Examining sensor-based physical activity recognition and monitoring for healthcare using Internet of Things: A systematic review

•It summarises the state-of-the-art in traditional PARM methodologies for healthcare.•It identifies new research trends and challenges of PARM studies in IoT environments.•It considers successful case studies in the area and look at the possible future industrial applications in smart healthcare. Du...

Full description

Saved in:
Bibliographic Details
Published inJournal of biomedical informatics Vol. 87; pp. 138 - 153
Main Authors Qi, Jun, Yang, Po, Waraich, Atif, Deng, Zhikun, Zhao, Youbing, Yang, Yun
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 01.11.2018
Subjects
Internet of Things
Physical activity monitoring
Physical activity recognition
Sensor-based
Systematic review
Systematic review
Internet of Things
Physical activity recognition
Sensor-based
Physical activity monitoring
Online AccessGet full text
ISSN1532-0464
1532-0480
1532-0480
DOI10.1016/j.jbi.2018.09.002

Cover

Abstract •It summarises the state-of-the-art in traditional PARM methodologies for healthcare.•It identifies new research trends and challenges of PARM studies in IoT environments.•It considers successful case studies in the area and look at the possible future industrial applications in smart healthcare. Due to importantly beneficial effects on physical and mental health and strong association with many rehabilitation programs, Physical Activity Recognition and Monitoring (PARM) have been considered as a key paradigm for smart healthcare. Traditional methods for PARM focus on controlled environments with the aim of increasing the types of identifiable activity subjects complete and improving recognition accuracy and system robustness by means of novel body-worn sensors or advanced learning algorithms. The emergence of the Internet of Things (IoT) enabling technology is transferring PARM studies to open and connected uncontrolled environments by connecting heterogeneous cost-effective wearable devices and mobile apps. Little is currently known about whether traditional PARM technologies can tackle the new challenges of IoT environments and how to effectively harness and improve these technologies. In an effort to understand the use of IoT technologies in PARM studies, this paper will give a systematic review, critically examining PARM studies from a typical IoT layer-based perspective. It will firstly summarize the state-of-the-art in traditional PARM methodologies as used in the healthcare domain, including sensory, feature extraction and recognition techniques. The paper goes on to identify some new research trends and challenges of PARM studies in the IoT environments, and discusses some key enabling techniques for tackling them. Finally, this paper consider some of the successful case studies in the area and look at the possible future industrial applications of PARM in smart healthcare.
AbstractList Due to importantly beneficial effects on physical and mental health and strong association with many rehabilitation programs, Physical Activity Recognition and Monitoring (PARM) have been considered as a key paradigm for smart healthcare. Traditional methods for PARM focus on controlled environments with the aim of increasing the types of identifiable activity subjects complete and improving recognition accuracy and system robustness by means of novel body-worn sensors or advanced learning algorithms. The emergence of the Internet of Things (IoT) enabling technology is transferring PARM studies to open and connected uncontrolled environments by connecting heterogeneous cost-effective wearable devices and mobile apps. Little is currently known about whether traditional PARM technologies can tackle the new challenges of IoT environments and how to effectively harness and improve these technologies. In an effort to understand the use of IoT technologies in PARM studies, this paper will give a systematic review, critically examining PARM studies from a typical IoT layer-based perspective. It will firstly summarize the state-of-the-art in traditional PARM methodologies as used in the healthcare domain, including sensory, feature extraction and recognition techniques. The paper goes on to identify some new research trends and challenges of PARM studies in the IoT environments, and discusses some key enabling techniques for tackling them. Finally, this paper consider some of the successful case studies in the area and look at the possible future industrial applications of PARM in smart healthcare.
Due to importantly beneficial effects on physical and mental health and strong association with many rehabilitation programs, Physical Activity Recognition and Monitoring (PARM) have been considered as a key paradigm for smart healthcare. Traditional methods for PARM focus on controlled environments with the aim of increasing the types of identifiable activity subjects complete and improving recognition accuracy and system robustness by means of novel body-worn sensors or advanced learning algorithms. The emergence of the Internet of Things (IoT) enabling technology is transferring PARM studies to open and connected uncontrolled environments by connecting heterogeneous cost-effective wearable devices and mobile apps. Little is currently known about whether traditional PARM technologies can tackle the new challenges of IoT environments and how to effectively harness and improve these technologies. In an effort to understand the use of IoT technologies in PARM studies, this paper will give a systematic review, critically examining PARM studies from a typical IoT layer-based perspective. It will firstly summarize the state-of-the-art in traditional PARM methodologies as used in the healthcare domain, including sensory, feature extraction and recognition techniques. The paper goes on to identify some new research trends and challenges of PARM studies in the IoT environments, and discusses some key enabling techniques for tackling them. Finally, this paper consider some of the successful case studies in the area and look at the possible future industrial applications of PARM in smart healthcare.Due to importantly beneficial effects on physical and mental health and strong association with many rehabilitation programs, Physical Activity Recognition and Monitoring (PARM) have been considered as a key paradigm for smart healthcare. Traditional methods for PARM focus on controlled environments with the aim of increasing the types of identifiable activity subjects complete and improving recognition accuracy and system robustness by means of novel body-worn sensors or advanced learning algorithms. The emergence of the Internet of Things (IoT) enabling technology is transferring PARM studies to open and connected uncontrolled environments by connecting heterogeneous cost-effective wearable devices and mobile apps. Little is currently known about whether traditional PARM technologies can tackle the new challenges of IoT environments and how to effectively harness and improve these technologies. In an effort to understand the use of IoT technologies in PARM studies, this paper will give a systematic review, critically examining PARM studies from a typical IoT layer-based perspective. It will firstly summarize the state-of-the-art in traditional PARM methodologies as used in the healthcare domain, including sensory, feature extraction and recognition techniques. The paper goes on to identify some new research trends and challenges of PARM studies in the IoT environments, and discusses some key enabling techniques for tackling them. Finally, this paper consider some of the successful case studies in the area and look at the possible future industrial applications of PARM in smart healthcare.
•It summarises the state-of-the-art in traditional PARM methodologies for healthcare.•It identifies new research trends and challenges of PARM studies in IoT environments.•It considers successful case studies in the area and look at the possible future industrial applications in smart healthcare. Due to importantly beneficial effects on physical and mental health and strong association with many rehabilitation programs, Physical Activity Recognition and Monitoring (PARM) have been considered as a key paradigm for smart healthcare. Traditional methods for PARM focus on controlled environments with the aim of increasing the types of identifiable activity subjects complete and improving recognition accuracy and system robustness by means of novel body-worn sensors or advanced learning algorithms. The emergence of the Internet of Things (IoT) enabling technology is transferring PARM studies to open and connected uncontrolled environments by connecting heterogeneous cost-effective wearable devices and mobile apps. Little is currently known about whether traditional PARM technologies can tackle the new challenges of IoT environments and how to effectively harness and improve these technologies. In an effort to understand the use of IoT technologies in PARM studies, this paper will give a systematic review, critically examining PARM studies from a typical IoT layer-based perspective. It will firstly summarize the state-of-the-art in traditional PARM methodologies as used in the healthcare domain, including sensory, feature extraction and recognition techniques. The paper goes on to identify some new research trends and challenges of PARM studies in the IoT environments, and discusses some key enabling techniques for tackling them. Finally, this paper consider some of the successful case studies in the area and look at the possible future industrial applications of PARM in smart healthcare.
