Approach towards the characteristic interpretation of Electromyography (EMG) signal with clinical study (original) (raw)
Related papers
2020
In this paper, Diagnostic Electromyography (D-EMG) signal interpretation of human arm towards characterization of arm muscle interaction during various arm movement has been discussed. EMG signals from 4 important arm muscle (i.e. Bicepsbracci, Tricepsbracci, brachioradialis and lateral deltoids) are recorded clinically during 5 different arm movements (i.e. Extension of forearm, Flexion of elbow joint, Pronation of forearm, shoulder abduction and Wrist flexor stretch) under load condition (a load of 2 Kg& 4 Kg maintained during experimental arm movement), the recorded D-EMG signals are properly enveloped within a range of 5-100 Hz and quantized within a proper sampling frequency range to produce a knowledge based database of muscle activity. In addition, correlation of muscle activity and Power spectral density (PSD) analysis has been carried out towards muscle process discriminating during various arm action.
Electromyography Signal Based For Intelligent Prosthesis Design
IFMBE Proceedings, 2008
Electromyography (EMG) is widely used throughout the world for different study such as clinical diagnosis and for movement analysis. One of the applications of EMG is in the development of myoelectric prosthesis. It is intended by all the biomechanics engineer in order to provide better living to the amputees since the current prosthesis are limited. It is principally operated based on the EMG signal generated from muscle contraction. Therefore, the challenge to develop myoelectric prosthesis devices begins with the challenging part to understand completely the principles of electromyography (EMG). Knowledge of EMG could lead the researcher to apply the signal correctly. This conceptual paper will provide better understanding and framework in process to develop such intelligent prosthesis.
EMG Signal Features Extraction of Different Arm Movement for Rehabilitation Device
Rehabilitation device is used as an exoskeleton for people who had failure of their limb. Arm rehabilitation device may help the rehab program to who suffer from arm disability. The device used to facilitate the tasks of the program should improve the electrical activity in the motor unit and minimize the mental effort of the user. Electromyography (EMG) is the techniques to analyze the presence of electrical activity in musculoskeletal systems. The electrical activity in muscles of disable person is failed to contract the muscle for movements. To prevent the muscles from paralysis becomes spasticity the force of movements should minimize the mental efforts. To minimize the used of mental forced for disable patients, the rehabilitation device should analyze the surface EMG signal of normal people that can be implemented to the device. The signal is collected according to procedure of surface electromyography for non-invasive assessment of muscles (SENIAM). The EMG signal is implemented to set the movements' pattern of the arm rehabilitation device. The filtered EMG signal were extracted for features of Standard Deviation(STD), Mean Absolute Value(MAV), Root Mean Square(RMS) in time-domain. The extraction of EMG data is important to have the reduced vector in the signal features with less of error. In order to determine the best features for any movements, several trials of extraction methods are used by determining the features that can be used in classifier. The accurate features can be appliedin future works of rehabilitation control system in real-time and classification of the EMG signal.
EMG Pattern Analysis and Classification for a Prosthetic Arm
IEEE Transactions on Biomedical Engineering, 1982
This paper deals with the statistical analysis and pattern classification of electromyographic signals from the biceps and triceps of a below-the-humerus amputated or paralyzed person. Such signals collected from a simulated amputee are synergistically generated to produce discrete lower arm movements. The purpose of this study is to utilize these signals to control an electrically driven prosthetic or orthotic arm with minimum extra mental effort on the part of the subject. The results show very good separability of classes of movements when a learning pattern classification scheme is used, and a superposition principle seems to hold which may provide a means of decomposition of any composite motion to the six basic primitive motions, e.g., humeral rotation in and out, elbow flexion and extension, and wrist pronation and supination. Since no synergy was detected for the hand movements, different inputs have to be provided for a grip.
An implementation of movement classification for prosthesis control using custom-made EMG system
Serbian Journal of Electrical Engineering, 2017
Electromyography (EMG) is a well known technique used for recording electrical activity produced by human muscles. In the last few decades, EMG signals are used as a control input for prosthetic hands. There are several multifunctional myoelectric prosthetic hands for amputees on the market, but so forth, none of these devices permits the natural control of more than two degrees of freedom. In this paper we present our implementation of the pattern classification using custom made components (electrodes and an embedded EMG amplifier). The components were evaluated in offline and online tests, in able bodied as well as amputee subjects. This type of control is based on computing the time domain features of the EMG signals recorded from the forearm and using these features as input for a Linear Discriminant Analysis (LDA) classifier estimating the intention of the prosthetic user.
