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A load-cell based in-bed body motion detection and classification system

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TitleInfo
Title
A load-cell based in-bed body motion detection and classification system
Name (type = personal)
NamePart (type = family)
Alaziz
NamePart (type = given)
Musaab Adil
NamePart (type = date)
1980-
DisplayForm
Musaab Adil Alaziz
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Zhang
NamePart (type = given)
Yanyong
DisplayForm
Yanyong Zhang
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
School of Graduate Studies
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (qualifier = exact)
2017
DateOther (qualifier = exact); (type = degree)
2017-10
CopyrightDate (encoding = w3cdtf); (qualifier = exact)
2017
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
The basic necessity of sleep in our life is critically important to ensure our wellbeing. Sufficient sleep of good quality is highly desired in order to have enough energy to live. One of the main factors to measure sleep quality is the amount of body motion during sleep. In-bed motion detection is an important technique that can enable an array of applications, among which are sleep monitoring and abnormal movement detection. When detection is combined with classification, it can be used to detect, notify, and recognize specific events, enabling us to focus on critical tasks. In this study, we present a low-cost, low-overhead, and highly robust system for in-bed movement detection and classification that uses low-end load cells. By observing the forces sensed by the load cells, placed under each bed leg, we can detect many different types of movements, and further classify them as big or small depending on magnitude of the force changes on the load cells. We have designed three different features, which we refer to as Log-Peak, Energy-Peak, and ZeroX-Valley, that can effectively extract body movement signals from load cell data that is collected through wireless links in an energy-efficient manner. After establishing feature values, we employ a simple threshold-based algorithm to detect and classify movements. We have conducted a thorough evaluation, that involves collecting data from 30 subjects who perform 27 pre-defined movements in an experiment. By comparing our detection and classification results against the ground truth captured by a video camera, we show the Log-Peak strategy can detect these 27 types of movements at an error rate of 6.3% while classifying them as big or small movements at an error rate of 4.2%. In the second part of this dissertation, we set out to achieve much finer body motion classification. Towards this goal, we define 9 classes of movements, and design a machine learning algorithm using Support Vector Machine (SVM) and Random Forest techniques to classify a movement into one of these 9 classes. In this way, we can find out which body parts are involved in every movement. For every movement, we have extracted 24 features and used them in our model. This movement classification system was evaluated on data collected from 40 subjects who performed 35 predefined movements in each experiment. The accuracy of our model is not the same for all classes of movements. On average, it correctly classifies 90% of movements. This model can be used conveniently for long-term home monitoring. To improve the classification accuracy, we investigate more machine learning techniques. We use Random Forest and XGBoost as additional classification tools. We apply multiple tree topologies for each technique to reach their best results. After examining various combinations, we achieve the final classification accuracy of 91.5%. Lastly, another in-bed motion detection system is built. We use a geophone sensor to detect body motions in bed, which we call MotionPhone. MotionPhone is more accurate in detecting motion but not efficient for classification purposes. We thus believe combining these two systems can give us better results. Both systems are unobtrusive, low-cost, and private, which can thus enable a large array of important applications.
Subject (authority = RUETD)
Topic
Electrical and Computer Engineering
Subject (authority = ETD-LCSH)
Topic
Sleep
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_8217
PhysicalDescription
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electronic resource
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application/pdf
InternetMediaType
text/xml
Extent
1 online resource (xv, 100 p. : ill.)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Musaab Adil Alaziz
RelatedItem (type = host)
TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/T33X89QH
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Alaziz
GivenName
Musaab
MiddleName
Adil
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (point = start); (qualifier = exact)
2017-05-23 14:28:11
AssociatedEntity
Name
Musaab Alaziz
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
AssociatedObject
Type
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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.
RightsEvent
DateTime (encoding = w3cdtf); (point = start); (qualifier = exact)
2018-05-21
DateTime (encoding = w3cdtf); (point = end); (qualifier = exact)
2019-10-31
Type
Embargo
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after October 31, 2019.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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2017-05-23T02:44:56
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2017-05-23T02:44:56
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