Non-invasive Techniques for Human Fatigue Monitoring

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Non-invasive Techniques for Human Fatigue
Monitoring
Qiang Ji Dept. of Electrical, Computer, and
Systems Engineering Rensselaer Polytechnic
Institute qji_at_ecse.rpi.edu http//www.ecse.rpi.edu
/homepages/qji Funded by AFOSR and Honda
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Visual Behaviors

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

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Eye Detection and Tracking
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Eye Detection
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Eye Tracking

• Develop an eye tracking technique based on
  combining mean-shift and Kalman filtering
  tracking.
• It can robustly track eyes under different face
  orientations, illuminations, and large head
  movements.

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Eyelid Movements Characterization

• Eyelid movement parameters
• Percentage of Eye Closure (PERCLOS)
• Average Eye Closure/Open Speed (AECS)

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Gaze (Pupil Movements)

• Real time gaze tracking
• Develop a real time gaze tracking technqiue.
• No calibration is needed and allows natural head
  movements !.

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Gaze Estimation

• Gaze is determined by
• Pupil location (local gaze)
• Local gaze is characterized by relative positions
  between glint and pupil.
• Head orientation (global gaze)
• Head orientation is estimated by pupil shape,
  pupil position, pupil orientation, and pupil size.

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Gaze Parameters

• Gaze spatial distribution over time
• PERSAC-percentage of saccade eye movement over
  time

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Gaze distribution over time while alert
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Gaze distribution over time while fatigue
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Gaze distribution over time for inattentive
driving
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Plot of PERSAC parameter over 30 seconds.
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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 (TiltFreq)

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The flowchart of face pose tracking
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Examples Face Model Acquisition
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Head pitches (tilts) monitoring over time
(seconds)
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Facial Expressions

• Tracking facial features
• Recognize certain facial expressions related to
  fatigue like yawning and compute its frequency
  (YawnFreq)
• Building a database of fatigue expressions for
  training

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The plot of the openness of the mouth over time
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Facial expression demo
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Fatigue Modeling

• Observations of fatigue is uncertain, incomplete,
  dynamic, and from different from perspectives
• Fatigue represents the affective state of an
  individual, is not observable, and can only be
  inferred.

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Overview of Our Approach

• Propose a probabilistic framework based on the
  Dynamic Bayesian Networks (DBN) to
• systematically represent and integrate various
  sources of information related to fatigue over
  time.
• infer and predict fatigue from the available
  observations and the relevant contextual
  information.

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Bayesian Networks Construction

• A DBN 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.

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Causes for Fatigue

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

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Bayesian Fatigue Model
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Dynamic Fatigue Modeling
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Bayesian Fatigue Model Demo
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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.

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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 the
  Dynamic Bayesian networks to model and integrate
  contextual and visual cues information for
  fatigue detection over time.

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Effective Fatigue Monitoring

• The technology must be non-intrusive and in real
  time.
• It should simultaneously extract multiple
  parameters and systematically combine them over
  time in order to obtain a robust and consistent
  fatigue characterization.
• A fatigue model is needed that can represent
  uncertain and dynamic knowledge associated with
  fatigue and integrate them over time to infer
  and predict human fatigue.