Author Zhao, Youbing
Yang, Yun
Deng, Zhikun
Qi, Jun
Yang, Po
Waraich, Atif
Author_xml – sequence: 1
  givenname: Jun
  surname: Qi
  fullname: Qi, Jun
  email: j.qi@2015.ljmu.ac.uk
  organization: School of Software, Yunnan University, Kunming, China
– sequence: 2
  givenname: Po
  orcidid: 0000-0002-8553-7127
  surname: Yang
  fullname: Yang, Po
  email: p.yang@ljum.ac.uk
  organization: School of Software, Yunnan University, Kunming, China
– sequence: 3
  givenname: Atif
  surname: Waraich
  fullname: Waraich, Atif
  organization: Department of Computer Science, Liverpool John Moores University, Liverpool L3 3AF, UK
– sequence: 4
  givenname: Zhikun
  surname: Deng
  fullname: Deng, Zhikun
  organization: Department of Computer Science, University of Bedfordshire, Luton LU1 3JU, UK
– sequence: 5
  givenname: Youbing
  surname: Zhao
  fullname: Zhao, Youbing
  organization: Department of Computer Science, University of Bedfordshire, Luton LU1 3JU, UK
– sequence: 6
  givenname: Yun
  surname: Yang
  fullname: Yang, Yun
  organization: School of Software, Yunnan University, Kunming, China
BackLink https://www.ncbi.nlm.nih.gov/pubmed/30267895$$D View this record in MEDLINE/PubMed
BookMark eNp9kTtvFDEUhS0URB7wA2iQS5oZbI_tmYEqigJEikQTasuPO1mvZuzF9iZsx0_Ho00oKFLdh853inPO0UmIARB6T0lLCZWftu3W-JYROrRkbAlhr9AZFR1rCB_Iyb9d8lN0nvOWEEqFkG_QaUeY7IdRnKE_17_14oMP9zhDyDE1RmdweLc5ZG_1jLUt_sGXA05g433wxceAdXB4ifWIaSWnmPAG9Fw2VifA-7w-b0KBFKDgOOG7Tf3kz_gS50MusOjibTV88PD4Fr2e9Jzh3dO8QD-_Xt9dfW9uf3y7ubq8bSyXrDSCT73oeD9QJyRjnQPhuJm4M1yA6JwcRyGtZcClG3pjnJscIZOxojeOD7a7QB-PvrsUf-0hF7X4bGGedYC4z4pRyqtJT_sq_fAk3ZsFnNolv-h0UM-pVUF_FNgUc04wKeuLXpMpSftZUaLWftRW1X7U2o8io6r9VJL-Rz6bv8R8OTJQ46mRJZWth2DB-dpJUS76F-i_FsWq4g
CitedBy_id crossref_primary_10_1109_ACCESS_2019_2891668
crossref_primary_10_1108_SR_02_2024_0165
crossref_primary_10_1109_TII_2022_3142315
crossref_primary_10_1155_2021_4028761
crossref_primary_10_3390_s23167095
crossref_primary_10_1155_2022_6389069
crossref_primary_10_3390_s21155006
crossref_primary_10_2196_33890
crossref_primary_10_1038_s41597_024_03254_8
crossref_primary_10_3389_fpubh_2023_1185702
crossref_primary_10_3390_s21113842
crossref_primary_10_1109_ACCESS_2020_3024003
crossref_primary_10_1016_j_engappai_2025_110465
crossref_primary_10_1016_j_technovation_2022_102558
crossref_primary_10_1109_OJCOMS_2024_3484002
crossref_primary_10_1038_s41598_024_56123_0
crossref_primary_10_1080_10447318_2024_2313282
crossref_primary_10_1108_IJIUS_01_2019_0005
crossref_primary_10_3390_app142310835
crossref_primary_10_1109_JSEN_2020_3000772
crossref_primary_10_1109_TBME_2024_3418688
crossref_primary_10_1016_j_inffus_2019_09_002
crossref_primary_10_3390_diagnostics13111861
crossref_primary_10_1016_j_jbi_2023_104483
crossref_primary_10_2196_37229
crossref_primary_10_3390_s21248297
crossref_primary_10_1109_TCSS_2019_2916086
crossref_primary_10_11834_jig_230020
crossref_primary_10_1016_j_nantod_2020_101016
crossref_primary_10_1007_s42979_024_03129_0
crossref_primary_10_1016_j_ins_2021_10_049
crossref_primary_10_1007_s40747_022_00767_w
crossref_primary_10_1038_s41467_021_23020_3
crossref_primary_10_1109_ACCESS_2023_3301872
crossref_primary_10_3390_healthcare9101329
crossref_primary_10_1109_JSEN_2024_3404777
crossref_primary_10_3390_app132212135
crossref_primary_10_3390_s21196401
crossref_primary_10_3390_s22228974
crossref_primary_10_1155_2021_6632599
crossref_primary_10_31590_ejosat_868000
crossref_primary_10_1007_s42835_022_01258_1
crossref_primary_10_1007_s11036_021_01834_1
crossref_primary_10_1007_s10586_024_04653_7
crossref_primary_10_2196_38536
crossref_primary_10_1007_s40860_021_00147_0
crossref_primary_10_1007_s43926_024_00062_9
crossref_primary_10_3390_mi15040479
crossref_primary_10_1016_j_measurement_2020_107480
crossref_primary_10_1016_j_procs_2021_09_253
crossref_primary_10_2147_CIA_S329668
crossref_primary_10_1109_ACCESS_2022_3159235
crossref_primary_10_2196_54049
crossref_primary_10_3390_s22176524
crossref_primary_10_4103_abr_abr_197_22
crossref_primary_10_1109_JSEN_2022_3161797
crossref_primary_10_1002_advs_202103694
crossref_primary_10_1016_j_patcog_2020_107561
crossref_primary_10_1016_j_sintl_2021_100122
crossref_primary_10_1007_s11042_022_12349_6
crossref_primary_10_51483_IJAIML_4_1_2024_94_121
crossref_primary_10_1002_ett_4258
crossref_primary_10_1016_j_iot_2025_101548
crossref_primary_10_3389_fpubh_2021_756675
crossref_primary_10_1016_j_cobme_2021_100291
crossref_primary_10_1109_ACCESS_2020_2989128
crossref_primary_10_3389_fphys_2023_1094946
crossref_primary_10_1002_dac_4678
crossref_primary_10_1109_JSEN_2021_3108011
crossref_primary_10_1109_TII_2020_3022432
crossref_primary_10_1016_j_techfore_2022_122001
crossref_primary_10_1177_00405175241253867
crossref_primary_10_1016_j_jnca_2021_103164
crossref_primary_10_1109_ACCESS_2022_3183228
crossref_primary_10_1108_SR_02_2024_0080
crossref_primary_10_1021_acsami_1c22001
crossref_primary_10_3390_make6020040
crossref_primary_10_1109_JSEN_2022_3141064
crossref_primary_10_1002_cpe_7327
crossref_primary_10_1016_j_engappai_2023_106543
crossref_primary_10_20517_ss_2024_68
crossref_primary_10_1186_s13102_025_01063_z
crossref_primary_10_1109_JIOT_2021_3135200
crossref_primary_10_1109_TNSRE_2020_3005616
crossref_primary_10_1080_1091367X_2018_1560297
crossref_primary_10_1080_09537287_2019_1702226
crossref_primary_10_1007_s12652_020_02548_0
crossref_primary_10_1109_ACCESS_2022_3176606
crossref_primary_10_1111_coin_12352
crossref_primary_10_1007_s10470_025_02350_y
crossref_primary_10_3390_s21144767
crossref_primary_10_1016_j_smhl_2019_100100
crossref_primary_10_1109_JSEN_2021_3058429
crossref_primary_10_1145_3688855
crossref_primary_10_1155_2022_3463274
crossref_primary_10_1186_s13063_020_04295_1
crossref_primary_10_1109_TCSS_2019_2918285
crossref_primary_10_1155_2021_6611366
crossref_primary_10_3389_frobt_2021_610653
crossref_primary_10_1142_S1793351X22500039
crossref_primary_10_1016_j_artmed_2022_102454
crossref_primary_10_3390_healthcare10101940
crossref_primary_10_1002_adma_202100047
crossref_primary_10_1109_TCSS_2019_2919097
crossref_primary_10_1002_ett_3994
Cites_doi 10.1109/ISWC.2011.24
10.1109/AINAW.2007.90
10.1109/ICMLA.2009.112
10.1152/japplphysiol.00465.2009
10.1088/0967-3334/35/7/1245
10.1109/TNSRE.2010.2070807
10.1109/ISWC.2007.4373774
10.1109/JBHI.2013.2282617
10.1109/RTCSA.2007.17
10.1109/TBME.2011.2160723
10.1109/TBME.2003.812189
10.1109/MPRV.2008.39
10.1145/1409635.1409637
10.1109/UIC-ATC.2010.26
10.1109/ICME.2005.1521728
10.1007/978-3-642-02830-4_4
