Exercise muscle fatigue detection system implementation via wireless surface electromyography and empirical mode decomposition

2013 ◽  
Author(s):  
Kang-Ming Chang ◽  
Shing-Hong Liu ◽  
Jia-Jung Wang ◽  
Da-Chuan Cheng
Keyword(s):  
Muscle Fatigue ◽  
Empirical Mode Decomposition ◽  
Surface Electromyography ◽  
Detection System ◽  
System Implementation ◽  
Mode Decomposition ◽  
Exercise Muscle ◽  
Fatigue Detection

FATIGUE CONTRACTION ANALYSIS USING EMPIRICAL MODE DECOMPOSITION AND WAVELET TRANSFORM

2015 ◽  
Vol 77 (6) ◽  
Author(s):  
Rubana Haque Chowdhury ◽  
Mamun Bin Ibne Reaz
Keyword(s):  
Wavelet Transform ◽  
Muscle Fatigue ◽  
Empirical Mode Decomposition ◽  
Surface Emg ◽  
Semg Signal ◽  
Emg Signal ◽  
Mode Decomposition ◽  
Spectral Variable ◽  
Walking Exercise ◽  
The Difference

Muscle fatigue is a long lasting reduction of the ability to contract and it is the condition when produced force is reduced. Walking fast can cause muscle fatigue, which is unhealthy and it is incurable when the level of fatigue is high. Muscle fatigue during walk can be determined using several spectral variables. The amplitude and frequency of the surface EMG signal provide a more accurate reflection of motor unit pattern among these spectral variables. This research reports on the effectiveness of Empirical mode decomposition (EMD) and wavelet transform based filtering method applied to the surface EMG (sEMG) signal as a means of achieving reliable discrimination of the muscle fatigue during human walking exercise. In this research, IAV, RMS and AIF values were used as spectral variable. These spectral variables extensively identifies the difference between fatigue and normal muscle when using EMD method compared with other different wavelet functions (WFs). The result shows that the sEMG amplitude and frequency momentously changes from rest position to maximum contraction position.

Anomaly Detection in Cloud Environments

2019 ◽  
pp. 140-164
Author(s):  
Angelos K. Marnerides
Keyword(s):  
Intrusion Detection ◽  
Anomaly Detection ◽  
Empirical Mode Decomposition ◽  
State Of The Art ◽  
Detection System ◽  
Security Threats ◽  
Accurate Identification ◽  
Mode Decomposition ◽  
Operational Characteristics ◽  
Cloud Environments

Cloud environments compose unique operational characteristics and intrinsic capabilities such as service transparency and elasticity. By virtue of their exclusive properties as being outcomes of their virtualized nature, these environments are prone to a number of security threats either from malicious or legitimate intent. By virtue of the minimal proactive properties attained by off-the-shelf signature-based commercial detection solutions employed in various infrastructures, cloud-specific Intrusion Detection System (IDS) Anomaly Detection (AD)-based methodologies have been proposed in order to enable accurate identification, detection, and clustering of anomalous events that could manifest. Therefore, in this chapter the authors firstly aim to provide an overview in the state of the art related with cloud-based AD mechanisms and pinpoint their basic functionalities. They subsequently provide an insight and report some results derived by a particular methodology that jointly considers cloud-specific properties and relies on the Empirical Mode Decomposition (EMD) algorithm.

scholarly journals BEMD-3DCNN-Based Method for COVID-19 Detection

2021 ◽  
Author(s):  
Ali Riahi ◽  
Omar Elharrouss ◽  
Noor Almaadeed ◽  
Somaya Al-Maadeed
Keyword(s):  
Empirical Mode Decomposition ◽  
Detection System ◽  
Virus Detection ◽  
Research Projects ◽  
X Ray ◽  
Mode Decomposition ◽  
The World ◽  
A Priority ◽  
The Right

Abstract Coronavirus outbreak continues to spread around the world and none knows when it will stop. Therefore, from the first day of the virus detection in Wuhan, scientists have launched numerous research projects to understand the nature of the virus, how to detect it, and search for the right medicine to help and protect patients. A fast diagnostic and detection system is a priority and should be found to stop COVID-19 from expanding. The purpose of the study is to combine the bi-dimensional empirical mode decomposition (BEMD) technique with 3DCNN to detect COVID-19. BEMD is used to decompose the original images into IMFs and from there built a video then apply the 3DCNN to classify and detect COVID-19 virus. In our experiment we used 6484 X-Ray images, 1802 of them were COVID-19 positive cases, 1910 normal cases, and 2772 pneumonia cases. The experiment results showed that our proposed techniques achieved the desired results on the selected dataset. It reached the accuracy of 100%.

Automated detection of circinate exudates in retina digital images using empirical mode decomposition and the entropy and uniformity of the intrinsic mode functions

2014 ◽  
Vol 59 (4) ◽  
Author(s):  
Salim Lahmiri ◽  
Mounir Boukadoum
Keyword(s):  
Empirical Mode Decomposition ◽  
Digital Images ◽  
Color Image ◽  
Detection System ◽  
Histogram Equalization ◽  
Automated System ◽  
Support Vector ◽  
Clinical Environment ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition

AbstractThis work presents a new automated system to detect circinate exudates in retina digital images. It operates as follows: the true color image is converted to gray levels, and contrast-limited adaptive histogram equalization (CLAHE) is applied to it before undergoing empirical mode decomposition (EMD) as intrinsic mode functions (IMFs). The entropies and uniformities of the first two IMFs are then computed to form a feature vector that is fed to a support vector machine (SVM) for classification. The experimental results using a set of 45 images (23 normal images and 22 images with circinate exudates taken from the STARE database) and tenfold cross-validation indicate that the proposed approach outperforms previous works found in the literature, with perfect classification. In addition, the image processing time was <4 min, making the presented circinate exudate detection system fit for use in a clinical environment.

Anomaly Detection in Cloud Environments

2015 ◽  
pp. 43-67
Author(s):  
Angelos K. Marnerides
Keyword(s):  
Intrusion Detection ◽  
Anomaly Detection ◽  
Empirical Mode Decomposition ◽  
State Of The Art ◽  
Detection System ◽  
Security Threats ◽  
Accurate Identification ◽  
Mode Decomposition ◽  
Operational Characteristics ◽  
Cloud Environments

Cloud environments compose unique operational characteristics and intrinsic capabilities such as service transparency and elasticity. By virtue of their exclusive properties as being outcomes of their virtualized nature, these environments are prone to a number of security threats either from malicious or legitimate intent. By virtue of the minimal proactive properties attained by off-the-shelf signature-based commercial detection solutions employed in various infrastructures, cloud-specific Intrusion Detection System (IDS) Anomaly Detection (AD)-based methodologies have been proposed in order to enable accurate identification, detection, and clustering of anomalous events that could manifest. Therefore, in this chapter the authors firstly aim to provide an overview in the state of the art related with cloud-based AD mechanisms and pinpoint their basic functionalities. They subsequently provide an insight and report some results derived by a particular methodology that jointly considers cloud-specific properties and relies on the Empirical Mode Decomposition (EMD) algorithm.

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