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Real-Time Drowsiness Detection and Health Status System in Agricultural Vehicles Using Artificial Intelligence

  • Conference paper
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Robot 2023: Sixth Iberian Robotics Conference (ROBOT 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 978))

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Abstract

This study presents a real-time monitoring system for preventing accidents in agricultural vehicles by monitoring the farmer and driving conditions. The system employs vital signals monitoring, using a bracelet to assess the farmer’s health status, and drowsiness detection, utilizing a camera and Artificial Intelligence to evaluate the level of drowsiness. In emergencies, the system communicates with a central station to identify the triggering factor. Driving conditions are monitored using inertial and GPS data. The focus of this paper is on the farmer’s monitoring aspect. Health status is determined by analyzing Heart Rate and Oxygen Saturation values measured by the bracelet. While currently measuring two values, the system is designed to accommodate additional measurements. Multiple algorithms for driver drowsiness detection were tested, highlighting the need to consider different approaches in the final solution. This research proposes an integrated system to enhance safety and prevent accidents in agricultural vehicles, addressing the specific requirements of the farming industry.

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Acknowledgements

This research was supported by project AgroSafeBox-Intelligent Alert System for AgroVehicles Rollover and Driver Safety funded by the PO Centro 2020 (CENTRO-01-0247-FEDER-047199).

Author information

Authors and Affiliations

  1. IEETA/LASI/DETI, Aveiro University, Aveiro, Portugal

    Beatriz Soares, Daniel Oliveira & Nuno Lau

  2. ProjectBox, Viseu, Portugal

    Helio Palaio

  3. Polytechnic Institute of Viseu, Viseu, Portugal

    Olga Contente & Daniel Albuquerque

  4. TIS, Coimbra, Portugal

    Daniel Marques

Authors
  1. Beatriz Soares
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  2. Daniel Oliveira
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  3. Nuno Lau
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  4. Helio Palaio
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  5. Olga Contente
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  6. Daniel Albuquerque
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  7. Daniel Marques
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Corresponding author

Correspondence to Beatriz Soares .

Editor information

Editors and Affiliations

  1. Dep. de Eng. Electrotecnica e de Computadores, University of Coimbra, Coimbra, Portugal

    Lino Marques

  2. Department of Electrónica Industrial, University of Minho, Escola de Engenharia, Guimarães, Portugal

    Cristina Santos

  3. Department of Electrical Engineering, ESTIG, Polytechnic Institute of Bragança, Bragança, Portugal

    José Luís Lima

  4. Centro Universitario de la Defensa, Zaragoza, Spain

    Danilo Tardioli

  5. Centre for Automation and Robotics UPM-CSIC, Universidad Politécnica de Madrid, Madrid, Spain

    Manuel Ferre

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Soares, B. et al. (2024). Real-Time Drowsiness Detection and Health Status System in Agricultural Vehicles Using Artificial Intelligence. In: Marques, L., Santos, C., Lima, J.L., Tardioli, D., Ferre, M. (eds) Robot 2023: Sixth Iberian Robotics Conference. ROBOT 2023. Lecture Notes in Networks and Systems, vol 978. Springer, Cham. https://doi.org/10.1007/978-3-031-59167-9_28

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