1. Prepared by: Badiuzaman Bin Baharu - 15165 - Supervisor: Dr. Nasreen Bt. Badruddin

2. Background Of Study Numbers of accidents by types of vehicles in Malaysia, 2005 – 2009

3. Main Causes Fatigue Drowsiness

4. Objectives To investigate the physical changes of drowsiness that can be captured by webcam and meet the features: - Detect drowsiness signs - Fast - Accurate The analysis of the physical changes includes: - Eye blink pattern - Yawning

5. Scope Of Study Data collecting - Video is being recorded to be use as data. Video analysis - To detect the drowsiness signs each frame of the video. Algorithm development - To develop the specific command algorithm only for the video.

6. Problem Statement Current method to detect drowsiness - Complex computation. - Complex and expensive equipment. - Not comfortable and suitable to use in real-time driving. Electroencephalography (EEG)

7. Relevancy Of The Project Can be implement and be patent to be use in Malaysia. Aiming to reduce the numbers of fatal or non-fatal road accidents. To reduce the risk on the roads, so it is safe to be use by other people.

8. Literature Review 1. What is fatigue? 2. What is drowsiness? 3. Electroencephalography (EEG) method 4. Eye blink pattern method 5. PERCLOS method 6. Yawning method

9. What is fatigue? Tired; mental & physical. [1] Mental fatigue leads to drowsiness. Decrease of physiological arousal. (movement) Sensorimotor functions slower. (alertness) Driver’s ability to respond to a situation decrease. (reflects) [1]. G. Borghini, L. Astolfi, G. Vecchiato, D. Mattia, and F. Babiloni, "Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness," Neuroscience & Biobehavioral Reviews, 2012.

10. What is drowsiness? A state of near sleep. Strong desire to sleep. Cannot give full attention or focus on something. [1] Under influence of drowsiness is not in alert state. [1] [1]. G. Borghini, L. Astolfi, G. Vecchiato, D. Mattia, and F. Babiloni, "Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness," Neuroscience & Biobehavioral Reviews, 2012.

11. Electroencephalography (EEG) Measuring the brain electrical activity. [2] Can measure heartbeat, eye blink, even major physical movement. Use special hardware on the scalp to sense the electrical brain activity. The best method to applied in detecting fatigue and drowsiness. Sensor too sensitive with noise. [2]. B. T. Jap, S. Lal, P. Fischer, and E. Bekiaris, "Using EEG spectral components to assess algorithms for detecting fatigue," Expert Systems with Applications, vol. 36, pp. 2352-2359, 2009.

12. Eye blink pattern Learned the eye blink pattern of the duration of the eyelid were closed. [3] The longer times it takes, it is possible the person is asleep. Measures the position of eyelid and iris. Not detect drowsiness, predict drowsiness by using eye closing time = awake/fall asleep. [3]. T. Danisman, I. M. Bilasco, C. Djeraba, and N. Ihaddadene, "Drowsy driver detection system using eye blink patterns," in Machine and Web Intelligence (ICMWI), 2010 International Conference on, 2010, pp. 230-233.

13. Average person eye blink duration is < 400ms. Average eye blink is 75ms. The set point of drowsiness time taken as consideration in this project is T drowsy = 400ms. T sleeping = 800ms.

14. PERCLOS PERcentage of eye CLOSure. [4] Calculating the percentage of eyelid droops. Drowsy eyelid droops take times. If eyelid is 80% droops, it is consider as drowsy and fall asleep. Must use special camera to detect iris position. [4]. D. F. Dinges and R. Grace, "PERCLOS: A valid psychophysiological measure of alertness as assessed by psychomotor vigilance," Federal Highway Administration. Office of motor carriers, Tech. Rep. MCRT-98-006, 1998.

15. Yawning Detect the mouth positioning. [5] Compared with set of images data for mouth and yawning. A person will take several times before close their mouth while yawning. [5]. M. Saradadevi and P. Bajaj, "Driver fatigue detection using mouth and yawning analysis," IJCSNS International Journal of Computer Science and Network Security, vol. 8, pp. 183-188, 2008.

16. Method/ Advantages & Disadvantages EEGEye blink pattern PERCLOSYawning Complex methodYNNN Expensive hardware YNYN Special hardwareYNYN ComfortableNYYY Suitable in real- time driving NYYY Therefore, the eye blink pattern and yawning method will be used in this project based by its advantages and disadvantages table.

17. Methodology

18. START ERROR SUCCESS NO HARDWARE SELECTION SOFTWARE SELECTION DATA COLLECTION END CHANGES EYE BLINK PATTERN YAWNING DETECT DROWSINESS SIGNS? ALGORITHM DEVELOPMENT AND TESTING YES ALGORITHM TROUBLESHOOTING AND IMPROVEMENT

19. Gantt Chart & Key Milestones

20. References [1]. G. Borghini, L. Astolfi, G. Vecchiato, D. Mattia, and F. Babiloni, "Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness," Neuroscience & Biobehavioral Reviews, 2012. [2]. B. T. Jap, S. Lal, P. Fischer, and E. Bekiaris, "Using EEG spectral components to assess algorithms for detecting fatigue," Expert Systems with Applications, vol. 36, pp. 2352-2359, 2009. [3]. T. Danisman, I. M. Bilasco, C. Djeraba, and N. Ihaddadene, "Drowsy driver detection system using eye blink patterns," in Machine and Web Intelligence (ICMWI), 2010 International Conference on, 2010, pp. 230-233. [4]. D. F. Dinges and R. Grace, "PERCLOS: A valid psychophysiological measure of alertness as assessed by psychomotor vigilance," Federal Highway Administration. Office of motor carriers, Tech. Rep. MCRT-98-006, 1998. [5]. M. Saradadevi and P. Bajaj, "Driver fatigue detection using mouth and yawning analysis," IJCSNS International Journal of Computer Science and Network Security, vol. 8, pp. 183-188, 2008.