Design of ANFIS based driver fatigue detection system using thermopile and ambient temperature sensors

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    Safety systems play a vital role in automotive industry. Among the safety systems, fatigue detection system is main part in monitoring the level of fatigue of the driver. Vehicle line departure warning system, drivers eye/face monitoring system, steering pattern-monitoring system etc. are a few of the fatigue detection systems deployed in the vehicle by the OEMs (Original Equipment Manufacturers). Lack of proper sleep, sleep apnea, spikes in blood sugar level, anemia etc., are considered as some of reasons for drivers’ fatigue. In this paper, we have designed and prototyped a system to monitor the lev-el of fatigue of the driver in real-time. The proposed system uses temperature sensors (thermopile array and ambient temperature sensor) to capture the heat map information of drivers’ face and the frequency of yawns. The neuro-fuzzy system in the background processes the data from the sensors for making a logical conclusion – fatigue/ no fatigue. The accuracy of detection can be improved by training the system across multiple subjects. The proposed system was evaluated across various membership functions, for selecting the right membership function.

     


  • Keywords


    ANFIS : Arduino UNO : Fatigue Detection: MLX Sensor: MATLAB:

  • References


      [1] US Department of Transportation, “An Evaluation of Emerging Driver Fatigue Detection Measures and Technologies”, 2009.

      [2] D. L. Hall and J. Llinas, "An introduction to multisensor data fusion," in Proceedings of the IEEE, vol. 85, no. 1, pp. 6-23, Jan 1997. doi: 10.1109/5.554205

      [3] P. Thiffault and J. Bergeron, “Monotony of road environment and driver fatigue: A simulator study,” Accid. Anal. Prev., vol. 35, no. 3, pp. 381–391, 2003

      [4] DENSO Technical Review, vol. 12, pp. 113–118, 2006.Miss.G. Victoreia et al., “Driver Fatigue Monitoring System Using Eye Closure”, Int. Journal of Modern Engineering Research, vol.4, Iss.11, 2014.

      [5] M. S. Firoozan, S. Porkhial, and A. S. Nejad, “Effect of tissue and atmosphere’s parameters on human eye temperature distribution,” J. Therm. Biol., vol. 47, pp. 51–58, 2015.

      [6] Melexis "MLX90621 16 x 4 Pixel Thermal Imager - Melexis | DigiKey", Digikey.com, 2018. [Online]. Available: https://www.digikey.com/en/product-highlight/m/melexis/mlx90621-16-x-4-pixel-thermal-imager. [Accessed: 17- May- 2018].

      [7] DS18B20, "One Wire Digital Temperature Sensor - DS18B20", Sparkfun.com, 2018. [Online]. Available: https://datasheets.maximintegrated.com/en/ds/DS18B20.pdf. [Accessed: 17- May- 2018].

      [8] Jang, J. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), pp.665-685.

      [9] Gandhi, P., Adarsh, S. and Ramachandran, K. (2017). Performance Analysis of Half Car Suspension Model with 4 DOF using PID, LQR, FUZZY and ANFIS Controllers. Procedia Computer Science, 115, pp.2-13.

      [10] C. Qing-xie and W. U. Chun-fu, “Based on ANFIS,” vol. 2, no. 4, pp. 278–281, 2017.

      [11] X. Li, Y. Wei, and Y. Li, “Research on the intelligent temperature control based on ANFIS for reheating furnace in rolling steel line,” Proc. 2015 27th Chinese Control Decis. Conf. CCDC 2015, pp. 5688–5692, 2015.

      Y. Chai and J. Zhang, “Sensorless method research for SRM based on ANFIS,” Proc. - 2010 Int. Conf. Intell. Syst. Des. Eng. Appl. ISDEA 2010, vol. 2, no. 2, pp. 361–365, 2011

 

View

Download

Article ID: 30918
 
DOI: 10.14419/ijet.v7i4.6.30918




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.