Features Extraction of Electromyography Signals in Time Domain on Biceps Brachii Muscle
International Journal of Modeling and Optimization, 2013
Electromyography (EMG) is widely used in various fields to investigate the muscular activities. Since EMG signals contain a wealth of information about muscle functions, there are many approaches in analyzing the EMG signals. It is important to know the features that can be extracting from the EMG signal. The ideal feature is important for the achievement in EMG analysis. Hence, the objective of this paper is to evaluate the features extraction of time domain from the EMG signal. The experiment was setup according to surface electromyography for noninvasive assessment of muscle (SENIAM). The recorded data was analyzed in time domain to get the features. Based on the analysis, three features have been considered based on statistical features. The features was then been evaluate by getting the percentage error of each feature. The less percentage error determines the ideal feature. The results shows that the extracted features of the EMG signals in time domain can be implement in signal classification. These findings could be integrated to design a signal classification based on the features extraction.
Electromyogram (EMG) Signal Processing Analysis for Clinical Rehabilitation Application
Analysis of electromyogram (EMG) signal processing and its application to identify human muscle strength of rehabilitation purpose has been successfully carried out in this paper. Single channel EMG signal was obtained from human muscle using non-invasive electrodes and further process by signal acquisition circuit to get a suitable signal to be process. In the first part of signal acquisition, the amplification circuit for the small EMG signal has been design successfully. After amplification stage EMG signal was digitized through analogue and digital converter (ADC) then further process in microcontroller (ATmega328) for getting accurate EMG signal. Finally, the processed EMG signal was classified into 6 different levels in order to display the muscle strength level of the user. This EMG device can be used to help the weak person or an elderly to identity their strength level of muscle for clinical rehabilitation purpose.
CLASSIFICATION OF ARM MOVEMENT BASED ON UPPER LIMB MUSCLE SIGNAL FOR REHABILITATION DEVICE
Rehabilitation device is used as an exoskeleton for people who experience limb failure. Arm rehabilitation device may ease the rehabilitation programme for those who suffer arm dysfunctional. The device used to facilitate the tasks of the program should improve the electrical activity in the motor unit by minimising the mental effort of the user. Electromyography (EMG) is the techniques to analyse the presence of electrical activity in musculoskeletal systems. The electrical activity in muscles of disable person are failed to contract the muscle for movements. To prevent the muscles from paralysis becomes spasticity or flaccid the force of movements has to minimise the mental efforts. To minimise the used of cerebral strength, analysis on EMG signals from normal people are conducted before it can be implement in the device. The signals are collect according to procedure of surface electromyography for non-invasive assessment of muscles (SENIAM). The implementation of EMG signals is to set the movements' pattern of the arm rehabilitation device. The filtered signal further the process by extracting the features as follows; Standard Deviation(STD), Mean Absolute Value(MAV), Root Mean Square(RMS), Zero Crossing(ZCS) and Variance(VAR). The extraction of EMG data is to have the reduced vector in the signal features for minimising the signals error than can be implement in classifier. The classification of features is by SOM-Toolbox using MATLAB. The features extraction of EMG signals is classified into several degree of arm movement visualize in U-Matrix form.
2018 21st International Conference of Computer and Information Technology (ICCIT), 2019
Recently, Human Machine Interface (HMI) has become an important part of medical technology where different bio-signals such as EOG, EMG, and EEG can be deployed to develop a closed-loop control system for physically disabled and elderly people to improve their quality of life. On the other hand, as the road and industrial accidents are increasing in countries like Bangladesh, more and more people are losing body parts and are not able to treat their condition properly due to the financial burden and lack in technological advancement. In this research, we have preliminarily designed, implemented and tested a low-cost EMG recording and monitoring system that can detect and process EMG signals from different kinds of muscle contraction. The recorded signals can be interfaced with the computer through Arduino UNO and then the EMG signals can be analyzed further in MATLAB platform. We have also developed an algorithm to detect muscle contraction and expansion from raw EMG recordings and then converted them into command signals that can control a robotic arm. In order to demonstrate the efficacy of the proposed system, lab experiments are performed by recording of EMG signals from 5 subjects by attaching electrodes on their shoulders and calculated the accuracy of detecting muscle contraction based on four ROC parameters, known as True Positives (TP) False Positives (FP), True Negatives (TN) and False Negatives (FN); which results in 83% accuracy on an average. Then based on the detected muscle contraction, we successfully demonstrated to control a robotic arm by opening and closing of its fingers which can later be replaced with a prosthetic arm for those disabled persons who lost their arms. This research will help not only to diagnose any neuromuscular disease by comparing it with any healthy subject’s EMG signal, but also these EMG recordings can be processed and decoded to control prosthetic arm for those people who lost their arm but cannot afford commercially available expensive prosthetic systems in a developing country like Bangladesh.