Next Article in Journal
Numerical Fatigue Analysis of Induction-Hardened and Mechanically Post-Treated Steel Components
Previous Article in Journal
Automatic Test and Sorting System for the Slide Valve Body of Oil Control Valve Based on Cartesian Coordinate Robot
Open AccessArticle

Feature Selection Based on Binary Tree Growth Algorithm for the Classification of Myoelectric Signals

1
Fakulti Kejuruteraan Elektrik, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal 76100, Melaka, Malaysia
2
Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal 76100, Melaka, Malaysia
*
Authors to whom correspondence should be addressed.
Machines 2018, 6(4), 65; https://doi.org/10.3390/machines6040065
Received: 10 October 2018 / Revised: 7 December 2018 / Accepted: 11 December 2018 / Published: 13 December 2018
Electromyography (EMG) has been widely used in rehabilitation and myoelectric prosthetic applications. However, a recent increment in the number of EMG features has led to a high dimensional feature vector. This in turn will degrade the classification performance and increase the complexity of the recognition system. In this paper, we have proposed two new feature selection methods based on a tree growth algorithm (TGA) for EMG signals classification. In the first approach, two transfer functions are implemented to convert the continuous TGA into a binary version. For the second approach, the swap, crossover, and mutation operators are introduced in a modified binary tree growth algorithm for enhancing the exploitation and exploration behaviors. In this study, short time Fourier transform (STFT) is employed to transform the EMG signals into time-frequency representation. The features are then extracted from the STFT coefficient and form a feature vector. Afterward, the proposed feature selection methods are applied to evaluate the best feature subset from a large available feature set. The experimental results show the superiority of MBTGA not only in terms of feature reduction, but also the classification performance. View Full-Text
Keywords: feature selection; tree growth algorithm; electromyography; classification; time frequency features feature selection; tree growth algorithm; electromyography; classification; time frequency features
Show Figures

Figure 1

MDPI and ACS Style

Too, J.; Abdullah, A.R.; Mohd Saad, N.; Mohd Ali, N. Feature Selection Based on Binary Tree Growth Algorithm for the Classification of Myoelectric Signals. Machines 2018, 6, 65.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop