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A machine learning based method for classification of fractal features of forearm sEMG using Twin Support vector machines.
- Source :
-
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference [Annu Int Conf IEEE Eng Med Biol Soc] 2010; Vol. 2010, pp. 4821-4. - Publication Year :
- 2010
-
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).
- Subjects :
- Algorithms
Female
Forearm physiology
Humans
Male
Reproducibility of Results
Sensitivity and Specificity
Artificial Intelligence
Diagnosis, Computer-Assisted methods
Electromyography methods
Fractals
Muscle Contraction physiology
Muscle, Skeletal physiology
Pattern Recognition, Automated methods
Subjects
Details
- Language :
- English
- ISSN :
- 2375-7477
- Volume :
- 2010
- Database :
- MEDLINE
- Journal :
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
- Publication Type :
- Academic Journal
- Accession number :
- 21097298
- Full Text :
- https://doi.org/10.1109/IEMBS.2010.5627902