This document presents a study comparing muscle activity characterization using standard electromyography (EMG) and a novel non-contact technique called Laser Doppler Myography (LDMi). Three muscle activity parameters - muscle activation timing, signal amplitude, and muscle fatigue - were analyzed using both techniques on the flexor carpi ulnaris and tibialis anterior muscles of 20 subjects. The results showed good correlation between EMG and LDMi for all three parameters, with maximum differences of 440ms in timing and Pearson correlation coefficients above 0.88. This suggests LDMi is a reliable non-contact method for measuring muscle activity characteristics traditionally obtained via EMG.
Embedded system for upper-limb exoskeleton based on electromyography controlTELKOMNIKA JOURNAL
A major problem in an exoskeleton based on electromyography (EMG) control with pattern recognition-based is the need for more time to train and to calibrate the system in order able to adapt for different subjects and variable. Unfortunately, the implementation of the joint prediction on an embedded system for the exoskeleton based on the EMG control with non-pattern recognition-based is very rare. Therefore, this study presents an implementation of elbow-joint angle prediction on an embedded system to control an upper limb exoskeleton based on the EMG signal. The architecture of the system consisted of a bio-amplifier, an embedded ARMSTM32F429 microcontroller, and an exoskeleton unit driven by a servo motor. The elbow joint angle was predicted based on the EMG signal that is generated from biceps. The predicted angle was obtained by extracting the EMG signal using a zero-crossing feature and filtering the EMG feature using a Butterworth low pass filter. This study found that the range of root mean square error and correlation coefficients are 8°-16° and 0.94-0.99, respectively which suggest that the predicted angle is close to the desired angle and there is a high relationship between the predicted angle and the desired angle.
Application of EMG and Force Signals of Elbow Joint on Robot-assisted Arm Tra...TELKOMNIKA JOURNAL
Flexion-extension based on the system's robotic arm has the potential to increase the patient's elbow joint movement. The force sensor and electromyography signals can support the biomechanical system to detect electrical signals generated by the muscles of the biological. The purpose of this study is to implement the design of force sensor and EMG signals application on the elbow flexion motion of the upper arm. In this experiments, the movements of flexion at an angle of 45º, 90º and 135º is applied to identify the relationship between the amplitude of the EMG and force signals on every angle. The contribution of this research is for supporting the development of the Robot-Assisted Arm Training. The correlation between the force signal and the EMG signal from the subject studied in the elbow joint motion tests. The application of sensors tested by an experimental on healthy subjects to simulating arm movement. The experimental results show the relationship between the amplitude of the EMG and force signals on flexion angle of the joint mechanism for monitoring the angular displacement of the robotic arm. Further developments in the design of force sensor and EMG signals are potentially for open the way for the next researches based on the physiological condition of each patient.
Myoelectric Prosthetic Arm Motion (Wrist/Hand) Control Systemidescitation
In India most of the people lost their hand due to road accident, disease and soldiers lost their
arm in war. This paper describes the design which controls the hand motion and wrist motion of
Myoelectric controlled prosthetic arm using cortex M3 microcontroller. In this design electromyogram
signals are generated by contracting the muscles of biceps and sensed by electrode sensors. Electrode
sensors produce the electrical signals and these signals are processed by microcontroller and achieve the
supination motion from 00 to 750 and pronation motion from 00 to 850 in the wrist of hand[1][2].
Prosthetic hand using Artificial Neural NetworkSreenath S
Real Time Moving Prosthetic.
It's an innovative technology,improvising the prosthetic field with the application of Artificial Neural Network technology.Unlike anyother prosthetic hand, this has a Real Time data accquisition system which varies the data set according to the input signal.This is customisable to any amputee. The hardware was developed by simple and easily available materials.We have come up with a new technology in the prosthetic field.
Embedded system for upper-limb exoskeleton based on electromyography controlTELKOMNIKA JOURNAL
A major problem in an exoskeleton based on electromyography (EMG) control with pattern recognition-based is the need for more time to train and to calibrate the system in order able to adapt for different subjects and variable. Unfortunately, the implementation of the joint prediction on an embedded system for the exoskeleton based on the EMG control with non-pattern recognition-based is very rare. Therefore, this study presents an implementation of elbow-joint angle prediction on an embedded system to control an upper limb exoskeleton based on the EMG signal. The architecture of the system consisted of a bio-amplifier, an embedded ARMSTM32F429 microcontroller, and an exoskeleton unit driven by a servo motor. The elbow joint angle was predicted based on the EMG signal that is generated from biceps. The predicted angle was obtained by extracting the EMG signal using a zero-crossing feature and filtering the EMG feature using a Butterworth low pass filter. This study found that the range of root mean square error and correlation coefficients are 8°-16° and 0.94-0.99, respectively which suggest that the predicted angle is close to the desired angle and there is a high relationship between the predicted angle and the desired angle.
