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Cough Detection for Prevention Against the COVID-19 Pandemic

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Proceedings of the 3rd International Conference on Electronic Engineering and Renewable Energy Systems (ICEERE 2022)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 954))

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Abstract

This paper proposes a novel and robust technique for remote cough recognition for COVID-19 detection. This technique is based on sound and image analysis. The objective is to create a real-time system combining artificial intelligence (AI) algorithms, embedded systems, and network of sensors to detect COVID-19-specific cough and identify the person who coughed. Remote acquisition and analysis of sounds and images allow the system to perform both detection and classification of the detected cough using AI algorithms and image processing to identify the coughing person. This will give the ability to distinguish between a normal person and a person carrying the COVID-19 virus.

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Author information

Authors and Affiliations

  1. SIGER Laboratory, Faculty of Sciences and Technology, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Btissam Bouzammour, Ghita Zaz, Ali Ahaitouf & Mohammed Jorio

  2. SIGER Laboratory, National School of Applied Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Malika Alami Marktani

Authors
  1. Btissam Bouzammour
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  2. Ghita Zaz
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  3. Malika Alami Marktani
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  4. Ali Ahaitouf
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  5. Mohammed Jorio
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Corresponding author

Correspondence to Btissam Bouzammour .

Editor information

Editors and Affiliations

  1. National School of Applied Sciences, Mohamed Premier University, Oujda, Morocco

    Hajji Bekkay

  2. Faculty of Sciences and Technology, Jijel University, Jijel, Algeria

    Adel Mellit

  3. Department of Electrical Engineering, Electronics and Computer Engineering, University of Catania, Catania, Italy

    Antonio Gagliano

  4. University of Picardie Jules Verne, Amiens, France

    Abdelhamid Rabhi

  5. National School of Applied Sciences, Mohammed Premier University, Oujda, Morocco

    Mohammed Amine Koulali

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Cite this paper

Bouzammour, B., Zaz, G., Alami Marktani, M., Ahaitouf, A., Jorio, M. (2023). Cough Detection for Prevention Against the COVID-19 Pandemic. In: Bekkay, H., Mellit, A., Gagliano, A., Rabhi, A., Amine Koulali, M. (eds) Proceedings of the 3rd International Conference on Electronic Engineering and Renewable Energy Systems. ICEERE 2022. Lecture Notes in Electrical Engineering, vol 954. Springer, Singapore. https://doi.org/10.1007/978-981-19-6223-3_46

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  • DOI: https://doi.org/10.1007/978-981-19-6223-3_46

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-6222-6

  • Online ISBN: 978-981-19-6223-3

  • eBook Packages: EnergyEnergy (R0)

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