Fractal analysis features for weak and single-channel upper-limb EMG signals

Authors:
Angkoon Phinyomark;Pornchai Phukpattaranont;Chusak Limsakul
Affiliations:
Department of Electrical Engineering, Faculty of Engineering, Prince of Songkla University, 15 Kanjanavanich Road, Kho Hong, Hat Yai, 90112 Songkhla, Thailand;Department of Electrical Engineering, Faculty of Engineering, Prince of Songkla University, 15 Kanjanavanich Road, Kho Hong, Hat Yai, 90112 Songkhla, Thailand;Department of Electrical Engineering, Faculty of Engineering, Prince of Songkla University, 15 Kanjanavanich Road, Kho Hong, Hat Yai, 90112 Songkhla, Thailand
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

Citing 4
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Abstract

Electromyography (EMG) signals are the electrical manifestations of muscle contractions. EMG signals may be weak or at a low level when there is only a small movement in the major corresponding muscle group or when there is a strong movement in the minor corresponding muscle group. Moreover, in a single-channel EMG classification identifying the signals may be difficult. However, weak and single-channel EMG control systems offer a very convenient way of controlling human-computer interfaces (HCIs). Identifying upper-limb movements using a single-channel surface EMG also has a number of rehabilitation and HCI applications. The fractal analysis method, known as detrended fluctuation analysis (DFA), has been suggested for the identification of low-level muscle activations. This study found that DFA performs better in the classification of EMG signals from bifunctional movements of low-level and equal power as compared to other successful and commonly used features based on magnitude and other fractal techniques.