Application of EMG and Force Signals of Elbow Joint on Robot-assisted Arm Tra...TELKOMNIKA JOURNAL
Flexion-extension based on the system's robotic arm has the potential to increase the patient's elbow joint movement. The force sensor and electromyography signals can support the biomechanical system to detect electrical signals generated by the muscles of the biological. The purpose of this study is to implement the design of force sensor and EMG signals application on the elbow flexion motion of the upper arm. In this experiments, the movements of flexion at an angle of 45º, 90º and 135º is applied to identify the relationship between the amplitude of the EMG and force signals on every angle. The contribution of this research is for supporting the development of the Robot-Assisted Arm Training. The correlation between the force signal and the EMG signal from the subject studied in the elbow joint motion tests. The application of sensors tested by an experimental on healthy subjects to simulating arm movement. The experimental results show the relationship between the amplitude of the EMG and force signals on flexion angle of the joint mechanism for monitoring the angular displacement of the robotic arm. Further developments in the design of force sensor and EMG signals are potentially for open the way for the next researches based on the physiological condition of each patient.
Myoelectric Prosthetic Arm Motion (Wrist/Hand) Control Systemidescitation
In India most of the people lost their hand due to road accident, disease and soldiers lost their
arm in war. This paper describes the design which controls the hand motion and wrist motion of
Myoelectric controlled prosthetic arm using cortex M3 microcontroller. In this design electromyogram
signals are generated by contracting the muscles of biceps and sensed by electrode sensors. Electrode
sensors produce the electrical signals and these signals are processed by microcontroller and achieve the
supination motion from 00 to 750 and pronation motion from 00 to 850 in the wrist of hand[1][2].
Prosthetic hand using Artificial Neural NetworkSreenath S
Real Time Moving Prosthetic.
It's an innovative technology,improvising the prosthetic field with the application of Artificial Neural Network technology.Unlike anyother prosthetic hand, this has a Real Time data accquisition system which varies the data set according to the input signal.This is customisable to any amputee. The hardware was developed by simple and easily available materials.We have come up with a new technology in the prosthetic field.
This paper will review the works on Surface Electromyography (SEMG) signal acquisition and controlling as well as the uses of SEMG signals analysis for Transfemoral amputee's people. In the beginning, this paper will briefly go through the basic theory of myoelectric signal generation. Next, the signal acquisition & filtering techniques applied for SEMG signal will be explained. Then after this EMG signal control or actuate the myoelectric leg who was suffering from Transfemoral amputee using microcontroller. This paper gives the better controlling SEMG signal and also very smooth and easy controlling of the Prosthetic leg motor using Myoelectric Controller.
Estimation of Arm Joint Angles from Surface Electromyography signals using Ar...IOSR Journals
Abstract: Vicon system is implemented in almost every motion analysis systems. It has many applications like
robotics, gaming, virtual reality and animated movies. The motion and orientation plays an important role in
the above mentioned applications. In this paper we propose a method to estimate arm joint angles from surface
Electromyography (s-EMG) signals using Artificial Neural Network (ANN). The neural network is trained with
EMG data from wrist flexion and extension action as input and joint angle values from the vicon system as
target. The results shown in this paper illustrate the neural network performance in estimating the joint angle
values during offline testing.
Index Terms: Vicon system, Joint angle, Surface EMG, Artificial Neural Network, Virtual reality, Robotics.
This power point presentation is presented by Satyajit Mohanty, MSPT,MIAP, MHPC(UK), a specialist physiotherapist in sports physiotherapists. This presentation till take you through the manual therapy prospective of lumbar spinal paraspinal EMG.
have a happy reading. Thank you.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
EMG Driven IPMC Based Artificial Muscle FingerAbida Zama
The medical, rehabilitation and bio-mimetic technology demands human actuated devices which can support in the daily life activities such as functional assistance or functional substitution of human organs. These devices can be used in the form of prosthetic, skeletal and artificial muscles devices. However, we still have some difficulties in the practical use of these devices. The major challenges to overcome are the acquisition of the user’s intention from his or her bionic signals and to provide with an appropriate control signal for the device. Also, we need to consider the mechanical design issues such as lightweight and small size with flexible behavior etc. For the bionic signals, the electromyography (EMG) signal can be used to control these devices, which reflect the muscles motion, and can be acquired from the body surface. We are familiar with the fact that Ionic polymer metal composite (IPMC) has tremendous potential as an artificial muscle. In place of the supply voltage from external source for actuating an IPMC, EMG signal can be used where EMG electrodes show a reliable approach to extract voltage signal from body. Using this voltage signal via EMG sensor, IPMC can illustrate the bio-mimetic behavior through the movement of human muscles. Therefore, an IPMC is used as an artificial muscle finger for the bio-mimetic/micro robot.
Emg driven ipmc based artificial muscle finger Abida Zama
The medical, rehabilitation and bio-mimetic technology demands human actuated devices which can support in the daily life activities such as functional assistance or functional substitution of human organs. These devices can be used in the form of prosthetic, skeletal and artificial muscles devices. However, we still have some difficulties in the practical use of these devices. The major challenges to overcome are the acquisition of the user’s intention from his or her bionic signals and to provide with an appropriate control signal for the device. Also, we need to consider the mechanical design issues such as lightweight and small size with flexible behavior etc. For the bionic signals, the electromyography (EMG) signal can be used to control these devices, which reflect the muscles motion, and can be acquired from the body surface. We are familiar with the fact that Ionic polymer metal composite (IPMC) has tremendous potential as an artificial muscle. This can be stimulated by supplying a small voltage of 3V and shows evidence of a large bending behavior. In place of the supply voltage from external source for actuating an IPMC, EMG signal can be used where EMG electrodes show a reliable approach to extract voltage signal from body. Using this voltage signal via EMG sensor, IPMC can illustrate the bio-mimetic behavior through the movement of human muscles. Therefore, an IPMC is used as an artificial muscle finger for the bio-mimetic/micro robot.
