Identification of finger operation using support vector machine and control of myoelectric prosthetic hand based on integrated electromyogram

2014 ◽  
Author(s):  
Takeshi Kikuchi ◽  
Chiharu Ishii
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Prosthetic Hand ◽  
Integrated Electromyogram ◽  
And Control

scholarly journals Control of prosthetic hand by using mechanomyography signals based on support-vector machine classifier

2021 ◽  
Vol 23 (2) ◽  
pp. 1180
Author(s):  
Firas Saaduldeen Ahmed ◽  
Noha Abed-Al-Bary Al-jawady
Keyword(s):  
Pattern Recognition ◽  
Support Vector Machine ◽  
Wavelet Packet ◽  
Support Vector ◽  
Prosthetic Hand ◽  
Hand Gestures ◽  
Prosthetic Devices ◽  
Prosthetic Hands ◽  
Average Accuracy ◽  
And Control

<div>Prosthetic devices are necessary to help amputees achieve their daily activity in the natural way possible. The prosthetic hand has controlled by type of signals such as electromyography (EMG) and mechanomyography (MMG). The MMG signals have represented mechanical signals that generate during muscle contraction. These signals can be detected by accelerometers or microphones and any kind of sensors that can detect muscle vibrations. The contribution of the current paper is classifying hand gestures and control prosthetic hands depends on pattern recognition through accelerometer and microphone are to detect MMG signals. In addition to the cost of prosthetic hand less than other designs. Six subjects are involved. In this present work is the devices. In this study, two of them are amputee subjects. Each subject performs seven classes of movements. Pattern recognition (PR) is used to classify hand gestures. The wavelet packet transform (WPT) and root mean square (RMS) as features extracted from the signals and support vector machine (SVM) as a classifier. The average accuracy is 88.94% for offline tests and 84.45% for online tests. 3D printing technology is used in this study to build prosthetic hands.</div>

Study on Application of Support Vector Machine to Prediction of Blasting Vibration Velocity

2011 ◽  
Vol 71-78 ◽  
pp. 4155-4159
Author(s):  
Hai Xia Wei ◽  
Jie Zhu
Keyword(s):  
Support Vector Machine ◽  
Prediction Models ◽  
Vibration Velocity ◽  
Support Vector ◽  
Blasting Vibration ◽  
Training Samples ◽  
Svm Model ◽  
New Learning ◽  
And Control ◽  
Regression Theory

Based on the nonlinear regression theory of Support Vector Machine, SVM model was put forward to predict blasting vibration velocity by using monitoring data obtained in blasting site as training samples. By comparing the results of the two prediction models of the improved Sadaovsk and SVM, the feasibility of the new learning method of SVM model was verified, which will provide a new way to predict and control intensity of blasting vibration. The best way to select the parameters of SVM needs to be further explored.

A P2P Traffic Detection Method Based on Support Vector Machine

2012 ◽  
Vol 198-199 ◽  
pp. 1280-1285 ◽  
Author(s):  
Shang Fu Gong ◽  
Juan Chen
Keyword(s):  
Support Vector Machine ◽  
Detection Method ◽  
Detection Methods ◽  
Support Vector ◽  
Svm Classifier ◽  
Data Sets ◽  
Security Risks ◽  
Trip Time ◽  
And Control ◽  
Traffic Detection

The widely use of P2P (Peer-to-Peer) technology has caused resources take up too much, security risks and other problems, it is necessary to detect and control P2P traffic. After analyzing current P2P detection methods, a new method called TCBDM (Traffic Characters Based Detection Method) is put forward which combines P2P traffic character with support vector machine to detect P2P traffic. By choosing P2P traffic characters which differ from other network traffic, such as Round-Trip Time (RTT), the method creates a SVM classifier, uses a package named LIBSVM to classify P2P traffic in Moore_Set data sets. The result shows that TCBDM can detect P2P traffic effectively; the accuracy could reach 98%.

Financial Market Risk Overflow Modeling and Inspection Based on Support Vector Machine

2014 ◽  
Vol 571-572 ◽  
pp. 1189-1194
Author(s):  
Hong Han Zhu
Keyword(s):  
Support Vector Machine ◽  
Financial Markets ◽  
Financial Market ◽  
Market Risk ◽  
Risk Information ◽  
Support Vector ◽  
Test Statistics ◽  
Extreme Risk ◽  
And Control ◽  
Financial Market Risk

Combined granger test statistics based on VaR and CCF and machine learning theory to establish financial market risk overflow model of support vector machine. To analyze risk information overflow by the statistic characteristics of risk information overflow structure. The model can more effective to test variety forms of risk overflow, Main performance is the extreme risk for information received peripheral selectivity and market volatility non-stability. Emerging markets characteristics in A Shares is evident, the performance are the selective reception of outside extreme risk information. Empirical results demonstrate that models have certain value to the management and control of overflow risks in financial markets.

scholarly journals Object Recognition using Lucas-Kanade Technique and Support Vector Machine Based Classification in Video Surveillance Systems

2019 ◽  
Vol 9 (1) ◽  
pp. 2219-2224
Keyword(s):  
Support Vector Machine ◽  
Object Recognition ◽  
Video Surveillance ◽  
Support Vector ◽  
Surveillance Systems ◽  
Multiple Objects ◽  
Novel Approach ◽  
Recognition Of Objects ◽  
And Control

Object recognition in video surveillance systems is the primary and most significant challenge task in the field of image processing. Video Surveillance systems provides us continuous monitoring of the objects for the enhancement of security and control. This paper presents novel approach recognizing the objects using Shi-Tomasi approach for detecting the corners of the object and then applies the Lucas-Kanade techniques to extract the features of the objects. The main objective of this paper is providing precise recognition of objects and estimation of their location from an unknown scene. Whenever the object is recognized from extracted frames of the input video the background subtraction will be applied. Then the classification of the objects into their respective categories can be achieved using support vector machine classifier by supervised learning. In case of multiple objects of different classes in a single frame, a vector containing the classes of all the detected in that frame is produced as output. The results of this work are drawn in the MATLAB tool by considering the input video dataset taken from various sources and extracting the frames from the input video for the detection then the efficiency of the proposed techniques will be measured.

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