10.1007/978-3-319-13105-4_14
10.1016/j.eswa.2012.09.004
10.4108/bodynets.2007.114
10.1109/TNSRE.2002.802879
10.1016/j.medengphy.2011.05.002
10.1016/j.neucom.2015.07.085
10.1109/ICME.2010.5583013
10.1109/TNSRE.2007.916282
10.1109/CVPR.2005.61
10.3390/s140610146
10.1016/j.patrec.2008.08.002
10.1016/j.eswa.2014.04.037
10.1109/TPAMI.2006.197
10.1145/2742647.2742674
10.1016/j.patcog.2007.11.016
10.1145/2702123.2702178
10.1007/s12652-011-0064-0
10.1007/s13042-010-0001-0
10.1145/2702123.2702200
10.1109/ICCAS.2007.4406764
10.3390/s131013099
10.1145/1964897.1964918
10.1016/j.maturitas.2011.03.016
10.1109/TITB.2009.2037752
10.1109/TITB.2005.856864
10.1016/j.artint.2007.01.006
10.1109/ISWC.2006.286336
10.1002/anr.1780321108
10.1016/j.simpat.2009.09.002
10.1007/978-3-642-21219-2_58
10.1049/el.2014.2611
10.1109/BSN.2006.24
10.1109/JBHI.2015.2432033
10.1016/j.medengphy.2013.11.010
10.1109/TITB.2007.899496
10.3390/s150613159
10.1109/TIM.2012.2187245
10.1109/86.547939
10.1109/TBME.2006.886670
10.1109/JBHI.2013.2282471
10.1016/j.medengphy.2004.11.006
10.1109/ICSMC.2001.973004
10.3390/s140916181
10.1109/ICCV.2007.4408865
10.1145/1409635.1409638
10.1109/MPRV.2008.36
10.1109/21.97458
10.1145/3102304.3105828
10.1109/7333.928571
10.1145/2423636.2423644
10.1145/1656274.1656278
10.1109/WMVC.2007.12
10.1109/MPRV.2004.7
10.1109/TKDE.2011.51
10.1109/EMBC.2014.6944533
10.1109/NNSP.1999.788121
10.1109/IEMBS.2007.4353446
10.1109/TBME.2005.851475
10.1109/HealthCom.2012.6379453
10.1007/s11517-010-0701-3
10.1109/TIM.2009.2025065
10.1109/TITB.2010.2051955
10.1007/978-3-540-39863-9_17
10.1109/ISWC.2008.4911590
10.1109/79.91217
10.1109/TBME.2012.2208750
10.1109/TASE.2013.2256349
10.3390/jsan6040028
10.1109/TNSRE.2012.2230189
10.1016/j.pmcj.2009.10.004
10.1109/TNSRE.2007.914454
10.1109/TBME.2011.2178070
10.1109/PERCOM.2015.7146519
10.1145/2134203.2134205
10.3233/AIS-2010-0071
10.1109/TITB.2005.856863
10.1109/TSMCC.2007.905750
10.1145/1497185.1497223
10.1007/s00779-010-0345-1
10.1109/TNSRE.2003.822759
10.1145/279943.279962
10.1145/1689239.1689243
10.1111/j.1469-8986.1997.tb01747.x
10.1007/978-3-540-24646-6_10
10.1109/TBME.2012.2206384
10.1145/2493432.2493454
10.1109/ISWC.2012.23
10.1016/j.pmcj.2012.11.004
10.1109/KICSS.2012.26
10.1109/CC.2015.7114066
10.1016/S0966-6362(01)00201-6
10.1016/S1350-4533(00)00041-2
10.1109/TNSRE.2010.2053217
10.1109/MPRV.2010.7
10.1023/A:1018628609742
10.1109/ISSNIP.2005.1595593
10.1016/j.pmcj.2014.02.003
10.1109/JBHI.2014.2328593
10.1016/j.pmcj.2012.06.002
10.1007/s00779-012-0515-4
10.1109/ISWC.2009.23
10.1016/j.gaitpost.2004.08.008
ContentType Journal Article
Copyright 2018
Copyright © 2018. Published by Elsevier Inc.
Copyright_xml – notice: 2018
– notice: Copyright © 2018. Published by Elsevier Inc.
DBID 6I.
AAFTH
AAYXX
CITATION
NPM
7X8
DOI 10.1016/j.jbi.2018.09.002
DatabaseName ScienceDirect Open Access Titles
Elsevier:ScienceDirect:Open Access
CrossRef
PubMed
MEDLINE - Academic
DatabaseTitle CrossRef
PubMed
MEDLINE - Academic
DatabaseTitleList PubMed
MEDLINE - Academic
Database_xml – sequence: 1
  dbid: NPM
  name: PubMed
  url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed
  sourceTypes: Index Database
DeliveryMethod fulltext_linktorsrc
Discipline Medicine
Engineering
Public Health
EISSN 1532-0480
EndPage 153
ExternalDocumentID 30267895
10_1016_j_jbi_2018_09_002
S153204641830176X
Genre Journal Article
Review
GroupedDBID ---
--K
--M
-~X
.DC
.GJ
.~1
0R~
1B1
1RT
1~.
1~5
29J
4.4
457
4G.
53G
5GY
5VS
6I.
7-5
71M
8P~
AACTN
AAEDT
AAEDW
AAFTH
AAIAV
AAIKJ
AAKOC
AALRI
AAOAW
AAQFI
AAQXK
AAWTL
AAXUO
AAYFN
ABBOA
ABBQC
ABFRF
ABJNI
ABLVK
ABMAC
ABMZM
ABVKL
ABXDB
ABYKQ
ACDAQ
ACGFO
ACGFS
ACNNM
ACRLP
ACZNC
ADBBV
ADEZE
ADFGL
ADMUD
AEBSH
AEFWE
AEKER
AENEX
AEXQZ
AFKWA
AFTJW
AFXIZ
AGHFR
AGUBO
AGYEJ
AHZHX
AIALX
AIEXJ
AIKHN
AITUG
AJBFU
AJOXV
AJRQY
ALMA_UNASSIGNED_HOLDINGS
AMFUW
AMRAJ
ANZVX
AOUOD
ASPBG
AVWKF
AXJTR
AZFZN
BAWUL
BKOJK
BLXMC
BNPGV
CAG
COF
CS3
DIK
DM4
DU5
EBS
EFBJH
EFLBG
EJD
EO8
EO9
EP2
EP3
F5P
FDB
FEDTE
FGOYB
FIRID
FNPLU
FYGXN
G-Q
G8K
GBLVA
GBOLZ
HVGLF
HZ~
IHE
IXB
J1W
KOM
LCYCR
LG5
M41
MO0
N9A
NCXOZ
O-L
O9-
OAUVE
OK1
OZT
P-8
P-9
PC.
Q38
R2-
RIG
ROL
RPZ
SDF
SDG
SDP
SES
SEW
SPC
SPCBC
SSH
SSV
SSZ
T5K
UAP
UHS
UNMZH
XPP
ZGI
ZMT
ZU3
~G-
AATTM
AAXKI
AAYWO
AAYXX
ABDPE
ABWVN
ACIEU
ACRPL
ACVFH
ADCNI
ADNMO
ADVLN
AEIPS
AEUPX
AFJKZ
AFPUW
AGCQF
AGQPQ
AIGII
AIIUN
AKBMS
AKRWK
AKYEP
ANKPU
APXCP
CITATION
EFKBS
0SF
NPM
7X8
AGRNS
ID FETCH-LOGICAL-c462t-54f7534781d56223de5d4bf4db45e53d69956cc2e46d87bbddfd00fbc57bd48c3
IEDL.DBID IXB
ISSN 1532-0464
1532-0480
IngestDate Fri Jul 11 02:06:06 EDT 2025
Wed Feb 19 02:34:20 EST 2025
Wed Aug 27 16:25:01 EDT 2025
Thu Apr 24 23:06:56 EDT 2025
Fri Feb 23 02:34:24 EST 2024
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Keywords Systematic review
Internet of Things
Physical activity recognition
Sensor-based
Physical activity monitoring
Language English
License This article is made available under the Elsevier license.
Copyright © 2018. Published by Elsevier Inc.
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c462t-54f7534781d56223de5d4bf4db45e53d69956cc2e46d87bbddfd00fbc57bd48c3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ObjectType-Undefined-3
ORCID 0000-0002-8553-7127
OpenAccessLink https://www.sciencedirect.com/science/article/pii/S153204641830176X
PMID 30267895
PQID 2114699717
PQPubID 23479
PageCount 16
ParticipantIDs proquest_miscellaneous_2114699717
pubmed_primary_30267895
crossref_citationtrail_10_1016_j_jbi_2018_09_002
crossref_primary_10_1016_j_jbi_2018_09_002
elsevier_sciencedirect_doi_10_1016_j_jbi_2018_09_002
PublicationCentury 2000
PublicationDate November 2018
2018-11-00
20181101
PublicationDateYYYYMMDD 2018-11-01
PublicationDate_xml – month: 11
  year: 2018
  text: November 2018
PublicationDecade 2010
PublicationPlace United States
PublicationPlace_xml – name: United States
PublicationTitle Journal of biomedical informatics
PublicationTitleAlternate J Biomed Inform
PublicationYear 2018
Publisher Elsevier Inc
Publisher_xml – name: Elsevier Inc
References Luštrek, Kaluža (b0075) 2009; 33
M. Stikic, B. Schiele, Activity recognition from sparsely labeled data using multi-instance learning, Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 5561 LNCS, 2009, pp. 156–173.
Moncada-Torres, Leuenberger, Gonzenbach, Luft, Gassert (b0105) 2014; 35
Palmes, Pung, Gu, Xue, Chen (b0630) 2010; 6
M. Stikic, K. Van Laerhoven, B. Schiele, Exploring semi-supervised and active learning for activity recognition, in: 12th IEEE Int. Symp. Wearable Comput., 2008, pp. 81–88.
