scholarly journals The Analysis of Surface EMG Signals with the Wavelet-Based Correlation Dimension Method

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Gang Wang ◽  
Yanyan Zhang ◽  
Jue Wang
Keyword(s):  
Frequency Domain ◽  
Classification Accuracy ◽  
Correlation Dimension ◽  
Surface Emg ◽  
Nonlinear Characteristics ◽  
Time Frequency ◽  
Frequency Domain Methods ◽  
Resolution Level ◽  
Correlation Dimension Method ◽  
Semg Signals

Many attempts have been made to effectively improve a prosthetic system controlled by the classification of surface electromyographic (SEMG) signals. Recently, the development of methodologies to extract the effective features still remains a primary challenge. Previous studies have demonstrated that the SEMG signals have nonlinear characteristics. In this study, by combining the nonlinear time series analysis and the time-frequency domain methods, we proposed the wavelet-based correlation dimension method to extract the effective features of SEMG signals. The SEMG signals were firstly analyzed by the wavelet transform and the correlation dimension was calculated to obtain the features of the SEMG signals. Then, these features were used as the input vectors of a Gustafson-Kessel clustering classifier to discriminate four types of forearm movements. Our results showed that there are four separate clusters corresponding to different forearm movements at the third resolution level and the resulting classification accuracy was 100%, when two channels of SEMG signals were used. This indicates that the proposed approach can provide important insight into the nonlinear characteristics and the time-frequency domain features of SEMG signals and is suitable for classifying different types of forearm movements. By comparing with other existing methods, the proposed method exhibited more robustness and higher classification accuracy.

scholarly journals Classification of Emotional States Inparkinson’s Disease Patients Using Time,Frequency and Time-Frequency Analysis

2021 ◽  
Author(s):  
Rejith K.N ◽  
Kamalraj Subramaniam ◽  
Ayyem Pillai Vasudevan Pillai ◽  
Roshini T V ◽  
Renjith V. Ravi ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Classification Accuracy ◽  
Machine Learning Algorithms ◽  
Signal Frequency ◽  
Spectral Energy ◽  
Support Vector ◽  
Svm Classifier ◽  
Time Frequency ◽  
Energy Entropy ◽  
Teager Energy

Abstract In this work, PD patients and healthy individuals were categorized with machine-learning algorithms. EEG signals associated with six different emotions, (Happiness(E1), Sadness(E2), Fear(E3), Anger(E4), Surprise,(E5) and disgust(E6)) were used for the study. EEG data were collected from 20 PD patients and 20 normal controls using multimodal stimuli. Different features were used to categorize emotional data. Emotional recognition in Parkinson’s disease (PD) has been investigated in three domains namely, time, frequency and time frequency using Entropy, Energy-Entropy and Teager Energy-Entropy features. Three classifiers namely, K-Nearest Neighbor Algorithm, Support Vector Machine and Probabilistic Neural Network were used to observethe classification results. Emotional EEG stimuli such as anger, surprise, happiness, sadness, fear, and disgust were used to categorize PD patients and healthy controls (HC). For each EEG signal, frequency features corresponding to alpha, beta and gamma bands were obtained for nine feature extraction methods (Entropy, Energy Entropy, Teager Energy Entropy, Spectral Entropy, Spectral Energy-Entropy, Spectral Teager Energy-Entropy, STFT Entropy, STFT Energy-Entropy and STFT Teager Energy-Entropy). From the analysis, it is observed that the entropy feature in frequency domain performs evenly well (above 80 %) for all six emotions with KNN. Classification results shows that using the selected energy entropy combination feature in frequency domain provides highest accuracy for all emotions except E1 and E2 for KNN and SVM classifier, whereas other features give accuracy values of above 60% for most emotions.It is also observed that emotion E1 gives above 90 % classification accuracy for all classifiers in time domain.In frequency domain also, emotion E1 gives above 90% classification accuracy using PNN classifier.

scholarly journals Fusion Learning for sEMG Recognition of Multiple Upper-Limb Rehabilitation Movements

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5385
Author(s):  
Tianyang Zhong ◽  
Donglin Li ◽  
Jianhui Wang ◽  
Jiacan Xu ◽  
Zida An ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Upper Limb ◽  
Classification Accuracy ◽  
Human Motion ◽  
Identification Accuracy ◽  
Classification Model ◽  
Upper Limb Rehabilitation ◽  
Time Frequency ◽  
Limb Rehabilitation ◽  
Semg Signals

Surface electromyogram (sEMG) signals have been used in human motion intention recognition, which has significant application prospects in the fields of rehabilitation medicine and cognitive science. However, some valuable dynamic information on upper-limb motions is lost in the process of feature extraction for sEMG signals, and there exists the fact that only a small variety of rehabilitation movements can be distinguished, and the classification accuracy is easily affected. To solve these dilemmas, first, a multiscale time–frequency information fusion representation method (MTFIFR) is proposed to obtain the time–frequency features of multichannel sEMG signals. Then, this paper designs the multiple feature fusion network (MFFN), which aims at strengthening the ability of feature extraction. Finally, a deep belief network (DBN) was introduced as the classification model of the MFFN to boost the generalization performance for more types of upper-limb movements. In the experiments, 12 kinds of upper-limb rehabilitation actions were recognized utilizing four sEMG sensors. The maximum identification accuracy was 86.10% and the average classification accuracy of the proposed MFFN was 73.49%, indicating that the time–frequency representation approach combined with the MFFN is superior to the traditional machine learning and convolutional neural network.