A Novel Displacement-amplifying Compliant Mechanism Implemented on a Modified...IJECEIAES
The micro-accelerometers are devices used to measure acceleration. They are implemented in applications such as tilt-control in spacecraft, inertial navigation, oil exploration, etc. These applications require high operating frequency and displacement sensitivity. But getting both high parameter values at the same time is difficult, because there are physical relationships, for each one, where the mass is involved. When the mass is reduced, the operating frequency is high, but the displacement sensitivity decreases and vice versa. The implementation of Displacement-amplifying Compliant Mechanism (DaCM) supports to this dependence decreases. In this paper the displacement sensitivity and operation frequency of a Conventional Capacitive Accelerometer are shown (CCA). A Capacitive Accelerometer with Extended Beams (CAEB) is also presented, which improves displacement sensitivity compared with CCA, and finally the implementation of DACM´s in the aforementioned devices was also carried out. All analyzed cases were developed considering the in-plane mode. The Matlab code used to calculate displacement sensitivity and operating frequency relationship is given in Appendix A.
Acrylic Prosthetic Limb Using EMG signalAnveshChinta1
The topic deals with the development of a prosthetic limb made of the acrylic sheet using electromyography signals for the people who lost a part of their limb due to circulation problems from atherosclerosis or diabetes, traumatic injuries occurring due to traffic accidents and military combat, cancer or birth
effects. Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. An electromyography (EMG) detects the electrical potential generated by muscle cells when these cells are electrically or neurologically activated. Measured EMG potentials range between 2 millivolts to 4 millivolts depending on the muscle under observation. The two surface electrodes are attached to the healthy limb and sense the muscle contraction when a movement is made. This output is given to the arduino
microcontroller, this controller is programmed that, it acquires the angle and transformation link obtained due to the locomotion of normal limb.
The output signals are given to the servo motor
through servo driver and then to the prosthetic
limb. The methodology adapted here provides
locomotive action for the prosthetic limb. The
major advantage of the proposed system is that usage of acrylic sheets reduces the weight of the
prosthetic limb to a greater extent. This cost-effective acrylic prosthetic limb avoids any
irritation or side effects to the one who carries it.
Correlation Analysis of Electromyogram SignalsIJMTST Journal
An inability to adapt myoelectric interfaces to a user’s unique style of hand motion. The system also adapts
the motion style of an opposite limb. These are the important factors inhibiting the practical application of
myoelectric interfaces. This is mainly attributed to the individual differences in the exhibited electromyogram
(EMG) signals generated by the muscles of different limbs. In this project myoelectric interface easily adapts
the signal from the users and maintains good movement recognition performance. At the initial stage the
myoelectric signal is extracted from the user by using the data acquisition system. A new set of features
describing the movements of user’s is extracted and the user’s features are classifed using SVM
classification. The given signal is then compared with the database signal with the accuracy of 90.910 %
across all the EMG signals.
This paper will review the works on Surface Electromyography (SEMG) signal acquisition and controlling as well as the uses of SEMG signals analysis for Transfemoral amputee's people. In the beginning, this paper will briefly go through the basic theory of myoelectric signal generation. Next, the signal acquisition & filtering techniques applied for SEMG signal will be explained. Then after this EMG signal control or actuate the myoelectric leg who was suffering from Transfemoral amputee using microcontroller. This paper gives the better controlling SEMG signal and also very smooth and easy controlling of the Prosthetic leg motor using Myoelectric Controller.
Estimation of Arm Joint Angles from Surface Electromyography signals using Ar...IOSR Journals
Abstract: Vicon system is implemented in almost every motion analysis systems. It has many applications like
robotics, gaming, virtual reality and animated movies. The motion and orientation plays an important role in
the above mentioned applications. In this paper we propose a method to estimate arm joint angles from surface
Electromyography (s-EMG) signals using Artificial Neural Network (ANN). The neural network is trained with
EMG data from wrist flexion and extension action as input and joint angle values from the vicon system as
target. The results shown in this paper illustrate the neural network performance in estimating the joint angle
values during offline testing.
Index Terms: Vicon system, Joint angle, Surface EMG, Artificial Neural Network, Virtual reality, Robotics.
This power point presentation is presented by Satyajit Mohanty, MSPT,MIAP, MHPC(UK), a specialist physiotherapist in sports physiotherapists. This presentation till take you through the manual therapy prospective of lumbar spinal paraspinal EMG.
have a happy reading. Thank you.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
EMG Driven IPMC Based Artificial Muscle FingerAbida Zama
The medical, rehabilitation and bio-mimetic technology demands human actuated devices which can support in the daily life activities such as functional assistance or functional substitution of human organs. These devices can be used in the form of prosthetic, skeletal and artificial muscles devices. However, we still have some difficulties in the practical use of these devices. The major challenges to overcome are the acquisition of the user’s intention from his or her bionic signals and to provide with an appropriate control signal for the device. Also, we need to consider the mechanical design issues such as lightweight and small size with flexible behavior etc. For the bionic signals, the electromyography (EMG) signal can be used to control these devices, which reflect the muscles motion, and can be acquired from the body surface. We are familiar with the fact that Ionic polymer metal composite (IPMC) has tremendous potential as an artificial muscle. In place of the supply voltage from external source for actuating an IPMC, EMG signal can be used where EMG electrodes show a reliable approach to extract voltage signal from body. Using this voltage signal via EMG sensor, IPMC can illustrate the bio-mimetic behavior through the movement of human muscles. Therefore, an IPMC is used as an artificial muscle finger for the bio-mimetic/micro robot.