Meditskos, Dasiopoulou, Kompatsiaris (b0675) 2015
E.M. Tapia, S.S. Intille, W. Haskell, K. Larson, J. Wright, A. King, R. Friedman, Real-time recognition of physical activities and their intensities using wireless accelerometers and a heart rate monitor, in: 11th IEEE Int. Symp. Wearable Comput., 2007, pp. 1–4.
F. Adib, H. Mao, Z. Kabelac, D. Katabi, R.C. Miller, Smart homes that monitor breathing and heart rate, in: Proc. 33rd Annu. ACM Conf. Hum. Factors Comput. Syst. – CHI ’15, 2015, pp. 837–846.
Wyatt, Philipose, Choudhury (b0635) 2005; 20
Banos, Moral-Munoz, Diaz-Reyes, Arroyo-Morales, Damas, Herrera-Viedma, Hong, Lee, Pomares, Rojas, Villalonga (b0720) 2015; 15
Kwon, Kang, Bae (b0685) 2014; 41
Dong, Scisco, Wilson, Muth, Hoover (b0740) 2014; 18
Chernbumroong, Cang, Atkins, Yu (b0700) 2013; 40
A. Purwar, D. Do Jeong, W.Y. Chung, Activity monitoring from real-time triaxial accelerometer data using sensor network, in: Int. Conf. Control. Autom. Syst., 2007, pp. 2402–2406.
Khan, Lee, Lee, Kim (b0210) 2010; 14
E.C. Larson, M. Goel, G. Boriello, S. Heltshe, M. Rosenfeld, S.N. Patel, SpiroSmart, in: Proc. 2012 ACM Conf. Ubiquitous Comput. – UbiComp ’12, no. Figure 1, 2012, p. 280.
N.C. Krishnan, P. Lade, S. Panchanathan, Activity gesture spotting using a threshold model based on adaptive boosting, in: IEEE Int. Conf. Multimed. Expo, ICME 2010, 2010, pp. 155–160.
D. Anguita, A. Ghio, L. Oneto, X. Parra, J.L. Reyes-Ortiz, Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 7657 LNCS, 2012, pp. 216–223.
Chen, Nugent, Wang (b0405) 2012; 24
Naranjo-Hernández, Roa, Reina-Tosina, Estudillo-Valderrama (b0200) 2012; 59
Kranz, Möller, Hammerla, Diewald, Plötz, Olivier, Roalter (b0335) 2013; 9
K. Van Laerhoven, E. Berlin, and B. Schiele, “Enabling efficient time series analysis for wearable activity data, in: 8th Int. Conf. Mach. Learn. Appl., ICMLA 2009, 2009, pp. 392–397.
T. Maekawa, S. Watanabe, Unsupervised activity recognition with user’s physical characteristics data, in: Proc. – Int. Symp. Wearable Comput. ISWC, 2011, pp. 89–96.
E.J. Wang, W. Li, D. Hawkins, T. Gernsheimer, C. Norby-Slycord, S.N. Patel, HemaApp, in: Proc. 2016 ACM Int. Jt. Conf. Pervasive Ubiquitous Comput. – UbiComp’16, 2016, pp. 593–604.
Okeyo, Chen, Wang, Sterritt (b0380) 2014; 10
Z. He, L. Jin, Activity recognition from acceleration data using AR model representation and SVM, in: Int. Conf. Mach. Learn. Cybern., no. July, 2008, pp. 12–15.
S. Knox, L. Coyle, S. Dobson, Using ontologies in case-based activity recognition, in: FLAIRS Conf., 2010, pp. 1–6.
Krishnan, Cook (b0375) 2012; 13
Logan, Healey, Philipose, Tapia, Intille (b0265) 2007; 4717
Kau, Chen (b0325) 2015; 19
Pärkkä, Ermes, Korpipää, Mäntyjärvi, Peltola, Korhonen (b0570) 2006; 10
Zhou, Hu, Member (b0735) 2010; 59
Costigan, Deluzio, Wyss (b0085) 2002; 16
Van Kasteren, Englebienne, Kröse (b0555) 2010; 2
Naik, Kumar, Jayadeva (b0590) 2010; 14
McKeever, Ye, Coyle, Bleakley (b0680) 2010; 2
Safavian, Landgrebe (b0565) 1991; 21
Khan, Siddiqi, Lee (b0250) 2013; 13
H. Chen, F. Perich, T. Finin, A. Joshi, SOUPA : Standard Ontology for Ubiquitous and Pervasive Applications.
P. Natarajan, R. Nevatia, Coupled hidden semi Markov Models for activity recognition, in: Proc. IEEE Work. Motion Video Comput., 2007, p. 10.
R. Nandakumar, S. Gollakota, N. Watson, Contactless sleep apnea detection on smartphones, in: Proc. 13th Annu. Int. Conf. Mob. Syst. Appl. Serv. – MobiSys ’15, 2015, pp. 45–57.
Kim, Helal, Cook (b0545) 2010; 9
Philipose, Fishkin, Perkowitz, Patterson, Fox, Kautz, Hähnel (b0310) 2004; 3
Liu, Gao, John, Staudenmayer, Freedson (b0155) 2012; 59
Liu, Darabi, Banerjee, Liu (b0285) 2007; 37
Liao, Patterson, Fox, Kautz (b0295) 2007; 171
Pappas, Popovic, Keller, Dietz, Morari (b0080) 2001; 9
Giuffrida, Lerner, Steiner, Daly (b0140) 2008; 16
S. Mika, G. Ratsch, J. Weston, B. Schölkopf, K.-R. Muller, Fisher discriminant analysis with kernels, in: IEEE, 1999, pp. 41–48.
Z. Yan, V. Subbaraju, D. Chakraborty, A. Misra, K. Aberer, Energy-efficient continuous activity recognition on mobile phones: an activity-adaptive approach, in: 16th Int. Symp. Wearable Comput., 2012, pp. 17–24.
Veltink, Bussmann, de Vries, Martens, Van Lummel (b0065) 1996; 4
D. Minnen, T. Starner, J.A. Ward, P. Lukowicz, G. Troster, Recognizing and discovering human actions from on-body sensor data, in: IEEE Int. Conf. Multimed. Expo ICME, 2005, pp. 1545–1548.
R. Musculo, S. Conforto, M. Schmid, P. Caselli, T. D’Alessio, Classification of motor activities through derivative dynamic time warping applied on accelerometer data, in: Annu. Int. Conf. IEEE Eng. Med. Biol. – Proc, 2007, pp. 4930–4933.
J. Rafferty, C. N.-I. Member, L. C.-I. Member, J. Qi, R. Dutton, A. Zirk, L.T. Boye, M. Kohn, R. Hellman, NFC based provisioning of instructional videos to assist with instrumental activities of daily living, 2014, pp. 4131–4134.
Yang, Wang, Chen (b0465) 2008; 29
T. Huỳnh, B. Schiele, Towards less supervision in activity recognition from wearable sensors, in: Proc. – Int. Symp. Wearable Comput. ISWC, 2007, pp. 3–10.
Trabelsi, Mohammed, Chamroukhi, Oukhellou, Amirat (b0745) 2013; 10
Altini, Penders, Vullers, Amft (b0135) 2014; 2194
H. Li, C. Ye, A.P. Sample, IDSense, in: Proc. 33rd Annu. ACM Conf. Hum. Factors Comput. Syst. – CHI’15, no. c, 2015, pp. 2555–2564.
Paiyarom, Tungamchit, Keinprasit, Kayasith (b0595) 2009
Roggen, Förster, Calatroni, Tröster (b0400) 2013; 4
Han, Bang, Nugent, McClean, Lee (b0300) 2014; 14
K. Van Laerhoven, E. Berlin, When else did this happen? Efficient subsequence representation for matching in wearable activity data, in: Proc. – Int. Symp. Wearable Comput. ISWC, 2009, pp. 101–104.
Godfrey, Bourke, Ólaighin, van de Ven, Nelson (b0220) 2011; 33
T. Mitchell, A. Blum, Combining labeled and unlabeled data with co-training, in: Proc. Elev. Annu. Conf. Comput. Learn. Theory, 1998, pp. 92–100.
Lu, Fu (b0340) 2009
Jian, Chen (b0355) 2015
Reyes-Ortiz, Oneto, Samà, Parra, Anguita (b0345) 2015; 171
Bagala, Klenk, Cappello, Chiari, Becker, Lindemann (b0520) 2013; 21
Castro (b0025) 2017; 6
Lowe, ÓLaighin (b0115) 2014; 36
Keogh, Chu, Hart, Pazzani (b0360) 2003
Hynes, Wang, McCarrick, Kilmartin (b0705) 2011; 15
M.H. Ko, G. West, S. Venkatesh, M. Kumar, Online context recognition in multisensor systems using dynamic time warping, in: Int. Conf. Intell. Sensors, Sens. Networks Inf. Process., 2005, pp. 283–288.
