Personalization without user interruption boosting activity recognition in new subjects using unlabeled data

Activity recognition systems are widely used in monitoring physical and physiological conditions as well as observing the short/long term behavioral patterns for the purpose of improving the health and wellbeing of the users. The major obstacle in widespread use of these systems is the need for coll...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:2017 ACM IEEE 8th International Conference on Cyber Physical Systems (ICCPS) s. 293 - 302
Hlavní autori: Fallahzadeh, Ramin, Ghasemzadeh, Hassan
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: New York, NY, USA ACM 18.04.2017
Edícia:ACM Other Conferences
Predmet:
Activity recognition
Adaptation models
Classification algorithms
Computing methodologies > Machine learning
Computing methodologies > Machine learning > Learning paradigms > Multi-task learning > Transfer learning
cross-subject boosting
Machine learning algorithms
Prediction algorithms
Training
uninformed transfer learning
cross-subject boosting
uninformed transfer learning
activity recognition
ISBN:9781450349659, 145034965X
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Abstract Activity recognition systems are widely used in monitoring physical and physiological conditions as well as observing the short/long term behavioral patterns for the purpose of improving the health and wellbeing of the users. The major obstacle in widespread use of these systems is the need for collecting labeled data to train the activity recognition model. While a personalized model outperforms a user-independent model, collecting labels from every single user is burdensome and in some cases impractical. In this paper, we propose an uninformed cross-subject transfer learning algorithm that leverages the cross-user similarities by constructing a network-based feature-level representation of the data in source and target users and perform a best effort community detection to extract the core observations in target data. Our algorithm uses a heuristic classifier-based mapping to assign activity labels to the core observations. Finally, the output of labeling is conditionally fused with the prediction of the source-model to develop a boosted and personalized activity recognition algorithm. Our analysis on real-world data demonstrates the superiority of our approach. Our algorithm achieves over 87% accuracy on average which is 7% higher than the state-of-the art approach.
AbstractList Activity recognition systems are widely used in monitoring physical and physiological conditions as well as observing the short/long term behavioral patterns for the purpose of improving the health and wellbeing of the users. The major obstacle in widespread use of these systems is the need for collecting labeled data to train the activity recognition model. While a personalized model outperforms a user-independent model, collecting labels from every single user is burdensome and in some cases impractical. In this paper, we propose an uninformed cross-subject transfer learning algorithm that leverages the cross-user similarities by constructing a network-based feature-level representation of the data in source and target users and perform a best effort community detection to extract the core observations in target data. Our algorithm uses a heuristic classifier-based mapping to assign activity labels to the core observations. Finally, the output of labeling is conditionally fused with the prediction of the source-model to develop a boosted and personalized activity recognition algorithm. Our analysis on real-world data demonstrates the superiority of our approach. Our algorithm achieves over 87% accuracy on average which is 7% higher than the state-of-the art approach.
Author Ghasemzadeh, Hassan
Fallahzadeh, Ramin
Author_xml – sequence: 1
  givenname: Ramin
  surname: Fallahzadeh
  fullname: Fallahzadeh, Ramin
  email: rfallahz@eecs.wsu.edu
  organization: Washington State University
– sequence: 2
  givenname: Hassan
  surname: Ghasemzadeh
  fullname: Ghasemzadeh, Hassan
  email: hassan@eecs.wsu.edu
  organization: Washington State University
BookMark eNqNkD1PwzAQho0ACSiZGVgywtBw_o5HVPElVYIBZssxF2Fo48pOhODXk9JMTEyvTs-rO91zQg662CEhZxQqSoW84iAlgKh-k8o9UhhdjwC4MEqa_T_zESlyfgcAakTNOT8ml0-YcuzcKny7PsSu_Az9Wxz6csiYytD1mNKw2ZJTcti6VcZiyhl5ub15XtzPl493D4vr5dwxofs5qvFgralqhGdees-wFUo5YFIwJrw06CUwhTU4baj28AqNNw0HgS2jns_I-W5vQES7SWHt0pfVZnyC1iO92FHn17aJ8SNbCnbrwk4u7ORirFb_rNomBWz5D0Y_XLA
CODEN IEEPAD
ContentType Conference Proceeding
Copyright 2017 ACM
Copyright_xml – notice: 2017 ACM
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1145/3055004.3055015
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Xplore POP ALL
IEEE Xplore All Conference Proceedings
IEEE Electronic Library (IEL)
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList

Database_xml – sequence: 1
  dbid: RIE
  name: IEEE/IET Electronic Library
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
EISBN 9781450349659
145034965X
EndPage 302
ExternalDocumentID 7945018
Genre orig-research
GroupedDBID 6IE
6IF
6IK
6IL
6IN
AAJGR
ABLEC
ACM
ADPZR
ALMA_UNASSIGNED_HOLDINGS
APO
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
CBEJK
GUFHI
IEGSK
OCL
RIB
RIC
RIE
RIL
AAWTH
LHSKQ
ID FETCH-LOGICAL-a247t-e67818716b4c2c5cc2ef466a0254224c59ec5026e80a7917c0d0bc9b304ef21c3
IEDL.DBID RIE
ISBN 9781450349659
145034965X
ISICitedReferencesCount 42
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000424191900032&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
IngestDate Wed Aug 27 02:52:21 EDT 2025
Wed Jan 31 06:43:20 EST 2024
IsPeerReviewed false
IsScholarly false
Keywords cross-subject boosting
uninformed transfer learning
activity recognition
Language English
License Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org
LinkModel DirectLink
MeetingName ICCPS '17: ACM/IEEE 8th International Conference on Cyber-Physical Systems
MergedId FETCHMERGED-LOGICAL-a247t-e67818716b4c2c5cc2ef466a0254224c59ec5026e80a7917c0d0bc9b304ef21c3
PageCount 10
ParticipantIDs ieee_primary_7945018
acm_books_10_1145_3055004_3055015_brief
acm_books_10_1145_3055004_3055015
PublicationCentury 2000
PublicationDate 20170418
2017-April
PublicationDateYYYYMMDD 2017-04-18
2017-04-01
PublicationDate_xml – month: 04
  year: 2017
  text: 20170418
  day: 18
PublicationDecade 2010
PublicationPlace New York, NY, USA
PublicationPlace_xml – name: New York, NY, USA
PublicationSeriesTitle ACM Other Conferences
PublicationTitle 2017 ACM IEEE 8th International Conference on Cyber Physical Systems (ICCPS)
PublicationTitleAbbrev ICCPS
PublicationYear 2017
Publisher ACM
Publisher_xml – name: ACM
SSID ssj0001948333
Score 1.8873595
Snippet Activity recognition systems are widely used in monitoring physical and physiological conditions as well as observing the short/long term behavioral patterns...
