The electromyographic (EMG) signals can be used as a control source of artificial limbs after it has been processed. The objective of this work is to achieve better classification for four different movements of a prosthetic limb making a time-frequency analysis of EMG signals which covers a feature extraction tools in the problem of the EMG signals while investigating the related dimensionalit...

Electromyography (EMG) signal is the muscle electrical activity. Electromyography is a technique for detecting and recording the electrical potential generated by muscle cells. This EMG signals are used in medical professionals to determine specific disorders. This paper basically deals with the analysis of different electromyography signals (NOR & MYO). In this paper, new method for classifica...

Target-directed elbow movements are essential in daily life; however, how different task demands affect motor control is seldom reported. In this study, the relationship between task demands and the complexity of kinematics and electromyographic (EMG) signals on healthy young individuals was investigated. Tracking tasks with four levels of task demands were designed, and participants were instr...

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...

An animal model was established for invivo studies of the EMG responses of a parturient uterine smooth muscle tissue to electrical stimuli. Five normally gravid sheep were fitted with implanted electrodes to register EMG signals and electrical stimulation. EMG signals were detected at the cervix and at the gravid horn. Spontaneous EMG activity was registered simultaneously with EMG responses to...

INTRODUCTION Recent developments in wavelet techniques allow the time and frequency components of EMG signals to be analyzed simultaneously. The resulting patterns of the EMG can provide new and more detailed insight into how muscles are used during physical activities. The aim of this study was to investigate the changes in the time-frequency content of the EMG signals which occur during prolo...

In the area of biomedical digital signal processing (DSP), wavelet analysis, neural networks and pattern recognition methods are being developed for analysis of EMG signals (generated by the muscles) in neuromuscular disease and CTG (the cardiotocogram) signals during labor. These are traditionally very difficult signals to quantify and innovative approaches to analysis are required for clinica...

This report describes an early version of a technique for decomposing surface electromyographic (sEMG) signals into the constituent motor unit (MU) action potential trains. A surface sensor array is used to collect four channels of differentially amplified EMG signals. The decomposition is achieved by a set of algorithms that uses a specially developed knowledge-based Artificial Intelligence fr...

Surface electromyogram (EMG) detected by electrode arrays along the muscle fibre direction can be approximated by the sum of propagating and non propagating components. A technique to separate propagating and non propagating components in surface EMG signals is developed. The first step is an adaptive filter, which allows obtaining an estimation of the delay between signals detected at differen...

This doctoral thesis is about a rather new model-based signal processing methodology that is based on factor graphs and message-passing algorithms. Using this, we have developed exemplary signal processing algorithms for various biomedical applications. The main application to evaluate and demonstrate this new methodology was in the field of electromyographic (EMG) signal analysis. EMG signals ...