Emg driven ipmc based artificial muscle finger Abida Zama
The medical, rehabilitation and bio-mimetic technology demands human actuated devices which can support in the daily life activities such as functional assistance or functional substitution of human organs. These devices can be used in the form of prosthetic, skeletal and artificial muscles devices. However, we still have some difficulties in the practical use of these devices. The major challenges to overcome are the acquisition of the user’s intention from his or her bionic signals and to provide with an appropriate control signal for the device. Also, we need to consider the mechanical design issues such as lightweight and small size with flexible behavior etc. For the bionic signals, the electromyography (EMG) signal can be used to control these devices, which reflect the muscles motion, and can be acquired from the body surface. We are familiar with the fact that Ionic polymer metal composite (IPMC) has tremendous potential as an artificial muscle. This can be stimulated by supplying a small voltage of 3V and shows evidence of a large bending behavior. In place of the supply voltage from external source for actuating an IPMC, EMG signal can be used where EMG electrodes show a reliable approach to extract voltage signal from body. Using this voltage signal via EMG sensor, IPMC can illustrate the bio-mimetic behavior through the movement of human muscles. Therefore, an IPMC is used as an artificial muscle finger for the bio-mimetic/micro robot.
A Novel Displacement-amplifying Compliant Mechanism Implemented on a Modified...IJECEIAES
The micro-accelerometers are devices used to measure acceleration. They are implemented in applications such as tilt-control in spacecraft, inertial navigation, oil exploration, etc. These applications require high operating frequency and displacement sensitivity. But getting both high parameter values at the same time is difficult, because there are physical relationships, for each one, where the mass is involved. When the mass is reduced, the operating frequency is high, but the displacement sensitivity decreases and vice versa. The implementation of Displacement-amplifying Compliant Mechanism (DaCM) supports to this dependence decreases. In this paper the displacement sensitivity and operation frequency of a Conventional Capacitive Accelerometer are shown (CCA). A Capacitive Accelerometer with Extended Beams (CAEB) is also presented, which improves displacement sensitivity compared with CCA, and finally the implementation of DACM´s in the aforementioned devices was also carried out. All analyzed cases were developed considering the in-plane mode. The Matlab code used to calculate displacement sensitivity and operating frequency relationship is given in Appendix A.
Acrylic Prosthetic Limb Using EMG signalAnveshChinta1
The topic deals with the development of a prosthetic limb made of the acrylic sheet using electromyography signals for the people who lost a part of their limb due to circulation problems from atherosclerosis or diabetes, traumatic injuries occurring due to traffic accidents and military combat, cancer or birth
effects. Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. An electromyography (EMG) detects the electrical potential generated by muscle cells when these cells are electrically or neurologically activated. Measured EMG potentials range between 2 millivolts to 4 millivolts depending on the muscle under observation. The two surface electrodes are attached to the healthy limb and sense the muscle contraction when a movement is made. This output is given to the arduino
microcontroller, this controller is programmed that, it acquires the angle and transformation link obtained due to the locomotion of normal limb.
The output signals are given to the servo motor
through servo driver and then to the prosthetic
limb. The methodology adapted here provides
locomotive action for the prosthetic limb. The
major advantage of the proposed system is that usage of acrylic sheets reduces the weight of the
prosthetic limb to a greater extent. This cost-effective acrylic prosthetic limb avoids any
irritation or side effects to the one who carries it.
Correlation Analysis of Electromyogram SignalsIJMTST Journal
An inability to adapt myoelectric interfaces to a user’s unique style of hand motion. The system also adapts
the motion style of an opposite limb. These are the important factors inhibiting the practical application of
myoelectric interfaces. This is mainly attributed to the individual differences in the exhibited electromyogram
(EMG) signals generated by the muscles of different limbs. In this project myoelectric interface easily adapts
the signal from the users and maintains good movement recognition performance. At the initial stage the
myoelectric signal is extracted from the user by using the data acquisition system. A new set of features
describing the movements of user’s is extracted and the user’s features are classifed using SVM
classification. The given signal is then compared with the database signal with the accuracy of 90.910 %
across all the EMG signals.
A comparative study of wavelet families for electromyography signal classific...journalBEEI
Automatic detection of neuromuscular disorders performed using electromyography (EMG) has become an interesting domain for many researchers. In this paper, we present an approach to evaluate and classify the non-stationary EMG signals based on discrete wavelet transform (DWT). Most often researches did not consider the effect of DWT factors on the performance of EMG signals classification. This problem is still an interesting unsolved challenge. However, the selection of appropriate mother wavelet and related level decomposition is an essential issue that should be addressed in DWT-based EMG signals classification. The proposed method consists of decomposing a raw EMG signal into different sub-bands. Several statistical features were extracted from each sub-band and six wavelet families were investigated. The feature vector was used as inputs to support vector machine (SVM) classifier for the diagnosis of neuromuscular disorders. The obtained results achieve satisfactory performances with optimal DWT factors using 10-fold cross-validation. From the classification performances, it was found that sym14 is the most suitable mother wavelet at the 8th optimal wavelet level of decomposition. These simulation results demonstrated that the proposed method is very reliable for reducing cost computational time of automated neuromuscular disorders system and removing the redundancy information.
