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CORRELATION BETWEEN EYE MOVEMENTS AND MOUTH MOVEMENTS TO DETECT DRIVER COGNITIVE DISTRACTION

BRAIN INSPIRED COGNITIVE SYSTEMS (BICS) 2010 16 TH JULY 2010 MADRID, SPAIN. CORRELATION BETWEEN EYE MOVEMENTS AND MOUTH MOVEMENTS TO DETECT DRIVER COGNITIVE DISTRACTION. afizan azman : a.azman@lboro.ac.uk qinggang meng : q.meng@lboro.ac.uk eran edirisinghe : e.a.edirisinghe@lboro.ac.uk.

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CORRELATION BETWEEN EYE MOVEMENTS AND MOUTH MOVEMENTS TO DETECT DRIVER COGNITIVE DISTRACTION

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  1. BRAIN INSPIRED COGNITIVE SYSTEMS (BICS) 2010 16TH JULY 2010 MADRID, SPAIN CORRELATION BETWEEN EYE MOVEMENTS AND MOUTH MOVEMENTS TO DETECT DRIVER COGNITIVE DISTRACTION afizan azman : a.azman@lboro.ac.uk qinggang meng : q.meng@lboro.ac.uk eran edirisinghe : e.a.edirisinghe@lboro.ac.uk

  2. FACTS AND STATISTICS • BBC News has been reported that in year of 2008, there were 2,538 people were killed on Britain’s roads Department for Transportation (DfT) in UK has reported in 2007, that 92% of passenger travel is by road. a.azman, q.meng, e.edirisinghe

  3. RESEARCH OVERVIEW To propose a comprehensive and effective system to detect drivers’ cognitive distraction in a real time via physiological measurement a.azman, q.meng, e.edirisinghe

  4. RESEARCH OBJECTIVE To suggest new features for cognitive distraction detection – lip and eyebrow movements (future work) To use data analysis approaches/techniques- Dynamic Bayesian Networks To use faceAPI toolkit for lip and eyebrow movement detection. a.azman, q.meng, e.edirisinghe

  5. RESEARCH PLAN collect real-time data on driver visual and cognitive behaviour-modelling process recognize what the driver is doing (using contextual information such as manoeuvres, actions and states) predict the alertness level of the driver design an interface to assist the driver a.azman, q.meng, e.edirisinghe

  6. DRIVING SAFETY ISSUE Related to distraction Driver state affecting factors Fatigue Monotony Drugs Alcohol Driver trait factors Experience Age Environmental factors Road environment demands Traffic demandsVehicle ergonomics a.azman, q.meng, e.edirisinghe

  7. MANUAL VISUAL DISTRACTION COGNITIVE a.azman, q.meng, e.edirisinghe

  8. COGNITIVE DISTRACTION DISTRACTION COGNITIVE COGNITIVE Cognitive produces(output) distraction or causes(input) distraction situation that might lead or shift a person from putting his attention doing something Harder to learn and measure – internal distraction Mind off the road Closely related to visual distraction Delay respond, slow brake, missed traffic light/signboard, unable to stay in a safe distance a.azman, q.meng, e.edirisinghe

  9. COGNITIVE DISTRACTION EFFECT Any types of distraction can undermine- (a) vehicle control (b) event detection Fixation concentration= narrowing of the visual field scanned by observer. Cognitive load on driver affects- driver eye’s movement and driver’s event detection a.azman, q.meng, e.edirisinghe

  10. DRIVER COGNITIVE MEASUREMENT available measurements which can be used to measure cognitive workload for drivers: Performance measure (primary tasks and secondary tasks)-primary is continuous(lane keeping), secondary is non-continuous(looking rare mirror) Physiological measurement- major organ, available for real time Rating scales- subjective measurers after activity is completed a.azman, q.meng, e.edirisinghe

