A New Detection Method for EMG Activity Monitoring
| Autor: | Abdelaziz Ouldali, Hichem Bengacemi, Karim Abed-Meraim, Olivier Buttelli, Ammar Mesloub |
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| Přispěvatelé: | Laboratoire pluridisciplinaire de recherche en ingénierie des systèmes, mécanique et énergétique (PRISME), Université d'Orléans (UO)-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA), Abed-Meraim, Karim |
| Předmět: |
Mean squared error
[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing Computer science 0206 medical engineering Biomedical Engineering 02 engineering and technology Constant false alarm rate 03 medical and health sciences Activity monitoring 0302 clinical medicine [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing Robustness (computer science) Humans Computer Simulation ComputingMilieux_MISCELLANEOUS Monitoring Physiologic Probability Voice activity detection Electromyography business.industry Order statistic Detector Parkinson Disease Signal Processing Computer-Assisted Pattern recognition 020601 biomedical engineering Computer Science Applications Artificial intelligence business Algorithms 030217 neurology & neurosurgery |
| Zdroj: | HAL Journal of Medical and Biological Engineering Journal of Medical and Biological Engineering, Springer Verlag, 2019 |
| ISSN: | 1609-0985 2199-4757 |
| Popis: | This paper introduces a new approach for electromyography (EMG) activity monitoring based on an improved version of the adaptive linear energy detector (ALED), a widely used technique in voice activity detection. More precisely, we propose a modified ALED technique (named M-ALED) to improve the method's robustness with respect to noise. To achieve this objective, M-ALED relies on the Teager-Kaiser operator for signal pre-conditioning to increase the SNR and uses the order statistics to gain robustness against the signal's impulsiveness. We propose again to exploit the order statistics for the initial signal baseline estimation to deal with the cases where such information is unavailable. Finally, since M-ALED detects the signal's activity at the frame level, we propose in a second stage to refine this detection (at the sample level) by using a constant false alarm rate (CFAR) approach leading to the fine M-ALED (FM-ALED) solution. The performance of FM-ALED is assessed via real and synthetic EMG signal recordings and the obtained results highlight its effectiveness as compared with the state-of-the-art methods (it reduces the mean error probability by a factor close to 2). |
| Databáze: | OpenAIRE |
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