Electromyography Analysis for Person IdentificationCSCJournals
Physiological descriptions of the electromyography signal and other literature say that when we make a motion, the motor neurons of respective muscle get activated and all the innervated motor units in that zone produce motor unit action potential. These motor unit action potentials travel through the muscle fibers with conduction velocity and superimposed signal gets recorded at the electrode site. Here we have taken an analogy from the speech production system model as the excitation signal travels through vocal tract to produce speech; similarly, an impulse train of firing rate frequency goes through the system with impulse response of motor unit action potentials and travels along the muscle fiber of that person. As the vocal tract contains the speaker information, we can also separate the muscle fiber pattern part and motor unit discharge pattern through proper selection of features and its classification to identify the respective person. Cepstral and non uniform filter bank features models the variation in the spectrum of the signals. Vector quantization and Gaussian mixture model are the two techniques of pattern matching have been applied.
Non-uniform electromyographic activity during fatigue and recovery of the vas...Nosrat hedayatpour
The aim of the study was to investigate EMG signal features
during fatigue and recovery at three locations of the vastus
medialis and lateralis muscles.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Comparative analysis of machine learning algorithms on myoelectric signal fro...IAESIJAI
Control strategies of smart hand prosthesis-based myoelectric signals in recent years don't provide the patients with the sensation of biological control of prostheses hand fingers. Therefore, in current work hyperparameters optimization in machine learning algorithm and hand gesture recognition techniques were applied to the myoelectric signal-based on residual muscles contraction of the amputees corresponding to intact forearm limb movement to improve their biological control. In this paper, myoelectric signals are extracted using the MYO armband to recognize ten gestures from ten volunteers (healthy and transradial amputation) on the forearm, thereafter the noise of myoelectric signals using a notch filter (NF) is removed. The proposed classification system involved two machine learning algorithms: (1) the decision tree (DT), tri-layered neural network (TLNN), k-nearest-neighbor (KNN), support vector machine (SVM) and ensemble boosted tree (EBT) classifiers. (2) the optimized machine learning classifiers, i.e., OKNN, OSVM, OEBT with optical diffraction tomography (ODT) and ommatidia detecting algorithm (ODA). The experimental results of classifiers comparison pointed out an algorithm that outperformed with high accuracy is OEBT closely followed by OKNN achieves an accuracy of 97.8% and 97.1% for intact forearm limb, while for transradial amputation with an accuracy of 91.9% and 91.4%, respectively.
Effect of Endurance on Gastrocnemius Muscle with Exercise by Employing EMG Am...ijtsrd
Muscle fatigue is a common experience in daily life. Many authors have defined it as the incapacity to maintain the required or expected force, and therefore, force, power and torque recordings have been used as direct measurements of muscle fatigue. In addition, the measurement of these variables combined with the measurement of surface electromyography sEMG recordings which can be measured during all types of movements during exercise may be useful to assess and understand muscle fatigue. EMG signal can be easily analyzed in time domain, frequency domain and time frequency domain. The time domain features are the most popular in EMG pattern recognition because they are easy and quick to calculate and they do not require a transformation. The purpose of this study was to analyze the fatigue and to study the endurance occurrence in the Gastrocnemius muscle with a pre defined exercise protocol for the targeted muscle. For this purpose, sEMG Amplitude parameters were characterized. Relation between EMG features like mean, force, standard deviation, etc. is verified for fatigue detection as well as to identify the Endurance developed in the Gastrocnemius muscle. Gaurav Patti | Poonam Kumari "Effect of Endurance on Gastrocnemius Muscle with Exercise by Employing EMG Amplitude Parameters" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33222.pdf Paper Url :https://www.ijtsrd.com/engineering/other/33222/effect-of-endurance-on-gastrocnemius-muscle-with-exercise-by-employing-emg-amplitude-parameters/gaurav-patti
Hand motion pattern recognition analysis of forearm muscle using MMG signalsjournalBEEI
Surface Mechanomyography (MMG) is the recording of mechanical activity of muscle tissue. MMG measures the mechanical signal (vibration of muscle) that generated from the muscles during contraction or relaxation action. It is widely used in various fields such as medical diagnosis, rehabilitation purpose and engineering applications. The main purpose of this research is to identify the hand gesture movement via VMG sensor (TSD250A) and classify them using Linear Discriminant Analysis (LDA). There are four channels MMG signal placed into adjacent muscles which PL-FCU and ED-ECU. The features used to feed the classifier to determine accuracy are mean absolute value, standard deviation, variance and root mean square. Most of subjects gave similar range of MMG signal of extraction values because of the adjacent muscle. The average accuracy of LDA is approximately 87.50% for the eight subjects. The finding of the result shows, MMG signal of adjacent muscle can affect the classification accuracy of the classifier.
