A Comprehensive Evaluation of Support Vector Machine in Hand Movement Classification Using Surface Electromyography

2017 ◽  
Vol 9 (5) ◽  
pp. 741-753 ◽  
Author(s):  
Wentao Wei ◽  
Yu Hu ◽  
Yu Du
Keyword(s):  
Support Vector Machine ◽  
Surface Electromyography ◽  
Comprehensive Evaluation ◽  
Hand Movement ◽  
Support Vector ◽  
Movement Classification

Comprehensive Evaluation on Power Quality of Tidal Current Convertors

2019 ◽  
Vol 53 (3) ◽  
pp. 46-53
Author(s):  
Caixia Xue ◽  
Xiang-nan Wang ◽  
Ning Jia ◽  
Yuan-fei Zhang ◽  
Hai-nan Xia
Keyword(s):  
Power Generation ◽  
Support Vector Machine ◽  
Power Quality ◽  
Tidal Current ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Support Vector ◽  
Operational Data ◽  
Testing And Evaluation

AbstractWith the continuous development of testing and evaluation of tidal current convertors, power quality assessment is becoming more and more critical. According to the characteristics of Chinese tidal current power generation and power quality standards, this paper proposes a comprehensive evaluation method of power quality based on K-means clustering and a support vector machine. The fundamental purpose of the method is to automatically select the weights of various indicators in the comprehensive assessment of power quality, by which the influence of subjective factors can be eliminated. In order to achieve the above purpose, K-means clustering is used for automatically classifying the operational data into five different categories. Then, a support vector machine is used to study and estimate the relationship of the operational data and categories. Using the method proposed in the paper, the analysis of operational data of a tidal current power generation shows that calculation results can objectively reflect the power quality of the device, and the influence of subjective factors is eliminated. The method can provide a reference for the testing and evaluation of a large amount of tidal current convertors in the future.

Improved Support Vector Machine and its Application

2010 ◽  
Vol 20-23 ◽  
pp. 147-153 ◽  
Author(s):  
Zhi Wei Huang ◽  
Jian Zhong Zhou ◽  
Li Xiang Song ◽  
Yong Chuan Zhang
Keyword(s):  
Support Vector Machine ◽  
Comprehensive Evaluation ◽  
Flood Hazard ◽  
Hazard Evaluation ◽  
Support Vector ◽  
Continuous Space ◽  
Svm Model ◽  
Improve Accuracy ◽  
Disaster Situation ◽  
Improved Support Vector Machine

According to the complex and uncertain relationships between indexes and grades of flood hazard evaluation, as well as the deficiency of measured samples, an improved support vector machine (SVM) model was established to improve accuracy and efficiency of calculation. The function that comprehensively evaluated indexes of multi-dimensional disaster situation in one-dimensional continuous space could be realized, and effectively solved the incompatible problems of different evaluation results with single index. The results showed that the model based on improved support vector machine had a better ability of generalization and calculation speed by reduce constraint conditions. It is considered to have a good application prospect in multi-index comprehensive evaluation.

scholarly journals Support Vector Machine-Based Classifier for the Assessment of Finger Movement of Stroke Patients Undergoing Rehabilitation

2019 ◽  
Vol 40 (1) ◽  
pp. 91-100 ◽  
Author(s):  
Toyohiro Hamaguchi ◽  
Takeshi Saito ◽  
Makoto Suzuki ◽  
Toshiyuki Ishioka ◽  
Yamato Tomisawa ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Peak Velocity ◽  
Hand Movement ◽  
Classification Model ◽  
Support Vector ◽  
Leap Motion ◽  
Stroke Patients ◽  
Related Data ◽  
Testing Dataset ◽  
Linear Svm