  11. NORMAL FACE a.azman, q.meng, e.edirisinghe

  12. THINKING FACE Images from Google Image a.azman, q.meng, e.edirisinghe

  13. USED FEATURES Pupil diameter Eye movement-blinking, gaze direction, PERCLOS, saccade Head pose Heart rate a.azman, q.meng, e.edirisinghe

  14. PROPOSED FEATURES a.azman, q.meng, e.edirisinghe

  15. AUTOMATED PROCESS Data fusion and data mining, both is complementary processes that contribute automated process. The automated processes are involved with abductive-inductive (learning and discovery) and deductive (detection) process a.azman, q.meng, e.edirisinghe

  16. ALGORITHMS A few approaches have been used by recent researchers: Regression- statistical modelling AdaBoost- for feature selection SVM- popular technique BN- popular technique; DBN and SBN a.azman, q.meng, e.edirisinghe

  17. a.azman, q.meng, e.edirisinghe

  18. BAYESIAN NETWORK Is an attractive modelling tool for human sensing. It combines an intuitive graphical representation with efficient algorithms for inference and learning. BNs is a reasoning approach which provides a probabilistic approach to inference. A set of random variables make up the nodes of the network. Variables may be discrete or continuous. A set of directed links or arrows connect pairs of nodes. If there is an arrow from node X to node Y, X is said to be a parent of Y. Each node Xi has a conditional probability distribution P(Xi|Parents (Xi)) that quantifies the effect of the parents on the node. The graph has no directed cycles (and hence, is a directed, acyclic graph, DAG). a.azman, q.meng, e.edirisinghe

  19. DYNAMIC BAYESIAN NETWORK Attractive modelling for human sensing tool. Probabilistic graphical modelling to do inference and learning. Encode dependencies among variable in an evolving time. Can fuse variety of information with contexual information and expert knowledge. Examples: Kalmann Filter and HMMs. a.azman, q.meng, e.edirisinghe

  20. DYNAMIC BAYESIAN NETWORK DBN contains several time slices, where at every time slice, the nodes might give a different action as previous time slice. BNs for time series has the directed arcs and they should flow forward in time and not backward. sequence of observation {Y} by assuming that each observation depends on a discrete hidden state X=hidden state variable Y=observation variable a.azman, q.meng, e.edirisinghe

  21. STATIC BAYESIAN NETWORK a.azman, q.meng, e.edirisinghe

  22. DBN MODEL a.azman, q.meng, e.edirisinghe

  23. INITIAL EXPERIMENT a.azman, q.meng, e.edirisinghe

  24. EXPERIMENTAL SETUP SETUP1 With Audio Task- Lab Setup This experiment will be conducted with an audio playing to the subject. Subjects are required to listen to a recorded streaming radio on air. Listen to the song and at the same time watching the video on the screen. The experimenter will ask questions to the subjects. Questions are based on the recorded audio, recorded video and trigger questions (questions to cognitively distracting the subject)- auditory-based questions, visual-based questions, conversation-based questions, arithmetic-based questions. a.azman, q.meng, e.edirisinghe

  25. EXPERIMENTAL SETUP SETUP2 Real environment- The experiment will take place in a real car on a real road: Put the facelab cameras on the car’s dashboard (real car) A video of the driver driving the car also will be captured Questions will be asked to the driver. Use the same questions Are going to use lane change keeping test (LCT) This setup needs to consider the contextual information. a.azman, q.meng, e.edirisinghe

  26. PEARSON-R CORRELATION magnitude of the r-values showed the strength of the relationship between those two variables 0.0 to 0.3 = negligible correlation 0.3 to 0.5 = low correlation 0.5 to 0.7 = reasonable correlation 0.7 or more = good or strong correlation a.azman, q.meng, e.edirisinghe

  27. INITIAL RESULTS a.azman, q.meng, e.edirisinghe

  28. INITIAL RESULTS a.azman, q.meng, e.edirisinghe

  29. SCATTER PLOT a.azman, q.meng, e.edirisinghe

  30. THE END THANK YOU a.azman, q.meng, e.edirisinghe

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