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Vision based cognitive fatigue detection

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TitleInfo
Title
Vision based cognitive fatigue detection
Name (type = personal)
NamePart (type = family)
Kumar
NamePart (type = given)
Neelesh
NamePart (type = date)
1993-
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Neelesh Kumar
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author
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Michmizos
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Konstantinos
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Konstantinos Michmizos
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Advisory Committee
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chair
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Metaxas
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Dimitris
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Dimitris Metaxas
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Advisory Committee
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internal member
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Elgammal
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Ahmed
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Ahmed Elgammal
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Advisory Committee
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internal member
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Rutgers University
Role
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degree grantor
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Graduate School - New Brunswick
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Text
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theses
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2017
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2017-05
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2017
Place
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xx
Language
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eng
Abstract (type = abstract)
Analyzing human activity is a basic component of any system, be it biological or artificial, that aims to predict future behavior. Tracking and recognizing voluntary and involuntary action traits are basic endeavors for artificial vision systems that aim to predict cognitive fatigue, a major cause for road and workplace accidents. In this work, we developed a vision system to detect the early onset of fatigue. In collaboration with the Magnetoencephalography Lab at MIT, we collected synchronous brain (Magnetoencephalography - MEG) and behavioral (high-speed camera) data from 13 subjects in a 3-hour task that was designed to induce cognitive exhaustion. We derived a set of 8 eye-movement and 6 head-movement features and trained classifiers(Random Forest, K- Nearest Neighbor, and Support Vector Machine) for two classes (fatigue, non-fatigue) and three classes (fatigue, transition stage, non-fatigue). The models achieved average test accuracies of 98\%, 97\%, 92\% (two classes) and 92\%, 90\%, 87\% (three classes) respectively, for combined features. To further validate our models, we used the alpha band power in the MEG data as the neural indicator of fatigue. A regression analysis between the camera-based features and the alpha band power revealed an average $
ho^2$ = 0.59 coefficient. Here, we also propose a new method to detect the early stages of fatigue by using the classification error as our behavioral marker. Specifically, we found that the accuracy of the classifiers was higher when the distance between the time intervals of labels for “non-fatigue” and “fatigue” was larger; We estimated the total number of the mis-classified “fatigue” and “non-fatigue” data points in a sliding window: the “fatigue” (“non-fatigue”) number was high (low) in the beginning -non-fatigue stage- and became lower (higher) with time, signifying clear periods of fatigue and non-fatigue. We also observed a sharp change in the labels from non-fatigue to fatigue in the interval of 40-50 minutes, when using a sliding window, which signifies early stages of fatigue. Our results are promising in terms of designing a fully automated system that can predict one’s effective “operation range”, based on behavioral and neurophysiological cues.
Subject (authority = RUETD)
Topic
Computer Science
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Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_8071
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (x, 44 p. : ill.)
Note (type = degree)
M.S.
Note (type = bibliography)
Includes bibliographical references
Subject (authority = ETD-LCSH)
Topic
Computer vision
Note (type = statement of responsibility)
by Neelesh Kumar
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/T3TB19RZ
Genre (authority = ExL-Esploro)
ETD graduate
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Rights

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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Kumar
GivenName
Neelesh
Role
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RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2017-04-17 01:02:22
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Name
Neelesh Kumar
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject
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Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical

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2017-04-17T05:34:24
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2017-04-17T05:34:24
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