F. Paganelli, D. Giuli, An ontology-based context model for home health monitoring and alerting in chronic patient care networks, in: Proc. – 21st Int. Conf. Adv. Inf. Netw. Appl. Work. AINAW’07, vol. 1, no. Iccc, 2007, pp. 838–845.
J. Lester, T. Choudhury, A hybrid discriminative/generative approach for modeling human activities, in: Proc. 19th Int. Jt. Conf. Artif. Intell., 2005, pp. 766–772.
Hall, Frank, Holmes, Pfahringer, Reutemann, Witten (b0575) 2009; 11
El-Gohary, McNames (b0750) 2012; 59
G. Spina, G. Huang, A. Vaes, M. Spruit, O. Amft, COPDTrainer: a smartphone-based motion rehabilitation training system with real-time acoustic feedback, 2013, pp. 597–606.
Nam, Rho, Lee (b0180) 2013; 12
Vandewalle (b0580) 1999; 9
Li, Rozgić, Thatte, Lee, Emken, Annavaram, Mitra, Spruijt-Metz, Narayanan (b0225) 2010; 18
J. Wu, A. Osuntogun, T. Choudhury, M. Philipose, J.M. Rehg, A scalable approach to activity recognition based on object use, in: 11th Int. Conf. Comput. Vision, ICCV 2007, IEEE, 2007, pp. 1–8.
Guyatt, Sullivan, Thompson, Fallen, Pugsley, Taylor, Berman (b0015) 1985; 132
Rioul, Vetterli (b0480) 1991; 8
Martín, Bernardos, Iglesias, Casar (b0255) 2013; 17
R. Ravichandran, E. Saba, K. Chen, M. Goel, S. Gupta, S.N. Patel, WiBreathe : Estimating respiration rate using wireless signals in natural settings in the home, vol. 16, 2015, pp. 131–139.
K. Oh, H.-S. Park, S.-B. Cho, A mobile context sharing system using activity and emotion recognition with bayesian networks, in: 7th Int. Conf. Ubiquitous Intell. Comput. 7th Int. Conf. Auton. Trust. Comput., no. Im, 2010, pp. 244–249.
Khan, Lee, Lee, Kim (b0245) 2010; 48
Karantonis, Narayanan, Mathie, Lovell, Celler (b0215) 2006; 10
A. McCallum, D. Freitag, F. Pereira, Maximum entropy markov models for information extraction and segmentation, in: Icml, 2000, pp. 1–26.
T. Huỳnh, M. Fritz, B. Schiele, Discovery of activity patterns using topic models, in: Proc. 10th Int. Conf. Ubiquitous Comput. (UbiComp ’08), 2008, pp. 10–19.
H. Amroun, Recognition of human activity using internet of things in a non-controlled environment, vol. 2016, no. November, 2016, pp. 13–15.
Kwapisz, Weiss, Moore (b0350) 2011; 12
J. Mantyjarvi, J. Himberg, T. Seppanen, Recognizing human motion with multiple acceleration sensors, in: IEEE Int. Conf. Syst. Man Cybern. e-Systems e-Man Cybern. Cybersp., vol. 2, 2001, pp. 2–7.
Bao, Intille (b0370) 2004
S. Kozina, M. Lustrek, M. Gams, Dynamic signal segmentation for activity recognition, in: Proc. Int. Jt. …, 2011, pp. 1–12.
Bannach, Lukowicz, Amft (b0435) 2008; 7
M.A. Stelios, A.D. Nick, M.T. Effie, K.M. Dimitris, S.C.A. Thomopoulos, An indoor localization platform for ambient assisted living using UWB, in: Proc. 6th Int. Conf. Adv. Mob. Comput. Multimed. – MoMM ’08, 2008, p. 178.
Ward, Lukowicz, Tröster, Starner (b0175) 2006; 28
Caspersen, Powell, Christenson (b0005) 1985; 100
Perriot, Argod, Pepin, Noury (b0185) 2014; 18
Spina, Casale, Albert, Alison, Garcia-ayme
Luštrek (10.1016/j.jbi.2018.09.002_b0075) 2009; 33
Hall (10.1016/j.jbi.2018.09.002_b0575) 2009; 11
Keogh (10.1016/j.jbi.2018.09.002_b0360) 2003
Altini (10.1016/j.jbi.2018.09.002_b0135) 2014; 2194
Kwapisz (10.1016/j.jbi.2018.09.002_b0350) 2011; 12
10.1016/j.jbi.2018.09.002_b0715
Martín (10.1016/j.jbi.2018.09.002_b0255) 2013; 17
10.1016/j.jbi.2018.09.002_b0710
10.1016/j.jbi.2018.09.002_b0315
Sekine (10.1016/j.jbi.2018.09.002_b0495) 2002; 10
Karantonis (10.1016/j.jbi.2018.09.002_b0215) 2006; 10
10.1016/j.jbi.2018.09.002_b0280
10.1016/j.jbi.2018.09.002_b0160
Kwon (10.1016/j.jbi.2018.09.002_b0685) 2014; 41
Khan (10.1016/j.jbi.2018.09.002_b0245) 2010; 48
Sekine (10.1016/j.jbi.2018.09.002_b0490) 2000; 22
10.1016/j.jbi.2018.09.002_b0440
10.1016/j.jbi.2018.09.002_b0045
10.1016/j.jbi.2018.09.002_b0320
Ye (10.1016/j.jbi.2018.09.002_b0730) 2014; 19
Merilahti (10.1016/j.jbi.2018.09.002_b0235) 2015; 2194
10.1016/j.jbi.2018.09.002_b0040
10.1016/j.jbi.2018.09.002_b0560
Vandewalle (10.1016/j.jbi.2018.09.002_b0580) 1999; 9
10.1016/j.jbi.2018.09.002_b0305
Logan (10.1016/j.jbi.2018.09.002_b0265) 2007; 4717
Kau (10.1016/j.jbi.2018.09.002_b0325) 2015; 19
10.1016/j.jbi.2018.09.002_b0665
Chernbumroong (10.1016/j.jbi.2018.09.002_b0700) 2013; 40
Moncada-Torres (10.1016/j.jbi.2018.09.002_b0105) 2014; 35
10.1016/j.jbi.2018.09.002_b0425
Tapia (10.1016/j.jbi.2018.09.002_b0695) 2004; 3001
10.1016/j.jbi.2018.09.002_b0390
El-Gohary (10.1016/j.jbi.2018.09.002_b0750) 2012; 59
Wyatt (10.1016/j.jbi.2018.09.002_b0635) 2005; 20
10.1016/j.jbi.2018.09.002_b0275
10.1016/j.jbi.2018.09.002_b0430
10.1016/j.jbi.2018.09.002_b0035
Liu (10.1016/j.jbi.2018.09.002_b0155) 2012; 59
Shoaib (10.1016/j.jbi.2018.09.002_b0690) 2014; 14
10.1016/j.jbi.2018.09.002_b0030
Jian (10.1016/j.jbi.2018.09.002_b0355) 2015
10.1016/j.jbi.2018.09.002_b0395
10.1016/j.jbi.2018.09.002_b0670
Lee (10.1016/j.jbi.2018.09.002_b0130) 2012; 61
10.1016/j.jbi.2018.09.002_b0615
Fahrenberg (10.1016/j.jbi.2018.09.002_b0530) 1997; 34
10.1016/j.jbi.2018.09.002_b0455
Lyons (10.1016/j.jbi.2018.09.002_b0070) 2005; 27
Dolédec (10.1016/j.jbi.2018.09.002_b0510) 1997
Luinge (10.1016/j.jbi.2018.09.002_b0125) 2004; 12
10.1016/j.jbi.2018.09.002_b0060
Lu (10.1016/j.jbi.2018.09.002_b0340) 2009
Staudenmayer (10.1016/j.jbi.2018.09.002_b0535) 2009; 17
Pappas (10.1016/j.jbi.2018.09.002_b0080) 2001; 9
Meditskos (10.1016/j.jbi.2018.09.002_b0675) 2015
Caspersen (10.1016/j.jbi.2018.09.002_b0005) 1985; 100
Junker (10.1016/j.jbi.2018.09.002_b0420) 2008; 41
Hong (10.1016/j.jbi.2018.09.002_b0470) 2010; 18
Najafi (10.1016/j.jbi.2018.09.002_b0500) 2003; 50
10.1016/j.jbi.2018.09.002_b0585
