Open Access
Detrended fluctuation analysis of electromyography signal to identify hand movement
Angkoon Phinyomark,M Phothisonothai,Chusak Limsakul,Pornchai Phukpattaranont +3 more
- Vol. 2010, Iss: 40, pp 325-330
TLDR
Examination of the use of Detrended Fluctuation Analysis (DFA), a novel parameter, to study the properties of sEMG signal and to use these properties to identify the hand movements shows that DFA is suitable to feature extraction for multifunction myoelectric control.Abstract:
Recent advances in nonlinear analysis techniques are essential to understand the complexity of surface Electromyography (sEMG) signal. This research examines the use of Detrended Fluctuation Analysis (DFA), a novel parameter, to study the properties of sEMG signal and to use these properties to identify the hand movements. The experimental results of mean and standard deviation show that the scaling exponents of DFA in various hand motions have the significant difference values and small experimental variation. Cluster-to-cluster distance and scatter plot between scaling exponents of hand movements were demonstrated that DFA is suitable for sEMG feature extraction to characterize the sEMG signal. The application of this parameter is use to feature extraction for multifunction myoelectric control.read more
Citations
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Journal ArticleDOI
Fractal analysis features for weak and single-channel upper-limb EMG signals
TL;DR: The fractal analysis method, known as detrended fluctuation analysis (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.
Journal ArticleDOI
Electromyography (emg) signal classification based on detrended fluctuation analysis
TL;DR: From the viewpoints of maximum class separability, robustness, and complexity, scaling exponent obtained from the DFA method shows the appropriateness to be used as a feature in the classification of the EMG signal.
Proceedings ArticleDOI
Analysis on Non-Linear Features of Electroencephalogram (EEG) Signal for Neuromarketing Application
TL;DR: DFA features indicate that the Fast Food category is most preferred as it obtained the highest classification accuracy of 80%, and the most preferred products for Category A (Smartphones) is iPhone.
Book
Multifractals and Chronic Diseases of the Central Nervous System
TL;DR: This chapter outlines the general description of the diseases like epilepsy, Parkinson’s, Huntington's, Alzheimer's, and motor neuron diseases, and a discussion on the diagnostic tools and the methodologies adapted is reviewed in detail.
Proceedings ArticleDOI
Identification of low level sEMG signals for individual finger prosthesis
TL;DR: This research reports the identification of motor tasks in a human hand from weak myoelectric signals, aimed to control a prosthesis with individual finger flexion and wrist and grasps movements.
References
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