Sensor-Based Abnormal Human-Activity Detection

With the availability of affordable sensors and sensor networks, sensor-based human activity recognition has attracted much attention in artificial intelligence and ubiquitous computing. In this paper, we present a novel two-phase approach for detecting abnormal activities based on wireless sensors...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE transactions on knowledge and data engineering Ročník 20; číslo 8; s. 1082 - 1090
Hlavní autoři:	Jie Yin, Qiang Yang, Pan, J.J.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.08.2008 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Activity Recognition Artificial intelligence Communication system security Data Mining Data security Human motion body Humans Intelligent sensors Mathematical models Monitoring Networks Outlier Detection Regression Sensor Networks Sensors Studies Support vector machines Training Training data Ubiquitous computing Wireless sensor networks Activity Recognition Data Mining Outlier Detection Sensor Networks
ISSN:	1041-4347, 1558-2191
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	With the availability of affordable sensors and sensor networks, sensor-based human activity recognition has attracted much attention in artificial intelligence and ubiquitous computing. In this paper, we present a novel two-phase approach for detecting abnormal activities based on wireless sensors attached to a human body. Detecting abnormal activities is a particular important task in security monitoring and healthcare applications of sensor networks, among many others. Traditional approaches to this problem suffer from a high false positive rate, particularly when the collected sensor data are biased towards normal data while the abnormal events are rare. Therefore, there is a lack of training data for many traditional data mining methods to be applied. To solve this problem, our approach first employs a one-class support vector machine (SVM) that is trained on commonly available normal activities, which filters out the activities that have a very high probability of being normal. We then derive abnormal activity models from a general normal model via a kernel nonlinear regression (KNLR) to reduce false positive rate in an unsupervised manner. We show that our approach provides a good tradeoff between abnormality detection rate and false alarm rate, and allows abnormal activity models to be automatically derived without the need to explicitly label the abnormal training data, which are scarce. We demonstrate the effectiveness of our approach using real data collected from a sensor network that is deployed in a realistic setting.
AbstractList	With the availability of affordable sensors and sensor networks, sensor-based human activity recognition has attracted much attention in artificial intelligence and ubiquitous computing. In this paper, we present a novel two-phase approach for detecting abnormal activities based on wireless sensors attached to a human body. Detecting abnormal activities is a particular important task in security monitoring and healthcare applications of sensor networks, among many others. Traditional approaches to this problem suffer from a high false positive rate, particularly when the collected sensor data are biased towards normal data while the abnormal events are rare. Therefore, there is a lack of training data for many traditional data mining methods to be applied. To solve this problem, our approach first employs a one-class support vector machine (SVM) that is trained on commonly available normal activities, which filters out the activities that have a very high probability of being normal. We then derive abnormal activity models from a general normal model via a kernel nonlinear regression (KNLR) to reduce false positive rate in an unsupervised manner. We show that our approach provides a good tradeoff between abnormality detection rate and false alarm rate, and allows abnormal activity models to be automatically derived without the need to explicitly label the abnormal training data, which are scarce. We demonstrate the effectiveness of our approach using real data collected from a sensor network that is deployed in a realistic setting. Traditional approaches to this problem suffer from a high false positive rate, particularly when the collected sensor data are biased towards normal data while the abnormal events are rare. [...] there is a lack of training data for many traditional data mining methods to be applied.
Author	Pan, J.J. Jie Yin Qiang Yang
Author_xml	– sequence: 1 surname: Jie Yin fullname: Jie Yin organization: Inf. & Commun. Technol. (ICT) Centre, Commonwealth Sci. & Ind. Res. Organ., Hobart, TAS – sequence: 2 surname: Qiang Yang fullname: Qiang Yang organization: Inf. & Commun. Technol. (ICT) Centre, Commonwealth Sci. & Ind. Res. Organ., Hobart, TAS – sequence: 3 givenname: J.J. surname: Pan fullname: Pan, J.J.