Han (10.1016/j.jbi.2018.09.002_b0300) 2014; 14
10.1016/j.jbi.2018.09.002_b0460
Okeyo (10.1016/j.jbi.2018.09.002_b0380) 2014; 10
Hankin (10.1016/j.jbi.2018.09.002_b0485) 1977; 41
Reddy (10.1016/j.jbi.2018.09.002_b0525) 2010; 6
Hynes (10.1016/j.jbi.2018.09.002_b0705) 2011; 15
Banos (10.1016/j.jbi.2018.09.002_b0720) 2015; 15
10.1016/j.jbi.2018.09.002_b0725
10.1016/j.jbi.2018.09.002_b0605
Veltink (10.1016/j.jbi.2018.09.002_b0065) 1996; 4
10.1016/j.jbi.2018.09.002_b0445
10.1016/j.jbi.2018.09.002_b0600
Alshurafa (10.1016/j.jbi.2018.09.002_b0230) 2013; 18
Nam (10.1016/j.jbi.2018.09.002_b0180) 2013; 12
Roggen (10.1016/j.jbi.2018.09.002_b0400) 2013; 4
Guyatt (10.1016/j.jbi.2018.09.002_b0015) 1985; 132
10.1016/j.jbi.2018.09.002_b0290
10.1016/j.jbi.2018.09.002_b0170
10.1016/j.jbi.2018.09.002_b0050
Rioul (10.1016/j.jbi.2018.09.002_b0480) 1991; 8
Pärkkä (10.1016/j.jbi.2018.09.002_b0570) 2006; 10
McKeever (10.1016/j.jbi.2018.09.002_b0680) 2010; 2
10.1016/j.jbi.2018.09.002_b0055
10.1016/j.jbi.2018.09.002_b0330
Dong (10.1016/j.jbi.2018.09.002_b0740) 2014; 18
Minor (10.1016/j.jbi.2018.09.002_b0010) 1989; 32
Giuffrida (10.1016/j.jbi.2018.09.002_b0140) 2008; 16
10.1016/j.jbi.2018.09.002_b0515
Mahdaviani (10.1016/j.jbi.2018.09.002_b0660) 2008
Bianchi (10.1016/j.jbi.2018.09.002_b0110) 2010; 18
Naik (10.1016/j.jbi.2018.09.002_b0590) 2010; 14
Dejnabadi (10.1016/j.jbi.2018.09.002_b0095) 2005; 52
Philipose (10.1016/j.jbi.2018.09.002_b0310) 2004; 3
Castro (10.1016/j.jbi.2018.09.002_b0025) 2017; 6
Gyllensten (10.1016/j.jbi.2018.09.002_b0195) 2011; 58
Maurer (10.1016/j.jbi.2018.09.002_b0165) 2006
Choudhury (10.1016/j.jbi.2018.09.002_b0150) 2008; 7
Kranz (10.1016/j.jbi.2018.09.002_b0335) 2013; 9
Bulling (10.1016/j.jbi.2018.09.002_b0610) 2012; 9
10.1016/j.jbi.2018.09.002_b0365
10.1016/j.jbi.2018.09.002_b0640
10.1016/j.jbi.2018.09.002_b0240
10.1016/j.jbi.2018.09.002_b0625
10.1016/j.jbi.2018.09.002_b0505
Zhou (10.1016/j.jbi.2018.09.002_b0735) 2010; 59
Reyes-Ortiz (10.1016/j.jbi.2018.09.002_b0345) 2015; 171
Ermes (10.1016/j.jbi.2018.09.002_b0190) 2008; 12
Bagala (10.1016/j.jbi.2018.09.002_b0520) 2013; 21
Liu (10.1016/j.jbi.2018.09.002_b0285) 2007; 37
Wu (10.1016/j.jbi.2018.09.002_b0120) 2008; 16
10.1016/j.jbi.2018.09.002_b0475
Kern (10.1016/j.jbi.2018.09.002_b0450) 2003
Coley (10.1016/j.jbi.2018.09.002_b0090) 2005; 22
10.1016/j.jbi.2018.09.002_b0415
Yang (10.1016/j.jbi.2018.09.002_b0465) 2008; 29
Liao (10.1016/j.jbi.2018.09.002_b0295) 2007; 171
Khan (10.1016/j.jbi.2018.09.002_b0250) 2013; 13
Safavian (10.1016/j.jbi.2018.09.002_b0565) 1991; 21
10.1016/j.jbi.2018.09.002_b0655
Lee (10.1016/j.jbi.2018.09.002_b0550) 2011
Kim (10.1016/j.jbi.2018.09.002_b0545) 2010; 9
Li (10.1016/j.jbi.2018.09.002_b0225) 2010; 18
Costigan (10.1016/j.jbi.2018.09.002_b0085) 2002; 16
Naranjo-Hernández (10.1016/j.jbi.2018.09.002_b0200) 2012; 59
Palmes (10.1016/j.jbi.2018.09.002_b0630) 2010; 6
Spina (10.1016/j.jbi.2018.09.002_b0205) 2015; 19
Lowe (10.1016/j.jbi.2018.09.002_b0115) 2014; 36
Godfrey (10.1016/j.jbi.2018.09.002_b0220) 2011; 33
Perriot (10.1016/j.jbi.2018.09.002_b0185) 2014; 18
Khan (10.1016/j.jbi.2018.09.002_b0210) 2010; 14
10.1016/j.jbi.2018.09.002_b0385
10.1016/j.jbi.2018.09.002_b0540
10.1016/j.jbi.2018.09.002_b0145
Bannach (10.1016/j.jbi.2018.09.002_b0435) 2008; 7
Shi (10.1016/j.jbi.2018.09.002_b0755) 2014; 50
10.1016/j.jbi.2018.09.002_b0260
Salarian (10.1016/j.jbi.2018.09.002_b0100) 2007; 54
10.1016/j.jbi.2018.09.002_b0020
Paiyarom (10.1016/j.jbi.2018.09.002_b0595) 2009
Chen (10.1016/j.jbi.2018.09.002_b0405) 2012; 24
10.1016/j.jbi.2018.09.002_b0645
Van Kasteren (10.1016/j.jbi.2018.09.002_b0555) 2010; 2
Trabelsi (10.1016/j.jbi.2018.09.002_b0745) 2013; 10
Ward (10.1016/j.jbi.2018.09.002_b0175) 2006; 28
Zhang (10.1016/j.jbi.2018.09.002_b0620) 2010; 1
Ding (10.1016/j.jbi.2018.09.002_b0270) 2011; 69
Krishnan (10.1016/j.jbi.2018.09.002_b0375) 2012; 13
10.1016/j.jbi.2018.09.002_b0650
10.1016/j.jbi.2018.09.002_b0410
Bao (10.1016/j.jbi.2018.09.002_b0370) 2004
References_xml – reference: M.H. Ko, G. West, S. Venkatesh, M. Kumar, Online context recognition in multisensor systems using dynamic time warping, in: Int. Conf. Intell. Sensors, Sens. Networks Inf. Process., 2005, pp. 283–288.
– volume: 7
  start-page: 22
  year: 2008
  end-page: 31
  ident: b0435
  article-title: Rapid prototyping of activity recognition applications
  publication-title: Pervasive Comput. IEEE
– volume: 10
  start-page: 119
  year: 2006
  end-page: 128
  ident: b0570
  article-title: Activity classification using realistic data from wearable sensors
  publication-title: IEEE Trans. Inf. Technol. Biomed.
– reference: J. Mantyjarvi, J. Himberg, T. Seppanen, Recognizing human motion with multiple acceleration sensors, in: IEEE Int. Conf. Syst. Man Cybern. e-Systems e-Man Cybern. Cybersp., vol. 2, 2001, pp. 2–7.
– reference: Z. He, L. Jin, Activity recognition from acceleration data using AR model representation and SVM, in: Int. Conf. Mach. Learn. Cybern., no. July, 2008, pp. 12–15.
– volume: 21
  start-page: 660
  year: 1991
  end-page: 674
  ident: b0565
  article-title: “A survey of decision tree classifier methodology
  publication-title: IEEE Trans. Syst. Man, Cybern.
– volume: 1
  start-page: 43
  year: 2010
  end-page: 52
  ident: b0620
  article-title: Understanding bag-of-words model: a statistical framework
  publication-title: Int. J. Mach. Learn. Cybern.
– reference: Z. Yan, V. Subbaraju, D. Chakraborty, A. Misra, K. Aberer, Energy-efficient continuous activity recognition on mobile phones: an activity-adaptive approach, in: 16th Int. Symp. Wearable Comput., 2012, pp. 17–24.
