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Using technology to build human
comforts and automation is growing rapidly possibly
because of dynamic nature of the world now,
particularly in most industrial setups. Hence, the
Human fatigue detection based on vision be said to be
the most promising commercial applications of
Human Computer Interface. Nevertheless, this area
has some limitations which include fast movement of
the head and eyes, external illuminations interference
and realistic lighting conditions. In this paper, a new
algorithm for detecting human fatigue by relying
primarily on eyelid movements as a facial feature is
proposed. The movement or velocity of the eyelid is
tracked using a Kalman filtered velocity function.
Unlike existing methods that employ similar
strategies for eye movement analysis, the new
algorithm developed, calculates a human blink cycle
for each individual, and estimates the associated
errors of the eye movement due to friction using the
Kalman filter. The proposed system which has been
employed on a number of diverse videos, averagely
has a 93.18% accuracy detection rate out of a total of
35000 image frames. The distinction in the proposed
method is due to its low error rate and quick
processing of input data. The paper has therefore
established human blink cycle calculation as a new
classifier to characterize human fatigue and the
calculation of the movement of eyelid using the
Kalman filter in determining the level of fatigue.Kalman Filter As A Threshold In Estimating The Level Of Fatigue Based On Eyel...
Kalman Filter As A Threshold In Estimating The Level Of Fatigue Based On Eyel...IJCSIS Research Publications
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Ähnlich wie Human cruisecontrol
Using technology to build human
comforts and automation is growing rapidly possibly
because of dynamic nature of the world now,
particularly in most industrial setups. Hence, the
Human fatigue detection based on vision be said to be
the most promising commercial applications of
Human Computer Interface. Nevertheless, this area
has some limitations which include fast movement of
the head and eyes, external illuminations interference
and realistic lighting conditions. In this paper, a new
algorithm for detecting human fatigue by relying
primarily on eyelid movements as a facial feature is
proposed. The movement or velocity of the eyelid is
tracked using a Kalman filtered velocity function.
Unlike existing methods that employ similar
strategies for eye movement analysis, the new
algorithm developed, calculates a human blink cycle
for each individual, and estimates the associated
errors of the eye movement due to friction using the
Kalman filter. The proposed system which has been
employed on a number of diverse videos, averagely
has a 93.18% accuracy detection rate out of a total of
35000 image frames. The distinction in the proposed
method is due to its low error rate and quick
processing of input data. The paper has therefore
established human blink cycle calculation as a new
classifier to characterize human fatigue and the
calculation of the movement of eyelid using the
Kalman filter in determining the level of fatigue.Kalman Filter As A Threshold In Estimating The Level Of Fatigue Based On Eyel...
Kalman Filter As A Threshold In Estimating The Level Of Fatigue Based On Eyel...IJCSIS Research Publications
Ähnlich wie Human cruisecontrol (7)
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Human cruisecontrol
- 2. The Idea „...to eliminate the distraction of having to constantly pay attention to your phone while finding your way.“ Max Pfeiffer, a coauthor of the study
- 3. By an electrically stimulation of the sartorius muscle the leg is moving in a particular direction. How does it work Video: https://www.youtube.com/ watch?v=JSfnm_HoUv4
- 4. Simple technologies with less disruptive navigation are already emerging, like the Apple Watch. Possibilities and Problems „If the researchers can figure out how to make the technology reliable enough and get people comfortable using it, it could also be helpful for exercise workouts or guiding emergency responders in situations where they can’t see well.“ Max Pfeiffer