BookMark	eNp9kD1PwzAQhi1UJNrCysJSMcCUcGc7Hx5LWyiiEgNlthznKqVKk2InSP33JCpiqATTvcPz3umeERtUdUWMXSOEiKAe1q_zRcgBkhBB8jM2xChKA44KB10GiYEUMrlgI--3AJAmKQ5Z-E6Vr13waDzlk2lW1W5nysmy3ZkqmNqm-Cqaw2RODXW5ri7Z-caUnq5-5ph9PC3Ws2Wwent-mU1XgRVSNQFyk0vLgUuITI4ZGsANJ0GbWFlUlBFkhjJjKY8hEyDjRMZc8jzhsUlyK8bs_rh37-rPlnyjd4W3VJamorr1Ok0iSGNMVUfe_UsKGSuMVdqBtyfgtm5d1X2hFXIBgkc9FB4h62rvHW303hU74w4aQfeWdW9Z95Z1b7kryJOCLRrTq2qcKcq_azfHWkFEvzekiLqPpPgG1VCJOg
CODEN	ITKEEH
CitedBy_id	crossref_primary_10_1109_TSMCC_2012_2215319 crossref_primary_10_3390_bioengineering11040345 crossref_primary_10_3390_s20144016 crossref_primary_10_3390_s131114918 crossref_primary_10_1016_j_pmcj_2016_07_004 crossref_primary_10_1016_j_robot_2019_02_005 crossref_primary_10_1016_j_eswa_2018_07_068 crossref_primary_10_1016_j_iot_2024_101068 crossref_primary_10_4316_AECE_2019_01005 crossref_primary_10_3390_s18061965 crossref_primary_10_3390_s91108422 crossref_primary_10_3390_computers5040022 crossref_primary_10_1016_j_bspc_2021_102406 crossref_primary_10_1007_s10916_016_0497_2 crossref_primary_10_1109_JIOT_2019_2946359 crossref_primary_10_1109_JPROC_2013_2262913 crossref_primary_10_1109_TKDE_2012_246 crossref_primary_10_1007_s11760_015_0756_6 crossref_primary_10_1080_20476965_2019_1710582 crossref_primary_10_1007_s10015_011_0872_5 crossref_primary_10_1007_s12083_016_0428_5 crossref_primary_10_1007_s42486_022_00108_3 crossref_primary_10_3390_rs11010055 crossref_primary_10_1007_s10844_014_0341_4 crossref_primary_10_1109_TASE_2015_2471842 crossref_primary_10_1016_j_future_2020_10_030 crossref_primary_10_1109_TSMCB_2012_2216873 crossref_primary_10_1007_s12065_020_00484_8 crossref_primary_10_1109_JSEN_2017_2707921 crossref_primary_10_1007_s10489_022_03897_3 crossref_primary_10_1016_j_iot_2019_01_002 crossref_primary_10_1587_transinf_E94_D_1153 crossref_primary_10_1016_j_im_2024_103922 crossref_primary_10_1109_JSEN_2021_3128046 crossref_primary_10_1002_smtd_202500379 crossref_primary_10_1109_JSEN_2013_2271721 crossref_primary_10_1155_2013_254629 crossref_primary_10_1007_s11042_015_2453_4 crossref_primary_10_32604_cmc_2021_018719 crossref_primary_10_1016_j_eswa_2010_07_120 crossref_primary_10_1109_ACCESS_2018_2846779 crossref_primary_10_1109_TASE_2015_2474743 crossref_primary_10_1109_TITB_2012_2226905 crossref_primary_10_1038_s41467_020_15086_2 crossref_primary_10_1016_j_engappai_2019_08_020 crossref_primary_10_3390_s16060822 crossref_primary_10_3390_s21165270 crossref_primary_10_1016_j_knosys_2021_106970 crossref_primary_10_1109_TKDE_2019_2905207 crossref_primary_10_1016_j_suscom_2020_100453 crossref_primary_10_1109_TPAMI_2018_2874455 crossref_primary_10_1016_j_neucom_2025_129576 crossref_primary_10_1080_1206212X_2024_2426501 crossref_primary_10_3233_JIFS_212088 crossref_primary_10_1109_JBHI_2020_3013403 crossref_primary_10_1109_TITS_2016_2617200 crossref_primary_10_1109_JIOT_2023_3285714 crossref_primary_10_1007_s11042_016_3267_8 crossref_primary_10_1109_ACCESS_2023_3264443 crossref_primary_10_1016_j_measurement_2022_111084 crossref_primary_10_1016_j_comnet_2010_05_003 crossref_primary_10_1016_j_jnca_2019_02_029 crossref_primary_10_1016_j_compbiomed_2019_103520 crossref_primary_10_3390_s20143811 crossref_primary_10_1016_j_dcan_2015_02_006 crossref_primary_10_1109_JTEHM_2015_2480082 crossref_primary_10_1016_j_measurement_2020_108050 crossref_primary_10_1007_s12559_020_09740_6 crossref_primary_10_1109_JBHI_2020_3007488 crossref_primary_10_1109_COMST_2019_2948204 crossref_primary_10_1177_1059712320930420 