NDLI: A machine learning based method for classification of fractal features of forearm sEMG using Twin Support vector machines

Content Provider	IEEE Xplore Digital Library
Author	Arjunan, S.P. Kumar, D.K. Naik, G.R.
Copyright Year	2010
Description	Author affiliation: School of Electrical and Computer Engineering, RMIT university, Melbourne, VIC 3001, Australia (Arjunan, S.P.; Kumar, D.K.; Naik, G.R.)
Abstract	Classification of surface electromyogram (sEMG) signal is important for various applications such as prosthetic control and human computer interface. Surface EMG provides a better insight into the strength of muscle contraction which can be used as control signal for different applications. Due to the various interference between different muscle activities, it is difficult to identify movements using sEMG during low-level flexions. A new set of fractal features - fractal dimension and Maximum fractal length of sEMG has been previously reported by the authors. These features measure the complexity and strength of the muscle contraction during the low-level finger flexions. In order to classify and identify the low-level finger flexions using these features based on the fractal properties, a recently developed machine learning based classifier, Twin Support vector machines (TSVM) has been proposed. TSVM works on basic learning methodology and solves the classification tasks as two SVMs for each classes. This paper reports the novel method on the machine learning based classification of fractal features of sEMG using the Twin Support vector machines. The training and testing was performed using two different kernel functions - Linear and Radial Basis Function (RBF).
Starting Page	4821
Ending Page	4824
File Size	700077
Page Count	4
File Format	PDF
ISBN	9781424441235
ISSN	1557170X
DOI	10.1109/IEMBS.2010.5627902
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-31
Publisher Place	Argentina
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fractals Support vector machines Kernel Muscles Electromyography Testing Accuracy
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

SVM for Estimation of Wrist Angle from Sonomyography And SEMG Signals

Knee motion pattern classification from trunk muscle based on sEMG signals

The Application of Support Vector Machine in Pattern Recognition

Machine learning approach to an otoneurological classification problem

Use of sEMG in identification of low level muscle activities: Features based on ICA and fractal dimension

A new Mercer sigmoid kernel for clinical data classification

Wavelet analysis for Support Vector Machine classification of motor unit action potentials

Combined use of sEMG and accelerometer in hand motion classification considering forearm rotation

Confidence-based classification with dynamic conformal prediction and its applications in biomedicine

A machine learning based method for classification of fractal features of forearm sEMG using Twin Support vector machines

Similar Documents

SVM for Estimation of Wrist Angle from Sonomyography And SEMG Signals

Knee motion pattern classification from trunk muscle based on sEMG signals

The Application of Support Vector Machine in Pattern Recognition

Machine learning approach to an otoneurological classification problem

Use of sEMG in identification of low level muscle activities: Features based on ICA and fractal dimension

A new Mercer sigmoid kernel for clinical data classification

Wavelet analysis for Support Vector Machine classification of motor unit action potentials

Combined use of sEMG and accelerometer in hand motion classification considering forearm rotation

Confidence-based classification with dynamic conformal prediction and its applications in biomedicine

A machine learning based method for classification of fractal features of forearm sEMG using Twin Support vector machines