Search results for "Electromyogram"
showing 7 items of 7 documents
Spatial variability of muscle activity during human walking: The effects of different EMG normalization approaches
2015
Human leg muscles are often activated inhomogeneously, e.g. in standing. This may also occur in complex tasks like walking. Thus, bipolar surface electromyography (sEMG) may not accurately represent whole muscle activity. This study used 64-electrode high-density sEMG (HD-sEMG) to examine spatial variability of lateral gastrocnemius (LG) muscle activity during the stance phase of walking, maximal voluntary contractions (MVCs) and maximal M-waves, and determined the effects of different normalization approaches on spatial and inter-participant variability. Plantar flexion MVC, maximal electrically elicited M-waves and walking at self-selected speed were recorded in eight healthy males aged 2…
Estimation of Muscular Fatigue under Electromyostimulation Using CWT
2012
International audience; The aims of this study are to investigate muscular fatigue and to propose a new fatigue index based on the continuous wavelet transform (CWT) which is compared to the standard fatigue indexes from literature. Fatigue indexes are all based on the electrical activity of muscles (electromyogram) acquired during an electrically stimulated contraction thanks to two modules (electromyostimulation + electromyography recording) that can analyze EMG signals in real time during electromyostimulation. The extracted parameters are compared with each other and their sensitivity to noise is studied. The effect of truncation of M waves is then investigated, enlightening the robustn…
EMG artifacts removal during electrical stimulation, a CWT based technique
2014
International audience; A technique of artifacts removal based on the continuous wavelet transform is presented. It uses common mother wavelets to find the temporal localization of stimulation artifacts on electromyogram (EMG) signal during an electrically evoked contraction of a muscle. This method can be used with standard stimulation pulse waveforms like monophasics or biphasics ones. It uses a histogram representation to find the best threshold to apply on the CWT domain. The algotithm is presented with Haar wavelet and then it is used with common wavelet famillies such as Daubechies or Symlets.
QUANTIFICATION OF MUSCLE FATIGUE WITHWAVELET ANALYSIS BASED ON EMG DURING MYOELECTRICAL STIMULATION
2012
6; International audience; We propose a device dedicated to real time analysis of electromyograms (EMG) under myoelectrical stimulation (ES). The muscular fatigue analysis, which is obtained by the use of a dedicated analog circuit and a processing part, is the main purpose of this study. The description of a hardware device which incorporates an electro-stimulator and an electromyogram amplifier combined to a computer is detailed. Then, we present a muscular fatigue analysis part based on wavelet decomposition in order to extract a fatigue index, which is confronted with synthetic and experimental data. We conclude that the CWT index applies well to M waves. The noise sensitivity is invest…
Contribution to the design of a smart electromyostimulator
2013
This project aims to develop a new tool for neuromuscular reeducation. Its function is to improve the quality and the duration of muscular strengthening training sessions and training of motor function for patients suffering from muscle deconditioning. A "smart" electromyostimulator using, at the same time, techniques of electrostimulation (EMS) and analysis of electromyography (EMG) allows the control in real time electrical stimulation parameters considering the physiological fatigue of the stimulated muscle. This control, performed on stimulation parameters depending on electrical response of muscles (M wave), allows the muscle stimulation taking into account the muscular reaction to the…
Assessment Of Driving Stress Through SVM And KNN Classifiers On Multi-Domain Physiological Data
2022
We propose an objective stress assessment method based on the extraction of features from physiological time series and their classification using Support Vector Machine and K-Nearest Neighbors algorithms. For this purpose, we used an open dataset consisting of multiparametric physiological signals (electrocardiogram, electromyogram, galvanic skin response and breath signal) obtained during the execution of a driving route within the city of Boston with restful, highway and city driving periods indicative of three different stress states. To predict the driver stress level, 21 features were extracted from 122 chunks of raw signals and were subsequently managed by classification algorithms. …
The Effect of the Cranial Electrotherapy on the Muscle Motor Function in Different Operating Modes
2015
The aim of the present study was to analyze the effect of cranial electrotherapy stimulation on muscle function analysis indicators. Instrumental assessment of muscle function (on a REV9000, Technogym, Italy) was performed before and after cranial electrotherapy stimulation, assessments of the muscle function was performed during knee extension maximum voluntary, isometric contraction (MVIC), angle velocity with load 45Nm isotonic contraction and maximum pick torque in isokinetic contraction on 30°/s, 200°/s and 300°/s and neuromuscular efficiency measurements. To analyze data was used Excel program Statistics 3.1. Subjects of our study were twenty healthy athletes of sport fitness. 1 minu…