crossref_primary_10_3390_s23031275 crossref_primary_10_1038_s41467_020_19424_2 crossref_primary_10_1145_3038917 crossref_primary_10_1371_journal_pone_0168069 crossref_primary_10_1016_j_measen_2023_100925 crossref_primary_10_3390_app12031021 crossref_primary_10_1109_TCSVT_2021_3134410 crossref_primary_10_1007_s11227_021_03921_2 crossref_primary_10_1016_j_jvcir_2023_103901 crossref_primary_10_1109_SURV_2012_110112_00192 crossref_primary_10_1109_JIOT_2020_3019270 crossref_primary_10_3390_e17031358 crossref_primary_10_3390_robotics9030055 crossref_primary_10_1109_JSEN_2020_3004411 crossref_primary_10_1109_JSYST_2018_2876461 crossref_primary_10_3390_s16101713 crossref_primary_10_1016_j_robot_2013_05_010 crossref_primary_10_1155_2022_7931729 crossref_primary_10_1109_ACCESS_2020_2991731 crossref_primary_10_1109_TSMCC_2011_2178594 crossref_primary_10_1016_j_pmcj_2011_06_004 crossref_primary_10_1007_s41050_021_00028_8 crossref_primary_10_1016_j_jpdc_2017_11_018 crossref_primary_10_1109_ACCESS_2020_3011654 crossref_primary_10_1016_j_knosys_2015_09_024 crossref_primary_10_1109_TSMCC_2012_2198883 crossref_primary_10_1007_s12652_018_0855_7 crossref_primary_10_1007_s00779_011_0465_2 crossref_primary_10_1016_j_eswa_2024_124339 crossref_primary_10_1109_ACCESS_2019_2921912 crossref_primary_10_1155_2018_9032945 crossref_primary_10_1109_JIOT_2022_3211889 crossref_primary_10_1016_j_medengphy_2016_10_014 crossref_primary_10_1109_TKDE_2018_2806975 crossref_primary_10_3390_mi6081100 crossref_primary_10_1007_s00138_022_01291_0 crossref_primary_10_1007_s12652_014_0246_7 crossref_primary_10_3390_s19040907 crossref_primary_10_1016_j_asoc_2022_109363 crossref_primary_10_1007_s00521_022_07665_9 crossref_primary_10_1109_ACCESS_2017_2771389 crossref_primary_10_1016_j_eswa_2018_09_021 crossref_primary_10_1109_MPRV_2019_2913933 crossref_primary_10_1109_JIOT_2018_2863039 crossref_primary_10_1016_j_neucom_2015_09_064 crossref_primary_10_1186_s12938_024_01213_3 crossref_primary_10_3390_s21041276 crossref_primary_10_1109_TIFS_2023_3300094 crossref_primary_10_1109_TKDE_2010_148 crossref_primary_10_1016_j_pmcj_2014_02_003 crossref_primary_10_1109_JIOT_2020_3042502 crossref_primary_10_1007_s12652_020_02229_y crossref_primary_10_1016_j_jnca_2020_102738 crossref_primary_10_1109_TMC_2021_3064252 crossref_primary_10_1007_s12652_014_0219_x crossref_primary_10_1002_dac_4348 crossref_primary_10_1109_TMM_2016_2597007 crossref_primary_10_3390_bios11080284 crossref_primary_10_1109_TKDE_2013_33 crossref_primary_10_1007_s12652_012_0122_2 crossref_primary_10_1016_j_adhoc_2012_06_013 crossref_primary_10_1145_2934666 crossref_primary_10_1109_JIOT_2023_3243476 crossref_primary_10_1155_2014_283197 crossref_primary_10_3390_s22031016 crossref_primary_10_3390_sym9100212 crossref_primary_10_1587_transinf_2016DAP0014 crossref_primary_10_1145_3687125 crossref_primary_10_1109_TNSE_2019_2958892 crossref_primary_10_1016_j_neucom_2016_08_156 crossref_primary_10_1155_2014_839045 crossref_primary_10_1007_s10115_014_0765_8 crossref_primary_10_3390_app15137508 crossref_primary_10_1016_j_compenvurbsys_2017_09_012 crossref_primary_10_1109_JSEN_2019_2910881 crossref_primary_10_1016_j_neucom_2020_10_102 crossref_primary_10_1109_JIOT_2022_3207090 crossref_primary_10_1007_s00779_014_0820_1 crossref_primary_10_1007_s11063_018_9940_3 crossref_primary_10_1007_s11760_024_03535_0 crossref_primary_10_3390_technologies6040110 crossref_primary_10_3390_s20226670 crossref_primary_10_1016_j_bspc_2022_103963 crossref_primary_10_1016_j_eswa_2018_10_022 crossref_primary_10_1007_s10916_015_0239_x crossref_primary_10_1145_2633685 crossref_primary_10_1016_j_eswa_2012_09_004 crossref_primary_10_1109_TIM_2015_2477159 crossref_primary_10_3390_electronics10101152