– reference: S. Knox, L. Coyle, S. Dobson, Using ontologies in case-based activity recognition, in: FLAIRS Conf., 2010, pp. 1–6.
– volume: 171
  start-page: 754
  year: 2015
  end-page: 767
  ident: b0345
  article-title: Transition-aware human activity recognition using smartphones
  publication-title: Neurocomputing
– volume: 32
  start-page: 1396
  year: 1989
  end-page: 1405
  ident: b0010
  article-title: Efficacy of physical conditioning exercise in patients with rheumatoid arthritis and osteoarthritis
  publication-title: Arthritis Rheum.
– volume: 37
  start-page: 1067
  year: 2007
  end-page: 1080
  ident: b0285
  article-title: Survey of wireless indoor positioning techniques and systems
  publication-title: IEEE Trans. Syst. Man, Cybern. Part C Appl. Rev.
– volume: 50
  start-page: 711
  year: 2003
  end-page: 723
  ident: b0500
  article-title: Ambulatory system for human motion analysis using a kinematic sensor: Monitoring of daily physical activity in the elderly
  publication-title: IEEE Trans. Biomed. Eng.
– year: 2015
  ident: b0675
  article-title: MetaQ: A knowledge-driven framework for context-aware activity recognition combining SPARQL and OWL 2 activity patterns
  publication-title: Pervasive Mob. Comput.
– reference: K. Van Laerhoven, E. Berlin, When else did this happen? Efficient subsequence representation for matching in wearable activity data, in: Proc. – Int. Symp. Wearable Comput. ISWC, 2009, pp. 101–104.
– start-page: 220
  year: 2003
  end-page: 232
  ident: b0450
  article-title: Multi-sensor activity context detection for wearable computing
  publication-title: Ambient Intell.
– reference: A. Environment, M.C. Mozer, That adapts to its inhabitants, 1998, pp. 0–4.
– volume: 4717
  start-page: 483
  year: 2007
  end-page: 500
  ident: b0265
  article-title: A long-term evaluation of sensing modalities for activity recognition
  publication-title: UbiComp 2007 Ubiquitous Comput.
– start-page: 4
  year: 2006
  end-page: 7
  ident: b0165
  article-title: Activity recognition and monitoring using multiple sensors on different body positions
  publication-title: Int. Work. Wearable Implant. Body Sens. Netw.
– reference: J. Rafferty, C. N.-I. Member, L. C.-I. Member, J. Qi, R. Dutton, A. Zirk, L.T. Boye, M. Kohn, R. Hellman, NFC based provisioning of instructional videos to assist with instrumental activities of daily living, 2014, pp. 4131–4134.
– volume: 34
  start-page: 607
  year: 1997
  end-page: 612
  ident: b0530
  article-title: Assessment of posture and motion by multichannel piezoresistive accelerometer recordings
  publication-title: Psychophysiology
– volume: 17
  start-page: 1300
  year: 2009
  end-page: 1307
  ident: b0535
  article-title: An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer
  publication-title: J. Appl. Physiol.
– volume: 9
  start-page: 1
  year: 2012
  end-page: 21
  ident: b0610
  article-title: Multimodal recognition of reading activity in transit using body-worn sensors
  publication-title: ACM Trans. Appl. Percept.
– volume: 20
  start-page: 21
  year: 2005
  end-page: 27
  ident: b0635
  article-title: Unsupervised activity recognition using automatically mined common sense
  publication-title: Sensors
– volume: 9
  start-page: 293
  year: 1999
  end-page: 300
  ident: b0580
  article-title: Least squares support vector machine classifiers
  publication-title: Neural Process. Lett.
– volume: 14
  start-page: pp
  year: 2014
  ident: b0690
  article-title: Fusion of smartphone motion sensors for physical activity recognition
  publication-title: Sensors
– reference: K. Oh, H.-S. Park, S.-B. Cho, A mobile context sharing system using activity and emotion recognition with bayesian networks, in: 7th Int. Conf. Ubiquitous Intell. Comput. 7th Int. Conf. Auton. Trust. Comput., no. Im, 2010, pp. 244–249.
– volume: 14
  start-page: 1166
  year: 2010
  end-page: 1172
  ident: b0210
  article-title: A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer
  publication-title: IEEE Trans. Inf. Technol. Biomed.
– reference: J. Lester, T. Choudhury, A hybrid discriminative/generative approach for modeling human activities, in: Proc. 19th Int. Jt. Conf. Artif. Intell., 2005, pp. 766–772.
– reference: T. Huỳnh, M. Fritz, B. Schiele, Discovery of activity patterns using topic models, in: Proc. 10th Int. Conf. Ubiquitous Comput. (UbiComp ’08), 2008, pp. 10–19.
– reference: S. Kozina, M. Lustrek, M. Gams, Dynamic signal segmentation for activity recognition, in: Proc. Int. Jt. …, 2011, pp. 1–12.
– reference: T. Mitchell, A. Blum, Combining labeled and unlabeled data with co-training, in: Proc. Elev. Annu. Conf. Comput. Learn. Theory, 1998, pp. 92–100.
– volume: 28
  start-page: 1553
  year: 2006
  end-page: 1566
  ident: b0175
  article-title: Activity recognition of assembly tasks using body-worn microphones and accelerometers
  publication-title: IEEE Trans. Pattern Anal. Mach. Intell.
– reference: D. Minnen, T. Starner, J.A. Ward, P. Lukowicz, G. Troster, Recognizing and discovering human actions from on-body sensor data, in: IEEE Int. Conf. Multimed. Expo ICME, 2005, pp. 1545–1548.
– reference: J. Wu, A. Osuntogun, T. Choudhury, M. Philipose, J.M. Rehg, A scalable approach to activity recognition based on object use, in: 11th Int. Conf. Comput. Vision, ICCV 2007, IEEE, 2007, pp. 1–8.
– volume: 17
  start-page: 675
  year: 2013
  end-page: 695
  ident: b0255
  article-title: Activity logging using lightweight classification techniques in mobile devices
  publication-title: Pers. Ubiquitous Comput.
– reference: H. Li, C. Ye, A.P. Sample, IDSense, in: Proc. 33rd Annu. ACM Conf. Hum. Factors Comput. Syst. – CHI’15, no. c, 2015, pp. 2555–2564.
– start-page: 598
  year: 2009
  end-page: 609
  ident: b0340
  article-title: Robust location-aware activity recognition using wireless sensor network in an attentive home
  publication-title: IEEE Trans. Autom. Sci. Eng.
– volume: 6
  start-page: 1
  year: 2010
  end-page: 27
  ident: b0525
  article-title: Using mobile phones to determine transportation modes
  publication-title: ACM Trans. Sens. Netw.
– volume: 18
  start-page: 619
  year: 2010
  end-page: 627
  ident: b0110
  article-title: Barometric pressure and triaxial accelerometry-based falls event detection
  publication-title: IEEE Trans. Neural Syst. Rehabil. Eng.
– volume: 19
  start-page: 47
  year: 2014
  end-page: 70
  ident: b0730
  article-title: KCAR: A knowledge-driven approach for concurrent activity recognition
  publication-title: Pervasive Mob. Comput.
– volume: 12
  start-page: 1
  year: 2013
  end-page: 14
  ident: b0180
  article-title: Physical activity recognition using multiple sensors embedded in a wearable device
  publication-title: ACM Trans. Embed. Comput. Syst.
– volume: 29
  start-page: 2213
  year: 2008
  end-page: 2220
  ident: b0465
  article-title: Using acceleration measurements for activity recognition: an effective learning algorithm for constructing neural classifiers
  publication-title: Pattern Recognit. Lett.
– volume: 59
  start-page: 3177
  year: 2012
  end-page: 3184
  ident: b0200
  article-title: SoM: A smart sensor for human activity monitoring and assisted healthy ageing
  publication-title: IEEE Trans. Biomed. Eng.
– volume: 3001
  start-page: 158
  year: 2004
  end-page: 175
  ident: b0695
  article-title: Activity recognition in the home using simple and ubiquitous sensors
  publication-title: Pervasive Comput.
– reference: R. Musculo, S. Conforto, M. Schmid, P. Caselli, T. D’Alessio, Classification of motor activities through derivative dynamic time warping applied on accelerometer data, in: Annu. Int. Conf. IEEE Eng. Med. Biol. – Proc, 2007, pp. 4930–4933.
– reference: A. McCallum, D. Freitag, F. Pereira, Maximum entropy markov models for information extraction and segmentation, in: Icml, 2000, pp. 1–26.
– volume: 19
  start-page: 1567
  year: 2015
  end-page: 1576
  ident: b0205
  article-title: Identifying physical activity profiles in COPD patients using topic models
  publication-title: IEEE J. Biomed. Health Inf.
