUs (motor units) in muscle fibers. The decomposition of EMG signal of a muscle provides useful information for the diagnosis of neuro-muscular diseases by physician and neurologist. In decomposition of EMG signal different MUAPs (Motor Unit Action Potentials) are classified into different categories. This paper gives a review of dif...
متن کاملClinical gait data analysis based on Spatio-Temporal features
Analysing human gait has found considerable interest in recent computer vision research. So far, however, contributions to this topic exclusively dealt with the tasks of person identification or activity recognition. In this paper, we consider a different application for gait analysis and examine its use as a means of deducing the physical well-being of people. The proposed method is based on t...
متن کامل