SourceID ieee
acm
SourceType Publisher
StartPage 293
SubjectTerms Activity recognition
Adaptation models
Classification algorithms
Computing methodologies -- Machine learning
Computing methodologies -- Machine learning -- Learning paradigms -- Multi-task learning -- Transfer learning
cross-subject boosting
Machine learning algorithms
Prediction algorithms
Training
uninformed transfer learning
Subtitle boosting activity recognition in new subjects using unlabeled data
Title Personalization without user interruption
URI https://ieeexplore.ieee.org/document/7945018
WOSCitedRecordID wos000424191900032&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8NAEB7a4kEvPlqxvlhB8GLaJLt5rDdfxYOUIhZ6C5vNBnpoInn4-53dpK2CIF6SsCQEdob9dmbn-wbgOk1xW8GEbTEe-nihnhUmrmMJN0b44SyQiRFxfQ2m03Cx4LMO3G64MEopU3ymRvrRnOUnuax1qmyMvqP157rQDYKg4Wpt8ymchZTSVr3HYd5Ya1mhC4zMXbe97Qq5-tFExWDIZP9_fz-AwZaMR2YbmDmEjsqOYO-bjmAfVrP1lrohVRKdXc3riszRwYhJ-hW1WRvuyEOel7rUmdzLpnEEeVvXEOGHy4zgskdwOdH5mZKYigIyz9BXEJ8S8iQqMYD55Pn98cVq-yjgjLOgshQCkqMDo5hJV3pSuiplvi80ER4RXHpcSQ9jMRXaIsDwTdqJHUseU5up1HUkPYZelmfqBIgIEs9JHZ7SWKKFHZ4IGsqUO66Po7Y3hCuc5EgHCGXUcJ69qDVE1BpiCDd_vhPFxVKlQ-hrM0QfjfBG1Frg9PfhM9h1Neaasppz6FVFrS5gR35Wy7K4NN7yBQ7EvC4
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dS8MwED-2KagvfkxxfkYQfLGuaZO18c2voTjHkA18K2mawh7Wytb593tJ66YgiC9tCS2F3JFf7nK_3wGcpyluK5h0HSbCDl587oSJRx3pxQg_ggUqsSKuvaDfD9_exKAGlwsujNbaFp_pK_Noz_KTXM1NqqyNvmP05-qwwhnzaMnWWmZUBAt936_0eyjjbaNmhU5wZe-m8W1dqsmPNioWRbqb__v_Fuwu6XhksACabajpbAc2vikJNmEy-NpUl7RKYvKr-bwgI3QxYtN-07ldHa7JbZ7PTLEzuVFl6wjy-lVFhB-OM4ILH8EFxWRoZsTWFJBRht6CCJWQe1nIXRh1H4Z3j07VSQHnnAWFoxGSqAmNYqY8xZXydMo6HWmo8IjhigutOEZjOnRlgAGcchM3ViL2XaZTjyp_DxpZnul9IDJIOE2pSP1YoY2pSKQfqlRQr4OjLm_BGU5yZEKEWVSynnlUGSKqDNGCiz_fieLpWKctaBozRO-l9EZUWeDg9-FTWHscvvSi3lP_-RDWPYPAtsjmCBrFdK6PYVV9FOPZ9MR6zicE9L91
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2017+ACM+IEEE+8th+International+Conference+on+Cyber+Physical+Systems+%28ICCPS%29&rft.atitle=Personalization+without+User+Interruption%3A+Boosting+Activity+Recognition+in+New+Subjects+Using+Unlabeled+Data&rft.au=Fallahzadeh%2C+Ramin&rft.au=Ghasemzadeh%2C+Hassan&rft.date=2017-04-01&rft.pub=ACM&rft.spage=293&rft.epage=302&rft_id=info:doi/10.1145%2F3055004.3055015&rft.externalDocID=7945018
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9781450349659/lc.gif&client=summon&freeimage=true
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9781450349659/mc.gif&client=summon&freeimage=true
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9781450349659/sc.gif&client=summon&freeimage=true