Application of gabor transform in the classification of myoelectric signalTELKOMNIKA JOURNAL
In recent day, Electromyography (EMG) signal are widely applied in myoelectric control. Unfortunately, most of studies focused on the classification of EMG signals based on healthy subjects. Due to the lack of study in amputee subject, this paper aims to investigate the performance of healthy and amputee subjects for the classification of multiple hand movement types. In this work, Gabor transform (GT) is used to transform the EMG signal into time-frequency representation. Five time-frequency features are extracted from GT coefficient. Feature extraction is an effective way to reduce the dimensionality, as well as keeping the valuable information. Two popular classifiers namely k-nearest neighbor (KNN) and support vector machine (SVM) are employed for performance evaluation. The developed system is evaluated using the EMG data acquired from the publicy available NinaPro Database. The results revealed that the extracting GT features can achieve promising performance in the classification of EMG signals.
A robotic arm is a Programmable mechanical arm which copies the functions of the human arm. They
are widely used in industries. Human robot-controlled interfaces mainly focus on providing rehabilitation to
amputees in order to overcome their amputation or disability leading them to live a normal life. The major
objective of this project is to develop a movable robotic arm controlled by EMG signals from the muscles of the
upper limb. In this system, our main aim is on providing a low 2-dimensional input derived from emg to move the
arm. This project involves creating a prosthesis system that allows signals recorded directly from the human body.
The arm is mainly divided into 2 parts, control part and moving part. Movable part contains the servo motor
which is connected to the Arduino Uno board, and it helps in developing a motion in accordance with the EMG
signals acquired from the body. The control part is the part that is controlled by the operation according to the
movement of the amputee. Mainly the initiation of the movement for the threshold fixed in the coding. The major
aim of the project is to provide an affordable and easily operable device that helps even the poor sections of the
amputated society to lead a happier and normal life by mimicking the functions of the human arm in terms of both
the physical, structural as well as functional aspects.
1. This content has been downloaded from IOPscience. Please scroll down to see the full text.
Download details:
IP Address: 193.205.129.194
This content was downloaded on 16/11/2015 at 11:05
Please note that terms and conditions apply.
Muscle activity characterization by laser Doppler Myography
View the table of contents for this issue, or go to the journal homepage for more
2013 J. Phys.: Conf. Ser. 459 012017
(http://iopscience.iop.org/1742-6596/459/1/012017)
Home Search Collections Journals About Contact us My IOPscience
2. Muscle activity characterization by laser Doppler Myography
Lorenzo Scalise, Sara Casaccia, Paolo Marchionni, Ilaria Ercoli, Enrico Primo
Tomasini
Dipartimento di Ingegneria Industriale e Scienze Matematiche (DIISM)
Università Politecnica delle Marche, 60131, Ancona, ITALY
E-mail: l.scalise@univpm.it
Abstract. Electromiography (EMG) is the gold-standard technique used for the evaluation of
muscle activity. This technique is used in biomechanics, sport medicine, neurology and
rehabilitation therapy and it provides the electrical activity produced by skeletal muscles.
Among the parameters measured with EMG, two very important quantities are: signal
amplitude and duration of muscle contraction, muscle fatigue and maximum muscle power.
Recently, a new measurement procedure, named Laser Doppler Myography (LDMi), for the
non contact assessment of muscle activity has been proposed to measure the vibro-mechanical
behaviour of the muscle.
The aim of this study is to present the LDMi technique and to evaluate its capacity to measure
some characteristic features proper of the muscle. In this paper LDMi is compared with
standard superficial EMG (sEMG) requiring the application of sensors on the skin of each
patient. sEMG and LDMi signals have been simultaneously acquired and processed to test
correlations. Three parameters has been analyzed to compare these techniques: Muscle
activation timing, signal amplitude and muscle fatigue. LDMi appears to be a reliable and
promising measurement technique allowing the measurements without contact with the patient
skin.
1. Introduction
Electromyography (EMG) is the most common biomedical instrumentation used for the evaluation of
muscles activity [1]. EMG uses needle electrodes (invasive measurement of EMG) or adhesive skin
electrodes which are placed in contact with skin (non-invasive measurement of EMG) to measure the
myoelectric activity. In this work, we use the latter method to measure the EMG signal that is
commonly known as surface electromyography (sEMG) [1,2]. sEMG is not always easy to be
performed because it requires the patient collaboration and the possibility to apply the electrodes to the
skin; it is therefore required an accurate skin preparation (removing eventual hair, skin detersion, etc.)
and particular attention to the placement of the electrodes and cable positioning.
The aim of this study is demonstrate that it is possible to evaluate some parameters related to the
muscle activity without contact by means of a novel optical measurement method (Laser Doppler
myography, LDMi [3]). This is possible because the muscle contraction is characterized by two
component: the electrical component measured by sEMG and the mechanical component measured by
Laser Doppler myography. So, the Laser Doppler vibrometer measures the muscle vibrations [3,4]
during an isometric muscle contraction. In recent years, the Laser Doppler vibrometer has been widely
used for biomedical applications and it has already been demonstrated to be a valid instrument for the
assessment of the vibrational behaviour of the human skin[3-7].
