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Non-invasive Techniques for Human Fatigue Monitoring

Non-invasive Techniques for Human Fatigue Monitoring. Qiang Ji Dept. of Electrical, Computer, and Systems Engineering Rensselaer Polytechnic Institute qji@ecse.rpi.edu http://www.ecse.rpi.edu/homepages/qji. Visual Behaviors. Visual behaviors that typically reflect a

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Non-invasive Techniques for Human Fatigue Monitoring

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  1. Non-invasive Techniques for Human Fatigue Monitoring Qiang Ji Dept. of Electrical, Computer, and Systems Engineering Rensselaer Polytechnic Institute qji@ecse.rpi.edu http://www.ecse.rpi.edu/homepages/qji

  2. Visual Behaviors • Visual behaviors that typically reflect a • person's level of fatigue include • Eyelid movement • Head movement • Gaze • Facial expressions

  3. Eyelid Movements • Tracking Eyes • Develop techniques that can robustly track eyes under different face orientations, illuminations, and large head movements. • Compute Eye movement parameters • PERCLOS • Average Eye Closure/Open Speed (AECS)

  4. Eyes tracking demo

  5. PERCLOS measurement over time

  6. Average Eye Closure Speed Over time

  7. Gaze (Pupil Movements) • Real time gaze tracking • No calibration is needed and allows natural head movements !. • Gaze parameters • Spatial gaze distribution overtime • Ratio of fixation time to saccade time.

  8. Gaze distribution over time while alert

  9. Gaze distribution over time under fatigue

  10. Head Movement • Real time head pose tracking • Perform 3D face pose estimation from a single uncalibrated camera. • Head movement parameters • Head tilt frequency over time • Percentage of side views (PerSideV)

  11. Facial Expressions • Tracking facial features • Recognize certain facial expressions related to fatigue like yawning. • Building a database of fatigue expressions.

  12. Facial expression demo

  13. Fatigue Modeling • Knowledge of fatigue is uncertain and from different levels of abstraction. • Fatigue represents the affective state of an individual, is not observable, and can only be inferred.

  14. Overview of Our Approach Propose a probabilistic framework based on Bayesian Networks (BN) to • model fatigue. • systematically integrate various sources of information related to fatigue. • infer and predict fatigue from the available observations and the relevant contextual information.

  15. Bayesian Networks Construction • A BN model consists of target hypothesis variables (hidden nodes) and information variables (information nodes). • Fatigue is the target hypothesis variable that we intend to infer. • Other contextual factors and visual cues are the information nodes.

  16. Causes for Fatigue Major factors to cause fatigue include: • Sleep quality. • Circadian rhythm (time of day). • Physical conditions. • Working environment.

  17. Bayesian Network Model for Monitoring Human Fatigue

  18. Interface with Vision Module • An interface has been developed to connect the output of the computer vision system with the information fusion engine. • The interface instantiates the evidences of the fatigue network, which then performs fatigue inference and displays the fatigue index in real time.

  19. Conclusions • Developed non-intrusive real-time computer vision techniques to extract multiple fatigue parameters related to eyelid movements, gaze, head movement, and facial expressions. • Develop a probabilistic framework based on Bayesian networks to model and integrate contextual and visual cues information for fatigue monitoring.

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