Cites_doi	10.1016/j.artint.2007.01.006 10.1162/089976601750264965 10.1016/S0031-3203(96)00142-2 10.1109/IJCNN.2003.1223670 10.1109/TKDE.2006.84 10.1007/978-3-540-24646-6_2 10.1006/csla.1995.0010 10.1007/3-540-57868-4_79 10.1137/1.9781611972733.3 10.1007/978-3-540-39653-6_6 10.1016/S0167-8655(99)00087-2 10.1109/TKDE.2007.250584 10.1109/icassp.2006.1660191 10.1109/5.18626 10.1145/956750.956758 10.1109/CVPR.2005.61 10.1109/TKDE.2006.131 10.1145/312129.312220 10.1109/ICCV.2005.248 10.1109/MIS.2005.93 10.5555/1642194.1642224
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2008
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2008
DBID	97E RIA RIE AAYXX CITATION 7SC 7SP 8FD JQ2 L7M L~C L~D F28 FR3
DOI	10.1109/TKDE.2007.1042
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Xplore CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional ANTE: Abstracts in New Technology & Engineering Engineering Research Database
DatabaseTitle	CrossRef Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional Engineering Research Database ANTE: Abstracts in New Technology & Engineering
DatabaseTitleList	Technology Research Database Technology Research Database Technology Research Database
Database_xml	– sequence: 1 dbid: RIE name: IEEE Xplore url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering Computer Science
EISSN	1558-2191
EndPage	1090
ExternalDocumentID	2545292731 10_1109_TKDE_2007_1042 4358934
Genre	orig-research
GroupedDBID	-~X .DC 0R~ 1OL 29I 4.4 5GY 5VS 6IK 97E 9M8 AAJGR AARMG AASAJ AAWTH ABAZT ABFSI ABQJQ ABVLG ACGFO ACIWK AENEX AETIX AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 E.L EBS EJD F5P HZ~ H~9 ICLAB IEDLZ IFIPE IFJZH IPLJI JAVBF LAI M43 MS~ O9- OCL P2P PQQKQ RIA RIE RNI RNS RXW RZB TAE TAF TN5 UHB VH1 AAYXX CITATION 7SC 7SP 8FD JQ2 L7M L~C L~D RIG F28 FR3
ID	FETCH-LOGICAL-c349t-12ad4c202405ad1b1a01f2e3ef69c19ebe0baebaced60b3046746242d726a7dc3
IEDL.DBID	RIE
ISICitedReferencesCount	251
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000256894000009&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1041-4347
IngestDate	Sun Sep 28 09:01:48 EDT 2025 Sat Sep 27 16:47:47 EDT 2025 Mon Jun 30 04:22:49 EDT 2025 Tue Nov 18 22:27:34 EST 2025 Sat Nov 29 08:10:21 EST 2025 Wed Aug 27 02:52:18 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	8
Keywords	Activity Recognition Data Mining Outlier Detection Sensor Networks
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c349t-12ad4c202405ad1b1a01f2e3ef69c19ebe0baebaced60b3046746242d726a7dc3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23
PQID	912303258
PQPubID	23500
PageCount	9
ParticipantIDs	proquest_journals_912303258 proquest_miscellaneous_875086189 crossref_primary_10_1109_TKDE_2007_1042 proquest_miscellaneous_34691698 ieee_primary_4358934 crossref_citationtrail_10_1109_TKDE_2007_1042
PublicationCentury	2000
PublicationDate	2008-08-01
PublicationDateYYYYMMDD	2008-08-01
PublicationDate_xml	– month: 08 year: 2008 text: 2008-08-01 day: 01
PublicationDecade	2000
PublicationPlace	New York
PublicationPlace_xml	– name: New York
PublicationTitle	IEEE transactions on knowledge and data engineering
PublicationTitleAbbrev	TKDE
PublicationYear	2008