– reference: Z. He, L. Jin, Activity recognition from acceleration data based on discrete consine transform and SVM, in: IEEE Int. Conf. Syst. Man Cybern. 2009, SMC 2009, no. October, 2009, pp. 5041–5044.
– volume: 2194
  start-page: 1
  year: 2014
  end-page: 8
  ident: b0135
  article-title: Estimating energy expenditure using body-worn accelerometers: a comparison of methods, sensors number and positioning
  publication-title: IEEE J. Biomed. Heal. Inf.
– volume: 41
  start-page: 6067
  year: 2014
  end-page: 6074
  ident: b0685
  article-title: Unsupervised learning for human activity recognition using smartphone sensors
  publication-title: Expert Syst. Appl.
– volume: 4
  start-page: 169
  year: 2013
  end-page: 186
  ident: b0400
  article-title: The adARC pattern analysis architecture for adaptive human activity recognition systems
  publication-title: J. Ambient Intell. Humaniz. Comput.
– reference: P. Natarajan, R. Nevatia, Coupled hidden semi Markov Models for activity recognition, in: Proc. IEEE Work. Motion Video Comput., 2007, p. 10.
– reference: H. Amroun, Recognition of human activity using internet of things in a non-controlled environment, vol. 2016, no. November, 2016, pp. 13–15.
– volume: 16
  start-page: 82
  year: 2008
  end-page: 90
  ident: b0140
  article-title: Upper-extremity stroke therapy task discrimination using motion sensors and electromyography
  publication-title: IEEE Trans. Neural Syst. Rehabil. Eng.
– volume: 4
  start-page: 375
  year: 1996
  end-page: 385
  ident: b0065
  article-title: Detection of static and dynamic activities using uniaxial\naccelerometers
  publication-title: IEEE Trans. Rehabil. Eng.
– volume: 8
  start-page: 14
  year: 1991
  end-page: 38
  ident: b0480
  article-title: Wavelets and signal processing
  publication-title: IEEE Signal Process Mag.
– volume: 18
  start-page: 1253
  year: 2014
  end-page: 1260
  ident: b0740
  article-title: Detecting periods of eating during free-living by tracking wrist motion
  publication-title: IEEE J. Biomed. Heal. Inf.
– volume: 6
  start-page: 28
  year: 2017
  ident: b0025
  article-title: Wearable-based human activity recognition using an IoT approach
  publication-title: J. Sens. Actuator Netw.
– reference: M. Stikic, K. Van Laerhoven, B. Schiele, Exploring semi-supervised and active learning for activity recognition, in: 12th IEEE Int. Symp. Wearable Comput., 2008, pp. 81–88.
– reference: N.C. Krishnan, P. Lade, S. Panchanathan, Activity gesture spotting using a threshold model based on adaptive boosting, in: IEEE Int. Conf. Multimed. Expo, ICME 2010, 2010, pp. 155–160.
– reference: D. Guan, W. Yuan, Y.-K. Lee, A. Gavrilov, S. Lee, Activity recognition based on semi-supervised learning, in: 13th IEEE Int. Conf. Embed. Real-Time Comput. Syst. Appl. (RTCSA 2007), no. 1, 2007, pp. 469–475.
– start-page: 1
  year: 1997
  end-page: 28
  ident: b0510
  article-title: Between- and within-groups principal components analyses
  publication-title: Work. Pap.
– volume: 11
  start-page: 10
  year: 2009
  end-page: 18
  ident: b0575
  article-title: The WEKA data mining software
  publication-title: ACM SIGKDD Explor.
– volume: 16
  start-page: 31
  year: 2002
  end-page: 37
  ident: b0085
  article-title: Knee, and hip kinetics during normal stair climbing
  publication-title: Gait Post.
– volume: 54
  start-page: 313
  year: 2007
  end-page: 322
  ident: b0100
  article-title: Quantification of tremor and bradykinesia in Parkinson’s disease using a novel ambulatory monitoring system
  publication-title: IEEE Trans. Biomed. Eng.
– volume: 59
  start-page: 575
  year: 2010
  end-page: 585
  ident: b0735
  article-title: Reducing drifts in the inertial measurements of wrist and elbow positions
  publication-title: IEEE Trans. Instrum. Meas.
– volume: 35
  start-page: 1245
  year: 2014
  end-page: 1263
  ident: b0105
  article-title: Activity classification based on inertial and barometric pressure sensors at different anatomical locations
  publication-title: Physiol. Meas.
– volume: 19
  start-page: 44
  year: 2015
  end-page: 56
  ident: b0325
  article-title: A smart phone-based pocket fall accident detection, positioning, and rescue system
  publication-title: IEEE J. Biomed. Heal. Inf.
– volume: 10
  start-page: 155
  year: 2014
  end-page: 172
  ident: b0380
  article-title: Dynamic sensor data segmentation for real-time knowledge-driven activity recognition
  publication-title: Pervasive Mob. Comput.
– reference: E.J. Wang, W. Li, D. Hawkins, T. Gernsheimer, C. Norby-Slycord, S.N. Patel, HemaApp, in: Proc. 2016 ACM Int. Jt. Conf. Pervasive Ubiquitous Comput. – UbiComp’16, 2016, pp. 593–604.
– volume: 12
  start-page: 74
  year: 2011
  ident: b0350
  article-title: Activity recognition using cell phone accelerometers
  publication-title: ACM SIGKDD Explor. Newsl.
– start-page: 460
  year: 2011
  end-page: 467
  ident: b0550
  article-title: Activity recognition using hierarchical hidden markov models on a smartphone with 3D accelerometer
  publication-title: Hybrid Artif. Intell. Syst.
– volume: 9
  start-page: 48
  year: 2010
  end-page: 53
  ident: b0545
  article-title: Human activity recognition and pattern discovery
  publication-title: Pervasive Comput. IEEE
– volume: 14
  start-page: 16181
  year: 2014
  end-page: 16195
  ident: b0300
  article-title: A lightweight hierarchical activity recognition framework using smartphone sensors
  publication-title: Sensors
– volume: 41
  start-page: 2010
  year: 2008
  end-page: 2024
  ident: b0420
  article-title: Gesture spotting with body-worn inertial sensors to detect user activities
  publication-title: Pattern Recognit.
– volume: 41
  start-page: 341
  year: 1977
  end-page: 345
  ident: b0485
  article-title: Ambulatory cardiac monitoring
  publication-title: Conn. Med.
– reference: E.C. Larson, M. Goel, G. Boriello, S. Heltshe, M. Rosenfeld, S.N. Patel, SpiroSmart, in: Proc. 2012 ACM Conf. Ubiquitous Comput. – UbiComp ’12, no. Figure 1, 2012, p. 280.
– volume: 10
  start-page: 156
  year: 2006
  end-page: 167
  ident: b0215
  article-title: Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring
  publication-title: IEEE Trans. Inf. Technol. Biomed.
– reference: R. Ravichandran, E. Saba, K. Chen, M. Goel, S. Gupta, S.N. Patel, WiBreathe : Estimating respiration rate using wireless signals in natural settings in the home, vol. 16, 2015, pp. 131–139.
– reference: M. Stikic, B. Schiele, Activity recognition from sparsely labeled data using multi-instance learning, Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 5561 LNCS, 2009, pp. 156–173.
– start-page: 1
  year: 2003
  end-page: 21
  ident: b0360
  article-title: Segmenting time series: a survey and novel approach
  publication-title: Data Min. Time Ser. Databases
– reference: G. Spina, G. Huang, A. Vaes, M. Spruit, O. Amft, COPDTrainer: a smartphone-based motion rehabilitation training system with real-time acoustic feedback, 2013, pp. 597–606.
– volume: 27
  start-page: 497
  year: 2005
  end-page: 504
  ident: b0070
  article-title: A description of an accelerometer-based mobility monitoring technique
  publication-title: Med. Eng. Phys.
– reference: A. Purwar, D. Do Jeong, W.Y. Chung, Activity monitoring from real-time triaxial accelerometer data using sensor network, in: Int. Conf. Control. Autom. Syst., 2007, pp. 2402–2406.
– volume: 2
  start-page: 253
  year: 2010
  end-page: 269
  ident: b0680
  article-title: Activity recognition using temporal evidence theory
  publication-title: J. Ambient Intell. Smart Environ.
– volume: 14
  start-page: 301
  year: 2010
  end-page: 308
  ident: b0590
  article-title: Twin SVM for gesture classification using the surface electromyogram
  publication-title: IEEE Trans. Inf. Technol. Biomed.
– volume: 40
  start-page: 1662
  year: 2013
  end-page: 1674
  ident: b0700
  article-title: Elderly activities recognition and classification for applications in assisted living
  publication-title: Expert Syst. Appl.
– reference: R. Nandakumar, S. