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Human motion recognition using a wireless wearable system

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TypeOfResource
Text
TitleInfo (ID = T-1)
Title
Human motion recognition using a wireless wearable system
Identifier
ETD_2930
Identifier (type = hdl)
http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000056812
Language
LanguageTerm (authority = ISO639-2); (type = code)
eng
Genre (authority = marcgt)
theses
Subject (ID = SBJ-1); (authority = RUETD)
Topic
Electrical and Computer Engineering
Subject (ID = SBJ-2); (authority = ETD-LCSH)
Topic
Human activity recognition
Subject (ID = SBJ-3); (authority = ETD-LCSH)
Topic
Wearable computers
Subject (ID = SBJ-4); (authority = ETD-LCSH)
Topic
Gyroscopes
Subject (ID = SBJ-5); (authority = ETD-LCSH)
Topic
Support vector machines
Subject (ID = SBJ-6); (authority = ETD-LCSH)
Topic
Accelerometers
Abstract (type = abstract)
The future of human computer interaction systems lies in how intelligently these systems can take into account the user's context, that is, how well the data that they produce characterizes the user's current situation. Context awareness is essential for ubiquitous and wearable computing. Research on recognizing the daily activities of people has progressed steadily, but little focus has been devoted to recognizing activities along with the movements involved in it. For many applications such as rehabilitation, sports medicine, geriatric care, and health/fitness monitoring, the importance of combined recognition of activity and movements within an activity can drive health care outcomes. Motion recognition aims at recognizing the actions of one or more users from a series of observations on the users' actions and environmental conditions. Sensor-based motion recognition integrates the emerging area of wireless sensor networks with novel machine learning techniques to model a wide range of human motions. A novel algorithm is proposed that can be tuned to recognize on-the-fly either range of activities or fine motor movements within a specific activity using wirelessly connected sensor motes (equipped with accelerometers and gyroscopes) attached to different body sites. This thesis describes a novel algorithm for both situations and also presents a case study on optimal feature set from sensor values and various parameter values for the algorithm to detect the fine motor movements within an activity.
PhysicalDescription
Form (authority = gmd)
electronic resource
Extent
viii, 43 p. : ill.
InternetMediaType
application/pdf
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text/xml
Note (type = degree)
M.S.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by John Paul Varkey
Name (ID = NAME-1); (type = personal)
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Varkey
NamePart (type = given)
John Paul
NamePart (type = date)
1984-
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author
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John Paul Varkey
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Pompili
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Dario
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chair
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Advisory Committee
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Dario Pompili
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Gajic
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Zoran
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internal member
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Advisory Committee
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Zoran Gajic
Name (ID = NAME-4); (type = personal)
NamePart (type = family)
Marsic
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Ivan
Role
RoleTerm (authority = RULIB)
internal member
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Advisory Committee
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Ivan Marsic
Name (ID = NAME-1); (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (ID = NAME-2); (type = corporate)
NamePart
Graduate School - New Brunswick
Role
RoleTerm (authority = RULIB)
school
OriginInfo
DateCreated (qualifier = exact)
2010
DateOther (qualifier = exact); (type = degree)
2010-10
Place
PlaceTerm (type = code)
xx
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TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
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/T3611014
Genre (authority = ExL-Esploro)
ETD graduate
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Rights

RightsDeclaration (AUTHORITY = GS); (ID = rulibRdec0006)
The author owns the copyright to this work.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
RightsHolder (ID = PRH-1); (type = personal)
Name
FamilyName
Varkey
GivenName
John Paul
Role
Copyright Holder
RightsEvent (ID = RE-1); (AUTHORITY = rulib)
Type
Permission or license
DateTime
2010-09-28 18:23:15
AssociatedEntity (ID = AE-1); (AUTHORITY = rulib)
Role
Copyright holder
Name
John Paul Varkey
Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject (ID = AO-1); (AUTHORITY = rulib)
Type
License
Name
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.
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Technical

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ETD
MimeType (TYPE = file)
application/pdf
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application/x-tar
FileSize (UNIT = bytes)
686080
Checksum (METHOD = SHA1)
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