ON THE CLASSIFICATION OF HAND MOVEMENTS WITH ELECTROMYOGRAM SIGNALS OBTAINED FROM ARM MUSCLES FOR CONTROLLING HAND PROSTHESIS

Yıl 2017, Cilt: 17 Sayı: 2, 3425 - 3432, 27.07.2017

Sami Arıca , Rouhollah Kıan Ara Kerem Tuncay Özgünen

Öz

Electromyography (EMG) signals are outcomes of skeletal
muscle activities. In this study EMG signal is read non-invasively from the
skin surface by placing electrodes on the skin of specified muscle (surface EMG
- SEMG). The aim of the study is to generate control signals from SEMGs
measured from four hand muscles; Extensor carpi radialis, Palmaris longus,
Pronator quadratus and Flexor digitorum superficialis to navigate a prosthetic
hand. The SEMGs for five hand movements; finger flexion, wrist flexion, wrist
extension, pronation, supination have been acquired. The features have been
computed from the windowed EMG of a 0.512 second interval.  From each muscle (channel), root mean square
value, mean frequency and peak frequency are employed as features. The mean
frequency is computed from the discrete Fourier transform, by counting number
of zero crossings and using minimum norm subspace frequency estimation
technique. The peak frequency is also obtained by employing the discrete
Fourier transform. These features and their pairwise combinations have been
classified with support vector machine. The classifications have been done for
two scenarios: 1. For each subject the right (left) hand movement is classified
from the right (left) arm EMG data. 2. 
The left (right) hand movement of a subject is classified from the right
(left) arm EMG data of the same subject.

The right hand and left hand data recorded from two males and
two females. The average right-hand
success of the classification was 82.0%, while the left-hand categorization was
83.5%. Interestingly, the left-hand versus right-hand and the right-hand versus
left-hand classification success was obtained 65.7%.

Anahtar Kelimeler

Electromyography, hand prosthesis, support vector machine

Kaynakça

• Liu YH, Huang HP, Weng CH. Recognition of Electromyographic Signals Using Cascaded Kernel Learning Machine. IEEE/ASME Transactions on Mechatronics 2007; 12 (3): 253-264.
• Momen K, Krishnan S and Chau T. Real-Time Classification of Forearm Electromyographic Signals Corresponding to User-Selected Intentional Movements for Multifunction Prosthesis Control. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2007; 15 (4): 535-542.
• Rekhi NS, Arora AS, Singh S, Singh D. Multi-Class SVM Classification of Surface EMG Signal for Upper Limb Function. In: 3rd International Conference on Bioinformatics and Biomedical Engineering; 11-13 June 2009; Beijing, China. pp. 1- 4.
• Ahsan MR, Ibrahimy MI, Khalifa OO. Hand motion detection from EMG signals by using ANN based classifier for Human Computer Interaction. In: Fourth International Conference on Modeling, Simulation and Applied Optimization; 19-21 April 2011; Kuala Lumpur, Malaysia. pp. 1-6.
• Baspinar U, Varol HS, Yildiz K. Classification of hand movements by using artificial neural network. In: 2012 International Symposium on Innovations in Intelligent Systems and Applications; 2012; Trabzon, Turkey. pp. 1-4.
• Al-Assaf Y. Surface myoelectric signal analysis: Dynamic approaches for change detection and classification. IEEE Transactions on Biomedical Engineering 2006; 53(11): 2248-2256.
• Zardoshti-Kermani M, Wheeler BC, Badie K and Hashemi RM. EMG feature evaluation for movement control of upper extremity prostheses. IEEE Transactions on Rehabilitation Engineering 1995; 3(4): 324-333.
• Haykin S. Neural Networks: A Comprehensive Foundation. 2nd ed. Prentice Hall, 1999.
• Shenoi BA. Introduction to Digital Signal Processing and Filter Design. Wiley, 2005.
• Haykin S. Communication systems. John Wiley & Sons, Inc, 1995.