ANALYSIS OF SURFACE EMG SIGNALS UNDER FATIGUE AND NON-FATIGUE CONDITIONS USING B-DISTRIBUTION BASED QUADRATIC TIME FREQUENCY DISTRIBUTION

2015 ◽  
Vol 15 (02) ◽  
pp. 1540028 ◽  
Author(s):  
P. A. KARTHICK ◽  
G. VENUGOPAL ◽  
S. RAMAKRISHNAN
Keyword(s):  
Frequency Distribution ◽  
Biceps Brachii ◽  
Surface Emg ◽  
Median Frequency ◽  
Biceps Brachii Muscle ◽  
Spectral Entropy ◽  
Time Frequency ◽  
Neuromuscular Conditions ◽  
Time Frequency Distribution ◽  
Semg Signals

In this paper, an attempt has been made to analyze surface electromyography (sEMG) signals under non-fatigue and fatigue conditions using time-frequency based features. The sEMG signals are recorded from biceps brachii muscle of 50 healthy volunteers under well-defined protocol. The pre-processed signals are divided into six equal epochs. The first and last segments are considered as non-fatigue and fatigue zones respectively. Further, these signals are subjected to B-distribution based quadratic time-frequency distribution (TFD). Time frequency based features such as instantaneous median frequency (IMDF) and instantaneous mean frequency (IMNF) are extracted. The expression of spectral entropy is modified to obtain instantaneous spectral entropy (ISPEn) from the time-frequency spectrum. The results show that all the extracted features are distinct in both conditions. It is also observed that the values of all features are higher in non-fatigue zone compared to fatigue condition. It appears that this method is useful in analysing various neuromuscular conditions using sEMG signals.

scholarly journals Evaluation of Feature Extraction and Classification for Lower Limb Motion Based on sEMG Signal

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 852 ◽  
Author(s):  
Pengjie Qin ◽  
Xin Shi
Keyword(s):  
Feature Extraction ◽  
Frequency Domain ◽  
Classification Accuracy ◽  
Evaluation Method ◽  
Lower Limbs ◽  
Thigh Muscle ◽  
Gaussian Kernel ◽  
Classification Methods ◽  
Linear Discriminant ◽  
Time Frequency

The real-time and accuracy of motion classification plays an essential role for the elderly or frail people in daily activities. This study aims to determine the optimal feature extraction and classification method for the activities of daily living (ADL). In the experiment, we collected surface electromyography (sEMG) signals from thigh semitendinosus, lateral thigh muscle, and calf gastrocnemius of the lower limbs to classify horizontal walking, crossing obstacles, standing up, going down the stairs, and going up the stairs. Firstly, we analyzed 11 feature extraction methods, including time domain, frequency domain, time-frequency domain, and entropy. Additionally, a feature evaluation method was proposed, and the separability of 11 feature extraction algorithms was calculated. Then, combined with 11 feature algorithms, the classification accuracy and time of 55 classification methods were calculated. The results showed that the Gaussian Kernel Linear Discriminant Analysis (GK-LDA) with WAMP had the highest classification accuracy rate (96%), and the calculation time was below 80 ms. In this paper, the quantitative comparative analysis of feature extraction and classification methods was a benefit to the application for the wearable sEMG sensor system in ADL.

scholarly journals Blind separation of complex-valued satellite-AIS data for marine surveillance: a spatial quadratic time-frequency domain approach

2019 ◽  
Vol 9 (3) ◽  
pp. 1732
Author(s):  
Omar Cherrak ◽  
Hicham Ghennioui ◽  
Nadege Thirion Moreau ◽  
El Hossein Abarkan
Keyword(s):  
Frequency Domain ◽  
Block Diagonalization ◽  
Blind Separation ◽  
Time Frequency ◽  
High Magnitude ◽  
Frequency Domain Methods ◽  
Stationary Signals ◽  
Mixing Matrix ◽  
Frequency Domain Approach ◽  
Complex Valued

<p>In this paper, the problem of the blind separation of complex-valued Satellite-AIS data for marine surveillance is addressed. Due to the specific properties of the sources under consideration: they are cyclo-stationary signals with two close cyclic frequencies, we opt for spatial quadratic time-frequency domain methods. The use of an additional diversity, the time delay, is aimed at making it possible to undo the mixing of signals at the multi-sensor receiver. The suggested method involves three main stages. First, the spatial generalized mean Ambiguity function of the observations across the array is constructed. Second, in the Ambiguity plane, Delay-Doppler regions of high magnitude are determined and Delay-Doppler points of peaky values are selected. Third, the mixing matrix is estimated from these Delay-Doppler regions using our proposed non-unitary joint zero-(block) diagonalization algorithms as to perform separation.</p>