Abstract Purpose Traditionally, clinical evaluation of motor paralysis following stroke has been of value to physicians and therapists because it allows for immediate pathophysiological assessment without the need for specialized tools. However, current clinical methods do not provide objective quantification of movement; therefore, they are of limited use to physicians and therapists when assessing responses to rehabilitation. The present study aimed to create a support vector machine (SVM)-based classifier to analyze and validate finger kinematics using the leap motion controller. Results were compared with those of 24 stroke patients assessed by therapists. Methods A non-linear SVM was used to classify data according to the Brunnstrom recovery stages of finger movements by focusing on peak angle and peak velocity patterns during finger flexion and extension. One thousand bootstrap data values were generated by randomly drawing a series of sample data from the actual normalized kinematics-related data. Bootstrap data values were randomly classified into training (940) and testing (60) datasets. After establishing an SVM classification model by training with the normalized kinematics-related parameters of peak angle and peak velocity, the testing dataset was assigned to predict classification of paralytic movements. Results High separation accuracy was obtained (mean 0.863; 95% confidence interval 0.857–0.869; p = 0.006). Conclusion This study highlights the ability of artificial intelligence to assist physicians and therapists evaluating hand movement recovery of stroke patients.

scholarly journals Design of Mirror Therapy System Base on Multi-Channel Surface-Electromyography Signal Pattern Recognition and Mobile Augmented Reality

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2142
Author(s):  
Lizheng Liu ◽  
Jianjun Cui ◽  
Jian Niu ◽  
Na Duan ◽  
Xianjia Yu ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Augmented Reality ◽  
Surface Electromyography ◽  
Hand Movement ◽  
Low Noise ◽  
Support Vector ◽  
Mobile Augmented Reality ◽  
Mirror Therapy ◽  
Semg Signal ◽  
Signal Pattern

Numerous studies have proven that the mirror therapy can make rehabilitation more effective on hemiparesis following a stroke. Using surface electromyography (SEMG) to predict gesture presents one of the important subjects in related research areas, including rehabilitation medicine, sports medicine, prosthetic control, and so on. However, current signal analysis methods still fail to achieve accurate recognition of multimode motion in a very reliable way due to the weak physiological signal and low noise-ratio. In this paper, a mirror therapy system based on multi-channel SEMG signal pattern recognition and mobile augmented reality is studied. Besides, wavelet transform method is designed to mitigate the noise. The spectrogram obtained by analyzing electromyography signals is proposed to be used as an image. Two approaches, including Convolutional Neural Network (CNN) and grid-optimized Support Vector Machine (SVM), are designed to classify the SEMG of different gestures. The mobile augmented reality provides a virtual hand movement in the real environment to perform mirror therapy process. The experimental results show that the overall accuracy of SVM is 93.07%, and that of CNN is up to 97.8%.

scholarly journals Research on support vector machine method for comprehensive evaluation after compression

2020 ◽  
Vol 145 ◽  
pp. 02072
Author(s):  
Wang Chengwang ◽  
Chen Shuai ◽  
Chen Gaojie ◽  
Lu Han
Keyword(s):  
Support Vector Machine ◽  
Hydraulic Fracture ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Kernel Functions ◽  
Support Vector ◽  
Treatment Technology ◽  
Machine Method ◽  
Fracture Width ◽  
Fracture Closure

Hydraulic fracturing is one of the important measures to increase production of oil and gas reservoirs. Pressure after the assessment of the existing technology is usually divided into direct and indirect diagnosis of hydraulic fracture. The diversity of evaluation results is due to the uncertainty and immaturity of the technology itself. Up to now, we have not found an economical and accurate hydraulic fracture evaluation method in the development and application of fracturing technology. In this paper, the pressure treatment technology after the comprehensive evaluation of boundary conditions is studied. By introducing the support vector regression theory, an evaluation model and a solution for correcting fracturing parameters are proposed. Fracturing parameters include fracture length, fracture height, fracture width, integrated fracturing fluid leakage coefficient, fracture conductivity, fracture closure pressure, and so on. For the optimization of various parameters, objective and scientific comprehensive evaluation results can be obtained by selecting different kernel functions. The results show that the model and method based on support vector machine are effective and practical.

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