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	ref13 ref12 ref15 ref30 ref10 ref2 ref1 ref16 Pollack (ref21) 2005; 26 ref19 ref18 Chan (ref4) ref24 ref23 ref26 ref20 ref22 Ling (ref17) ref28 Kukar (ref11) ref27 Breunig (ref3) Fumera (ref8) Jarvis (ref9) ref29 Yin (ref31) ref7 Lester (ref14) ref6 ref5 Ting (ref25)
References_xml	– ident: ref15 doi: 10.1016/j.artint.2007.01.006 – ident: ref23 doi: 10.1162/089976601750264965 – volume-title: Proc. Workshop Machine Learning, Methods and Applications, held in the Context of the Eighth Meeting of the Italian Assoc. of Artificial Intelligence (AI*IA ’02) ident: ref8 article-title: Cost-Sensitive Learning in Support Vector Machines – ident: ref2 doi: 10.1016/S0031-3203(96)00142-2 – start-page: 858 volume-title: Proc. 19th Nat’l Conf. Artificial Intelligence (AAAI ’04) ident: ref9 article-title: Identifying Terrorist Activity with AI Plan Recognition Technology – ident: ref19 doi: 10.1109/IJCNN.2003.1223670 – ident: ref28 doi: 10.1109/TKDE.2006.84 – start-page: 766 volume-title: Proc. 19th Int’l Joint Conf. Articial Intelligence (IJCAI ’05) ident: ref14 article-title: A Hybrid Discriminative/Generative Approach for Modeling Human Activities – ident: ref18 doi: 10.1007/978-3-540-24646-6_2 – ident: ref13 doi: 10.1006/csla.1995.0010 – ident: ref10 doi: 10.1007/3-540-57868-4_79 – start-page: 445 volume-title: Proc. 13th European Conf. Artificial Intelligence (ECAI ’98) ident: ref11 article-title: Cost-Sensitive Learning with Neural Networks – ident: ref12 doi: 10.1137/1.9781611972733.3 – ident: ref20 doi: 10.1007/978-3-540-39653-6_6 – start-page: 578 volume-title: Proc. 19th Nat’l Conf. in Artificial Intelligence (AAAI ’04) ident: ref31 article-title: High-Level Goal Recognition in a Wireless LAN – start-page: 519 volume-title: Proc. 18th Int’l Joint Conf. Artificial Intelligence (IJCAI ’03) ident: ref17 article-title: AUC: A Statistically Consistent and More Discriminating Measure than Accuracy – ident: ref24 doi: 10.1016/S0167-8655(99)00087-2 – start-page: 983 volume-title: Proc. 17th Int’l Conf. Machine Learning (ICML ’00) ident: ref25 article-title: A Comparative Study of Cost-Sensitive Boosting Algorithms – volume: 26 start-page: 9 issue: 2 year: 2005 ident: ref21 article-title: Intelligent Technology for an Aging Population: The Use of AI to Assist Elders with Cognitive Impairment publication-title: AI Magazine – ident: ref29 doi: 10.1109/TKDE.2007.250584 – ident: ref26 doi: 10.1109/icassp.2006.1660191 – ident: ref22 doi: 10.1109/5.18626 – start-page: 164 volume-title: Proc. Fourth Int’l Conf. Knowledge Discovery and Data Mining (KDD ’98) ident: ref4 article-title: Toward Scalable Learning with Non-Uniform Class and Cost Distributions – ident: ref1 doi: 10.1145/956750.956758 – ident: ref6 doi: 10.1109/CVPR.2005.61 – ident: ref16 doi: 10.1109/TKDE.2006.131 – start-page: 93 volume-title: Proc. ACM SIGMOD Int’l Conf. Management of Data (SIGMOD ’00) ident: ref3 article-title: Identifying Density-Based Local Outliers – ident: ref5 doi: 10.1145/312129.312220 – ident: ref27 doi: 10.1109/ICCV.2005.248 – ident: ref30 doi: 10.1109/MIS.2005.93 – ident: ref7 doi: 10.5555/1642194.1642224
SSID	ssj0008781
Score	2.4527197
Snippet	With the availability of affordable sensors and sensor networks, sensor-based human activity recognition has attracted much attention in artificial... Traditional approaches to this problem suffer from a high false positive rate, particularly when the collected sensor data are biased towards normal data while...