The Use of Surface Electromyography in Muscle Fatigue Assessments–A Review

2015 ◽  
Vol 74 (6) ◽  
Author(s):  
Nurul Asyikin Kamaruddin ◽  
Puspa Inayat Khalid ◽  
Ahmad Zuri Shaameri
Keyword(s):  
Muscle Fatigue ◽  
Frequency Domain ◽  
Surface Electromyography ◽  
Sports Injuries ◽  
Surface Emg ◽  
Time Frequency ◽  
Emg Signal ◽  
Assessment Techniques ◽  
Fatigue Estimation ◽  
Interaction Surface

The developments in physiological studies have established the importance of muscle fatigue estimation in various aspects including neurophysiological and medical research, rehabilitation, ergonomics, sports injuries and human-computer interaction. Surface electromyography signals are commonly used in muscle fatigue assessment. Techniques of surface EMG signal processing used to quantify muscle fatigue are not only based on time domain and frequency domain, but also on time–frequency domain. The developments of different signal analysis to extract different indices for muscle fatigue assessments are reviewed in this paper. Several indices in time, frequency, and time-frequency representations for muscle fatigue assessments have been identified. However the sensitivity of those indices needs to be investigated. Minimizing this issue becomes the objective of the recent research in muscle fatigue assessments.

Assessment of Muscles Fatigue during 400-Meters Running Strategies Based on the Surface EMG Signals

2019 ◽  
Vol 42 ◽  
pp. 1-13
Author(s):  
Hayder A. Yousif ◽  
Abdul Rahim Norasmadi ◽  
Ahmad Faizal Bin Salleh ◽  
Zakaria Ammar ◽  
Khudhur A. Alfarhan
Keyword(s):  
Frequency Domain ◽  
Research Work ◽  
Gluteus Maximus ◽  
Rectus Femoris ◽  
Biceps Femoris ◽  
Surface Emg ◽  
Median Frequency ◽  
Bipolar Electrodes ◽  
Time Frequency ◽  
Full Speed

The aim of this research work is to assess the muscles fatigue of the male runner during 400 meters (m) running with three types of running strategies. The Electromyography (EMG) signals from the Rectus Femoris (RF), Biceps Femoris (BF), Gluteus Maximus (GM), Gastrocnemius Lateralis (GL), and Gastrocnemius Medialis (GMS) were collected by using bipolar electrodes from the right lower extremity’s muscles. EMG signals were collected during the run on the tartan athletic track. Five subjects (non-athletes) had run 400m with three various types of running strategies. The first type: the first 200m running 85-93% of full speed and the last 200m sprinting (full speed), second type: the first 300m running 85-93% of sprinting and the last 100m sprinting, and third type: running 85-93% of sprinting for 400m. The EMG signals were transformed to the time-frequency domain using Short Time Fourier Transform to calculate the instantaneous mean frequency (IMNF) and instantaneous median frequency (IMDF). The less index fatigues were during 1st strategy, while the RF, BF, GM, and GL muscles got recovered with IMNF and IMDF with the three strategies, and the GMS muscle has less negative regression slope value with IMNF with 1st strategy during the 4th 100m of the 400m running event. From the results, it can be concluded the running with the 1st strategy get less fatigues compared with the 2nd and 3rd strategy based on the results of time-frequency domain features (IMNF and IMDF).

scholarly journals Power quality disturbance classification based on time-frequency domain multi-feature and decision tree

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Wenjing Zhao ◽  
Liqun Shang ◽  
Jinfan Sun
Keyword(s):  
Wavelet Transform ◽  
Decision Tree ◽  
Frequency Domain ◽  
Power Quality ◽  
Classification Accuracy ◽  
Support Vector ◽  
Classification Rules ◽  
Time Frequency ◽  
S Transform ◽  
Power Quality Disturbance

AbstractAccurate classification of power quality disturbance is the premise and basis for improving and governing power quality. A method for power quality disturbance classification based on time-frequency domain multi-feature and decision tree is presented. Wavelet transform and S-transform are used to extract the feature quantity of each power quality disturbance signal, and a decision tree with classification rules is then constructed for classification and recognition based on the extracted feature quantity. The classification rules and decision tree classifier are established by combining the energy spectrum feature quantity extracted by wavelet transform and other seven time-frequency domain feature quantities extracted by S-transform. Simulation results show that the proposed method can effectively identify six types of common single disturbance signals and two mixed disturbance signals, with fast classification speed and adequate noise resistance. Its classification accuracy is also higher than those of support vector machine (SVM) and k-nearest neighbor (KNN) algorithms. Compared with the method that only uses S-transform, the proposed feature extraction method has more abundant features and higher classification accuracy for power quality disturbance.

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