i-manager Publications

Real-Time Fatigue Detection using a Low Cost Wireless EEG Device

Shashidhar R.*, Shruthi B. R.**, Hariprasad S. A.***, Kavita R. Singh****

* Department of Electronics and Communication Engineering, Sri Jayachamarajendra College of Engineering, JSS Science and Technology University, Mysore, India.

** Department of Medical Electronics, MVJ College of Engineering, Bangalore, Karnataka, India.

*** Department of Electronics and Communication Engineering, Jain University, Bengaluru, Karnataka, India.

**** Department of Computer Technology, Yeshwantrao Chavan College of Engineering, Nagpur, Maharashtra, India.

Periodicity:January - June'2020
DOI : https://doi.org/10.26634/jes.8.2.17197

Abstract

The evolution of methods for the observation of psychological tiredness has wide range of implementation in regions where continuous observation is of prominence like safety and transportation. This work is to improve a new driving fatigue recognition method in real time using brainsense band. One of the most significant features of road accidents is the driver's fatigue and this can be fatal, resulting in serious injuries and death. Therefore, a reliable real-time driver fatigue detection system that can alert the driver before an accident occurs is required. The various techniques available using EEG signals to detect the driver's drowsiness are found to be more effective. Here we used a single channel wireless EEG device which can obtain driver's attention and blink levels. The values are analyzed using MATLAB as a platform to detect driver's fatigue level. A threshold is set for the driver's attention and blink values and the system alerts the driver with an alarm if the extracted values fall below the threshold. A test is conducted on 15 subjects using the Brainsense headband, which provides an effective resolution for analyzing and avoiding driver fatigue in real-time situations.

Keywords

Fatigue Detection System, EEG (Electroencephalogram), Brainwave Sensing, Wearable Device, MATLAB.

How to Cite this Article?

Shashidhar, R., Shruthi, B. R., Hariprasad, S. A., and Singh, K. R. (2020). Real-Time Fatigue Detection using a Low Cost Wireless EEG Device. i-manager's Journal on Embedded Systems, 8(2), 8-13. https://doi.org/10.26634/jes.8.2.17197

References

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Online	35	35	200	15
Pdf & Online	35	35	400	25

Real-Time Fatigue Detection using a Low Cost Wireless EEG Device

Abstract

Keywords

How to Cite this Article?

References

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