Classification of Surface EMGs Using Wavelet Packet Energy Analysis and a Genetic Algorithm-Based Support Vector Machine

Ye, Rong; Hao, Dongmei; Han, Xiao; Zhang, Y.; Zhang, J.; Zeng, Yanjun

doi:10.1007/s11062-013-9335-z

Cited by 20 publications

(15 citation statements)

References 23 publications

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“…GA-SVM has been applied in the classification of the muscle states (maximum voluntary contraction, fatigue degree) based on MMG signal. GA-SVM achieved a higher classification accuracy than the back-propagation neural networks (BP-NN) and SVM [20], the flowchart of GA-SVM could be seen in [21].…”

Section: Methodsmentioning

confidence: 99%

Research on GA-SVM Based Head-Motion Classification via Mechanomyography Feature Analysis

Zhang

Xia

et al. 2019

Sensors

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This study investigated classification of six types of head motions using mechanomyography (MMG) signals. An unequal segmenting algorithm was adopted to segment the MMG signals generated by head motions. Three types of features (time domain, time-frequency domain and nonlinear dynamics) were extracted to construct five feature sets as candidate datasets for classification analysis. Genetic algorithm optimized support vector machine (GA-SVM) was used to classify the MMG signals. Three different kernel functions, different combinations of feature sets, different number of signal channels and training samples were selected for comparative analysis to evaluate the classification accuracy. Experimental results showed that the classifier had the best overall classification accuracy when using the radial basis function (RBF). Any combination of three different types of feature sets guaranteed an average accuracy of over 80%. In the case of the best combination (feature set 2 + 3 + 5), the classification accuracy was up to 88.2%. Using four channels to acquire MMG signal and no less than 60 training samples can assure a satisfactory classification accuracy.

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Section: Methodsmentioning

confidence: 99%

Research on GA-SVM Based Head-Motion Classification via Mechanomyography Feature Analysis

Zhang

Xia

et al. 2019

Sensors

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“…Wavelet Packet Decomposition (WPD) is a more sophisticated analysis method that can more accurately reflect signal characteristics. The twelve time-frequency features we use include the maximum values of WT and WPD, singular values, average energy, variance, standard deviation, and WL [34][35][36][37][38][39][40][41]. In this paper, the wavelet-based sym3 is used to perform level = 3 wavelet decomposition on the EMG signal, and the third-order Symlet wavelet packet base is used to perform WPD on the EMG signal.…”

Section: Time-frequency Featuresmentioning

confidence: 99%

Exploration of Feature Extraction Methods and Dimension for sEMG Signal Classification

Wang

et al. 2019

Applied Sciences

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It is necessary to complete the two parts of gesture recognition and wireless remote control to realize the gesture control of the automatic pruning machine. To realize gesture recognition, in this paper, we have carried out the research of gesture recognition technology based on surface electromyography signal, and discussed the influence of different numbers and different gesture combinations on the optimal size. We have calculated the 630-dimensional eigenvector from the benchmark scientific database of sEMG signals and extracted the features using principal component analysis (PCA). Discriminant analysis (DA) has been used to compare the processing effects of each feature extraction method. The experimental results have shown that the recognition rate of four gestures can reach 100.0%, the recognition rate of six gestures can reach 98.29%, and the optimal size is 516~523 dimensions. This study lays a foundation for the follow-up work of the pruning machine gesture control, and p rovides a compelling new way to promote the creative and human computer interaction process of forestry machinery.

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“…Fourier change is for the most part utilized for signal processing since 1950's, however latest change called wavelet transform in [24] brings another stage towards denoising, compression, and characterization. Wavelet change's fundamental objective is to personify a signal which might be dissected as superposition of wavelet.…”

Section: Feature Extraction Using Wavelet Packet Transformmentioning

confidence: 99%

Design and Application of a Smart Diagnostic System for Parkinson’s Patients using Machine Learning

Channa¹,

Baqai²,

Ceylan³

2019

IJACSA

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For analysis of Parkinson illness gait disabilities detection is essential. The only motivation behind this examination is to equitably and consequently differentiate among sound subjects and the one who is forbearing the Parkinson, utilizing IOT based indicative framework. In this examination absolute, 16 distinctive force sensors being attached with the shoes of subjects which documented the Multisignal Vertical Ground Reaction Force (VGRF). Overall sensors signals utilizing 1024 window estimate around the raw signals, utilizing the Packet wavelet change (PWT) five diverse characteristics that includes entropy, energy, variance, standard deviation and waveform length were derived and support vector machine (SVM) is to recognize Parkinson patients and healthy subjects. SVM is trained on 85% of the dataset and tested on 15% dataset. Preparation accomplice relies upon 93 patients with idiopathic PD (mean age: 66.3 years; 63% men and 37% ladies), and 73 healthy controls (mean age: 66.3 years; 55% men and 45% ladies). IOT framework included all 16 sensors, from which 8 compel sensors were appended to left side foot of subject and the rest of the 8 on the right side foot. The outcomes demonstrate that fifth sensor worn on a Medial part of the dorsum of right foot highlighted by R5 gives 90.3% accuracy. Henceforth this examination gives the knowledge to utilize single wearable force sensor. Hence, this examination deduce that a solitary sensor might help in differentiation amongst Parkinson and healthy subjects.

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Classification of Surface EMGs Using Wavelet Packet Energy Analysis and a Genetic Algorithm-Based Support Vector Machine

Cited by 20 publications

References 23 publications

Research on GA-SVM Based Head-Motion Classification via Mechanomyography Feature Analysis

Research on GA-SVM Based Head-Motion Classification via Mechanomyography Feature Analysis

Exploration of Feature Extraction Methods and Dimension for sEMG Signal Classification

Design and Application of a Smart Diagnostic System for Parkinson’s Patients using Machine Learning

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