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Updated: Jun 18, 2026

A Detailed Protocol for Perspiration Monitoring Using a Novel, Small, Wireless Device
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A Detailed Protocol for Perspiration Monitoring Using a Novel, Small, Wireless Device

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A portable device for real time drowsiness detection using novel active dry electrode system.

Pai-Yuan Tsai1, Weichih Hu, Terry B J Kuo

  • 1Chung Yuan Christian University, Chung Li, ROC. view@mhit.url.com.tw

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|December 8, 2009
PubMed
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This study developed a real-time electroencephalogram (EEG) system to detect drowsy drivers. The portable device uses EEG analysis to identify drowsiness, alerting drivers with a vibrator and beeper.

Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Signal Processing

Background:

  • Electroencephalogram (EEG) signals are crucial for understanding vigilance states.
  • Driver drowsiness is a significant cause of road accidents.
  • Non-invasive methods for real-time drowsiness detection are needed.

Purpose of the Study:

  • To construct a real-time electroencephalogram (EEG)-based system for detecting drowsy drivers.
  • To develop a portable and non-invasive system for monitoring driver vigilance.
  • To implement advanced signal processing techniques for accurate drowsiness classification.

Main Methods:

  • Utilized a novel six-channel active dry electrode system for non-invasive EEG acquisition.
  • Employed a TMS320VC5510 DSP chip for algorithm processing and a MSP430F149 chip as a controller.

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  • Implemented stationary wavelet transform to extract EEG features (integral and zero crossings).
  • Used a backpropagation neural network for classifying vigilance states (alertness vs. drowsiness).

    • Main Results:

    • The system achieved real-time discrimination between alertness and drowsiness.
    • Classification accuracy for alertness was 79.1%.
    • Classification accuracy for drowsiness was 90.91%.

    Conclusions:

    • The developed EEG-based system effectively detects driver drowsiness in real-time.
    • The system provides timely warnings to drivers through a vibrator and beeper upon detecting drowsiness.
    • This technology holds potential for enhancing road safety by mitigating fatigue-related accidents.