Extreme learning machine classification method for lower limb movement recognition

Kuang, Yuxiang; Wu, Qun; Shao, Junkai; Wu, Jianfeng; Wu, Xue-Hua

doi:10.1007/s10586-017-0985-2

Cited by 26 publications

(3 citation statements)

References 23 publications

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“…The classification of hand signs based on data collected from accelerometers, and sEMG were used to identify sign language for hearing-impaired and non-verbal community [6]. Kuang et al [7] proposed a recognition method based on extreme learning machine (ELM) to recognize the patterns of seven lower limb movements, with the overall recognition accuracy above 95%.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

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

Zhang

Xia

et al. 2019

Sensors

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“…ELM has a mathematical model that differs from backpropagation with a simpler and more effective model [27]. ELM neural network model with neuron inputs, hidden layer neurons, and activation function…”

Section: Extreme Learning Machine (Elm)mentioning

confidence: 99%

Image X-Ray Classification for COVID-19 Detection Using GCLM-ELM

2021

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COVID-19 is a disease or virus that has recently spread worldwide. The disease has also taken many casualties because the virus is notoriously deadly. An examination can be carried out using a chest X-Ray because it costs cheaper compared to swab and PCR tests. The data used in this study was chest X-Ray image data. Chest X-Ray images can be identified using Computer-Aided Diagnosis by utilizing machine learning classification. The first step was the preprocessing stage and feature extraction using the Gray Level Co-Occurrence Matrix (GLCM). The result of the feature extraction was then used at the classification stage. The classification process used was Extreme Learning Machine (ELM). Extreme Learning Machine (ELM) is one of the artificial neural networks with advanced feedforward which has one hidden layer called Single Hidden Layer Feedforward Neural Networks (SLFNs). The results obtained by GLCM feature extraction and classification using ELM achieved the best accuracy of 91.21%, the sensitivity of 100%, and the specificity of 91% at 135° rotation using linear activation function with 15 hidden nodes.

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“…The current research ( Han et al, 2015 ; Li et al, 2017 ) on the continuous movement estimation of limbs mainly focuses on using various surface electromyography (sEMG) features to estimate the joint angle, and there are two ways to achieve it. The first is to establish an articulation dynamics model with muscle physiology involved, which takes sEMG as the input and then calculates joint torque; the second is to set the regression relationship of sEMG and the articulation movement angle ( Gijsberts et al, 2014 ; Hahne et al, 2014 ; Kuang et al, 2017 ; Long et al, 2017 ; Wang et al, 2017 ; Zhang et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Continuous Pronation–Supination Movement by Using Multichannel EMG Signal Features and Kalman Filter: Application to Control an Exoskeleton

Zhang

Long

Zhao

et al. 2022

Front. Bioeng. Biotechnol.

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The Hill muscle model can be used to estimate the human joint angles during continuous movement. However, adopting this model requires the knowledge of many parameters, such as the length and speed of contraction of muscle fibers, which are liable to change with different individuals, leading to errors in estimation. This study established the backpropagation neural network model based on surface electromyography (sEMG) features and human movement angle. First, the function of muscles in joint rotation is defined, and then, sensors are placed on muscle tissues to gain sEMG, and then, a relation model between the surface sEMG features and the joint angle is constructed. As integrated electromyography information cannot be well reflected through a single electromyography feature, a feature extraction method combining the time domain, frequency domain, and time–frequency domain was proposed. As the degree of freedom (DOF) of the pronation–supination movement was controlled by several muscles, it was difficult to make an angle prediction. A method of correcting the estimation error based on the Kalman filter was raised to cope with this problem. An exoskeleton robot with one DOF was designed and put into the tracking experiment. The results show that the proposed model was able to enhance the estimation of the joint angle during continuous pronation–supination movements.

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Extreme learning machine classification method for lower limb movement recognition

Cited by 26 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

Image X-Ray Classification for COVID-19 Detection Using GCLM-ELM

Estimation of the Continuous Pronation–Supination Movement by Using Multichannel EMG Signal Features and Kalman Filter: Application to Control an Exoskeleton

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