Evaluation of the Hilbert-Huang Transform for myoelectric pattern classification: Towards a method to detect movement intention
| Autor: | Alberto López Delis, Andrés F. Ruiz Olaya, Leondry Mayeta Revilla |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
business.industry
Speech recognition Short-time Fourier transform Wavelet transform Pattern recognition Linear discriminant analysis Hilbert–Huang transform Wavelet packet decomposition Time–frequency analysis symbols.namesake Fourier transform symbols Artificial intelligence business Hilbert spectrum Mathematics |
| Zdroj: | 2013 Pan American Health Care Exchanges (PAHCE). |
| DOI: | 10.1109/pahce.2013.6568259 |
| Popis: | Time-frequency (TFR) and time-scales representation techniques like short-time Fourier transform (STFT) and wavelet packet transform (WPT), respectively, have received considerable attention in the analysis of nonstationary signals, and specifically in the detection of movement intention. Surface Electromyography (SEMG) plays a leading role in the study and control of motor activities of people with movement disorders. This paper evaluates a new sEMG-based movement intention detection method at upper limb level. The process of feature extraction based on Hilbert-Huang Transform, a time-frequency domain technique. Selected features were the root mean square, average frequency and the central frequency variance of sEMG signals, applied to the frequency and instantaneous amplitude of Hilbert spectrum. In the pattern recognition stage, the method used is the Linear Discriminant Analysis (LDA). Two postprocessing techniques were incorporated: majority vote and reduction transitions. The efficiency of the algorithm was evaluated with parameters of sensitivity, specificity and error rate under Gaussian white noise and 60 Hz interfering conditions. Obtained results may be considered to use Hilbert-Huang transform in a real time application on the myoelectric pattern classification. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|