IMEKO 2013 TC1 + TC7 + TC13 IOP Publishing
Journal of Physics: Conference Series 459 (2013) 012017 doi:10.1088/1742-6596/459/1/012017
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution
of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Published under licence by IOP Publishing Ltd 1
3. 2. Materials and methods
The study is focused on the detection of the muscle contraction from different muscles: flexor carpi
ulnaris and tibialis anterior. The test involved the simultaneous acquisition of the sEMG signal and the
LDMi signal during an isometric contraction of the muscle. In order to record the sEMG signal it is
necessary to apply two Ag-AgCl electrodes on the skin in correspondence to the right and left flexor
carpi ulnaris/tibialis anterior while a reference electrode is fixed on the wrist for the flexor carpi
ulnaris and on the ankle for the tibialis anterior (bipolar configuration, fig.1). Before electrodes
application, the skin must be shaved and cleaned and cables were fixed in order to limit the noise.
During the test on the forearm carpi ulnaris, the subject was asked to seat on a chair with the elbow
flexed at the right angle and the dorsal side of the forearm in a horizontal downwards position and for
the muscle contraction he must contract the hand holding an object. While for the test on the tibialis
anterior the subject was seated with the leg stretched and he brought the tip of the foot upward without
extension of the great toe. The LDMi signal is measured with a single point system (PDV100;
calibration accuracy ±0.05 mm/s, bandwidth 0.05 Hz- 22 kHz, spot dimension < 1 mm, working
wavelength He-Ne 632.8 nm). The velocity of vibration of the measurement point was measured. The
laser beam was pointed perpendicular to the skin surface at a distance of about 30 cm and the laser
spot was pointed between the skin electrodes. sEMG and LDMi signals are acquired by a 12 bit
acquisition board (ML865 PowerLab 4/25T). The surface EMG signal is filtered by a band-pass filter
(10-200 Hz) and by a Notch filter to eliminate 50 Hz noise. Both the acquired signals are sampled at
1kHz, synchronously. The experimental setup is reported in fig.1.
Figure 1. Experimental setup used for the tests.
Tests have been performed on 20 subjects (10 females and 10 males), of mean weight 65 ± (10) kg,
height 170 ± (10) cm, and 24 ± (5) years. All subjects have been measured on flexor carpi ulnaris and
tibialis anterior (left and right). They were trained with the experimental protocols before the test. To
investigate that the LDMi method is valid to determine the muscle contraction, we have calculated the
RMS (root mean square) of the signals which can be considered as an index of the signal sensitivity
and an index of muscle fatigue [8].
3. Results
3.1 Timing of muscle activity
The detection of the muscle activation has been performed using a threshold algorithm. This method
allows to evaluate activation events analyzing the RMS of the signals. This algorithm is applied to
IMEKO 2013 TC1 + TC7 + TC13 IOP Publishing
Journal of Physics: Conference Series 459 (2013) 012017 doi:10.1088/1742-6596/459/1/012017
2
4. both the acquired signals (sEMG and LDMi) to evaluate the differences of timing of the signal
activations measured by the sEMG and the LDMi (tab.1). The threshold to discriminate the muscle
contraction was based on the rest condition. A muscle activation is detected when the RMS of the
signal reaches values higher then the threshold for a at least 100 samples. Each test was characterized
by 3s of rest, 10s of muscle activation and of 3s of rest after the contraction and each subject was
asked to repeat the test 4 times.
Table 1. Mean and standard deviation of the duration of the muscle contraction, activation and
deactivation time for sEMG and LDMi
Deviation between sEMG and LDMi values
t2-t1 t1 t2
Right flexor carpi ulnaris
(mean±SD)
0.12±0.09 0.01±0.05 0.13±0.09
Left flexor carpi ulnaris
(mean±SD)
0.23±0.44 -0.008±0.04 0.22±0.06
Right tibialis anterior
(mean±SD)
0.18±0.13 -0.07±0.04 0.12±0.11
Left tibialis anterior
(mean±SD)
0.15±0.12 -0.05±0.03 0.10±0.09
The activation on the sEMG signal is characterized by a clear increase of the RMS signal amplitude
respect to the amplitude of the RMS of the noise. The deactivation phase is clearly visible by the
decreasing of the RMS signal amplitude. For the LDMi signal, the activation starts with the first group
of peaks and it finishes before the last group of peaks. An example of the events detection
(activation/deactivation) is shown in fig.2 for both the signals (sEMG and LDVi):
Figure 2. Timing of muscle contraction: sEMG signal and LDMi signal (t2-t1=total
muscle activation)
IMEKO 2013 TC1 + TC7 + TC13 IOP Publishing
Journal of Physics: Conference Series 459 (2013) 012017 doi:10.1088/1742-6596/459/1/012017
3
5. 3.2 Signal amplitude
The signal amplitude was evaluated by the calculation of the RMS value of the acquired signal during
the activation and rest phases on sEMG signal and LDMi signal (fig.2). The ratio of the RMS values
of each of two signals (sEMG and LDMi) during activation (t2-t1) and rest (t1) was then calculated.
This ratio is called Signal to Noise Ratio (S/N). With this parameter is possible to evaluate the
sensitivity of the signal on the noise.
1
2
1
0
2
1
2
12
1
1
/
t
j
j
t
ti
i
rest
t
ractionmusclecont
tt
NS
(eq. 1)
LDMi signal was filtered in order to remove noise; a wavelet filter was applied using a Sym4 mother
for the case of the flexor carpi ulnaris acquisition, while the Sym2 was used for the case of the tibialis
anterior. Correlation between S/N of the sEMG signals and S/N LDMi signals - calculated using eq. 1
– are reported in fig.3, fig.4 and fig.5.