SourceID	proquest crossref ieee
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	1082
SubjectTerms	Activity Recognition Artificial intelligence Communication system security Data Mining Data security Human motion body Humans Intelligent sensors Mathematical models Monitoring Networks Outlier Detection Regression Sensor Networks Sensors Studies Support vector machines Training Training data Ubiquitous computing Wireless sensor networks
Title	Sensor-Based Abnormal Human-Activity Detection
URI	https://ieeexplore.ieee.org/document/4358934 https://www.proquest.com/docview/912303258 https://www.proquest.com/docview/34691698 https://www.proquest.com/docview/875086189
Volume	20
WOSCitedRecordID	wos000256894000009&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
journalDatabaseRights	– providerCode: PRVIEE databaseName: IEEE Xplore customDbUrl: eissn: 1558-2191 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0008781 issn: 1041-4347 databaseCode: RIE dateStart: 19890101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LSwMxEB60eNCD1apYn3sQvJh2k003ybFqiyCIYAVvS5JNQZBd6cPf7yS7XRT14G0hAxsmmcx8ycx8ABcqcamSOScY2zPCuaNEyxgPQ6mZ1KG0ygayCfHwIF9e1OMaXDW1MM65kHzmev4zvOXnpV36q7I-unZ0r3wd1oVIq1qt5tSVIhCSIrpATJRwUTdopLHqT-5vR1WzQhxl3xxQYFT5cQwH3zJu_29WO7Bdx5DRsFr0XVhzRQfaK36GqDbXDmx9aTa4B70nRKzljFyj38qjoSl8tPoWhVt8MrQVi0R06xYhOavYh-fxaHJzR2q2BGITrhaEMp1zy3zPsoHOqaE6plPmEjdNlaUKFys22hltXZ7Gxj-ICu6LQ3LBUi1ymxxAqygLdwgRxzggNqkbTKniRg6kYVRzDB4MTRXGSF0gKxVmtm4l7hkt3rIAKWKVeZV7hkuReZV34bKRf6-aaPwpuecV3EjVuu3C8WqFstrG5plCpxsnbCC7cN6MonH4Fw9duHI5zxIE_zhllIj-kEC4hqCOSnX0-5-PYbPKEPEpfyfQWsyW7hQ27MfidT47C1vwEztL1wk
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8QwEB58gXrwLa7PHgQvxk3StE2O6wtFXQRX8FaSNAuCdGUf_n4nabco6sFbIQMNk0xmvmRmPoBjFbtUyUIQjO05EcIxoiXFw1BqLnUorbKBbCLrduXLi3qcgdOmFsY5F5LP3Jn_DG_5xcBO_FVZG107ulcxC_OJEJxW1VrNuSuzQEmK-AJRUSyyukUjo6rdu7u8qtoV4ij_5oICp8qPgzh4l-vV_81rDVbqKDLqVMu-DjOu3IDVKUNDVBvsBix_aTe4CWdPiFkHQ3KOnquIOqb08epbFO7xScdWPBLRpRuH9KxyC56vr3oXN6TmSyA2FmpMGNeFsNx3LUt0wQzTlPW5i10_VZYpXC5qtDPauiKlxj-JZsKXhxQZT3VW2Hgb5spB6XYgEhgJUJO6pM-UMDKRhjMtMHwwLFUYJbWATFWY27qZuOe0eMsDqKAq9yr3HJdZ7lXegpNG_r1qo_Gn5KZXcCNV67YFe9MVymsrG-UK3S6NeSJbcNSMonn4Nw9dusFklMcI_3HKKBH9IYGADWEdk2r39z8fweJN7-E-v7_t3u3BUpUv4hMA92FuPJy4A1iwH-PX0fAwbMdPkivaUA
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%3Ajournal&rft.genre=article&rft.atitle=Sensor-Based+Abnormal+Human-Activity+Detection&rft.jtitle=IEEE+transactions+on+knowledge+and+data+engineering&rft.au=Yin%2C+Jie&rft.au=Yang%2C+Qiang&rft.au=Pan%2C+J.J&rft.date=2008-08-01&rft.pub=The+Institute+of+Electrical+and+Electronics+Engineers%2C+Inc.+%28IEEE%29&rft.issn=1041-4347&rft.eissn=1558-2191&rft.volume=20&rft.issue=8&rft.spage=1082&rft_id=info:doi/10.1109%2FTKDE.2007.1042&rft.externalDBID=NO_FULL_TEXT&rft.externalDocID=2545292731
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1041-4347&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1041-4347&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1041-4347&client=summon