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A new detection method for EMG activity monitoring

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Abstract

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).

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Notes

  1. Usually, the window size is taken even in most CFAR papers. So, for simplicity, we consider this case here and we omit the normalizing constant 1/M as it is incorporated in the threshold factor T.

  2. This expertise was conducted within the ECOTECH project [8] where the EMG signal segmentation has been achieved by biomedical researchers using visual inspection.

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Acknowledgments

The present paper used collected data from the french national project ECOTECH supported by the french National Agency for research under the contract No. ANR-12-TECS-0020.

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Authors and Affiliations

  1. Laboratoire Traitement du Signal, Ecole Militaire Polytechnique, Algiers, Algeria

    Hichem Bengacemi & Ammar Mesloub

  2. PRISME Laboratory, University of Orléans, 12 Rue de Blois, 45067, Orléans, France

    Hichem Bengacemi, Karim Abed-Meraim & Olivier Buttelli

  3. Laboratoire Signaux et Systemes, Université de Mostaganem, Mostaganem, Algeria

    Abdelaziz Ouldali

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  1. Hichem Bengacemi
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  2. Karim Abed-Meraim
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  3. Olivier Buttelli
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  4. Abdelaziz Ouldali
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  5. Ammar Mesloub
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Correspondence to Hichem Bengacemi.

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Bengacemi, H., Abed-Meraim, K., Buttelli, O. et al. A new detection method for EMG activity monitoring. Med Biol Eng Comput 58, 319–334 (2020). https://doi.org/10.1007/s11517-019-02048-0

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