Figure 3. Correlation between S/N of sEMG
signals and LDMi signals for the right and left
flexor carpi ulnaris (Pearson’s coefficient: 0.95).
Figure 4. Correlation between S/N of sEMG
signals and LDMi signals for the right and left
tibialis anterior (Pearson’s coefficient: 0.89).
Figure 5. Correlation between S/N of sEMG signals and LDMi
signals for both the flexor carpi ulnaris and the tibialis anterior
(Pearson’s coefficient: 0.93).
IMEKO 2013 TC1 + TC7 + TC13 IOP Publishing
Journal of Physics: Conference Series 459 (2013) 012017 doi:10.1088/1742-6596/459/1/012017
4
6. 3.3 Relationship between sEMG and LDMi signals
A second test was carried out aiming to analyze the correlation between surface EMG and LDMi
amplitudes. The subject contracted the flexor carpi ulnaris muscle with two different force levels
(minimum and maximum). For this test the subjects were 3 and each subject repeated the test 4 times
for the maximum level of force and 4 times for the minimum level of force. The parameter that was
calculated is S/N (according eq.1) and the LDMi signal was filtered with a Wavelet (sym4).
Figure 6. Scatter plot of sEMG and LDMi S/Ns for isometric
contraction of the right and left flexor carpi ulnaris muscle
(Pearson’s coefficient: 0.88).
In correspondence to a force increase, it’s possible to observe an increase of the S/N sEMG signal
amplitude as well as of the LDMi signal which allows to cluster the data in terms of force level.
3.4 Muscle fatigue
The RMS value of the time signals and the slope of the interpolating line are known parameters for the
evaluation of the muscle fatigue. The RMS value, according eq.1, is calculated during the muscle
contraction for sEMG and LDMi signals. For this test, acquisitions stands for 60s. In fig. 8 and 9, an
example of time series of the RMS of the acquired signals are reported; it is possible to observe a
negative slope on both the interpolating lines testifying the effect of the muscle fatigue.
Figure 8. RMS of EMG signal activation. Figure 9. RMS of LDVi signal activation.
IMEKO 2013 TC1 + TC7 + TC13 IOP Publishing
Journal of Physics: Conference Series 459 (2013) 012017 doi:10.1088/1742-6596/459/1/012017
5
7. 4. Discussion
The aim of this research is to present a non-contact measurement procedure for the determination of
the muscle characteristics related to the muscle contraction. Comparative tests with sEMG and the
proposed method have been carried out on a population of 20 subjects on the flexor carpi ulnaris and
tibialis anterior (left and right) muscles. The RMS value of the acquired signals and the Signal to
Noise ratio (S/N) have been calculated and results show that the detection timing is sufficiently
accurate (maximum standard deviation: 440 ms). A good correlation between the S/N of sEMG
signals and the S/N of LDMi signals is also reported (mean Pearson coefficient: 0.93). This result is
also shown for the test at minimum and maximum level of force. Finally, it was also possible to
evaluate the muscle fatigue.
Such results highlight the possibility to measure such parameters, related to the muscle activity,
using the proposed method without contact, obtaining results which are in accord with the ones
measured with the gold standard (sEMG).
References
[1] J.G Webster (ed.), Medical instrumentation: application and design. 3rd
ed. New York: John
Wiley & Sons, 1998.
[2] Medved, Cifrek (2011). Kinesiological Electromyography, Biomechanics in Applications,
Vaclav Klika (Ed.), InTech, Available from: http://www.intechopen.com/books/biomechanics-
in-applications/kinesiological-electromyography
[3] J W Rohrbaugh, E J Sirevaag, E J Richter, Laser Doppler Vibrometry measurement of the
mechanical myogram. 10th
Int. Conference on Vibration Measurements by Laser and
Noncontact Techniques. AIP Conf. Proceedings, Vol. 1457, pp. 266-274 (2012).
[4] M. De Melis, U. Morbiducci , L. Scalise, E.P. Tomasini, D. Delbeke, R. Baets, L.M. Van
Bortel, P. Segers, A non contact approach for the evaluation of large artery stiffness: a
preliminary study. American Journal of Hypertension, Vol. 21, pp. 1280-1283 (2008).
[5] L. Scalise, U. Morbiducci, Non contact cardiac monitoring from carotid artery using optical
vibrocardiography. Medical Engineering & Physics, Vol. 30(4), pp. 490-497 (2008).
[6] L. Scalise, P. Marchionni, I. Ercoli. A non-contact optical procedure for precise measurement of
respiration rate and flow, Proc SPIE 7715, 77150G (2010).
[7] L. Scalise, F. Rossetti, N. Paone, Hand vibration: non-contact measurement of local
transmissibility. Int Arch Occupational and Environmental Health. Vol. 81(1), pp. 31-40 (2007).
[8] A. Georgakis, L.K. Stergioulas, G. Giakas. Fatigue analysis of the surface EMG signal in
isometric constant force contractions using the averaged instantaneous frequency. IEEE
Transactions on biomedical engineering, 50, 2, 2003.
IMEKO 2013 TC1 + TC7 + TC13 IOP Publishing
Journal of Physics: Conference Series 459 (2013) 012017 doi:10.1088/1742-6596/459/1/012017
6