Steering Wheel Based Vehicle Human Interface

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While drowsy driving has increased safety concerns. 28 percent of drivers had driven while drowsy at least once per month in the past year. Ten percent of drivers admitted having fallen asleep in the past year and 41% at some point in their lifetime. Furthermore, studies have concluded that at least 15–20% of fatal car accidents are fatigue-related. To detect driving fatigue, several approaches have been proposed, including facial expression systems, lane departure monitoring system, electroencephalography (EEG).

Facial expression and lane-departure methods are the physical- and operating-based methods that indirectly measure drivers' cognitive states. EEG is gold standard for determination of driver’s brain functional status. Spectrum components of brain wave (recorded by electroencephalography, EEG) have been demonstrated to be quantitatively changed from alert to drowsy state. Our previous work has demonstrated that quantitative EEG can be used for drowsy driving monitoring. EEG change threshold has been identified that can predict accurately drowsy level.

Head-based wearable device has been proposed for drowsiness driving monitoring. However, driver may not always wear a head based wearable device. Now we propose to develop a steering wheel mounted, hand-based sensor system to measure the brain wave. This is based on that brain wave signals (EEG) can be transferred from the brain via peripheral nerve to the hand. The nerve signal conduction speed is 80-120 m/s, which is quick enough for sensor system to capture the brain nerve signals. Drowsy brain wave signals can thus be measured from the hands.

Prototype of steering wheel based vehicle human interface (SWB-VHI) includes the following components. Firmware includes 1) wheel based sensor – dry electrodes made of polymer or graphene which captures high signal-to noise-ratio (SNR) bioelectrical signals, 2) miniature customized bioelectrical signal acquisition and processing board, 3) Controlling Microchip - Renesas promotion board (model RL78/G13) or Ardurino Mega board. Software includes customized algorithms developed using C++ language. Drowsy status criteria are setup based on our EEG database.

This system can be integrated into vehicle system to activate automate driving when driver becomes drowsy during operation. It can also monitor driver sympathetic and parasympathetic nerve activity as well as wellness and sudden illness.


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  • Name:
    Chaoayng Chen
  • Type of entry:
    Team members:
    Chaoyang Chen, Yang Zhou, Yousef Alshahrani, Bo Cheng, Mark Cheng, John Cavanaugh
  • Profession:
  • Patent status: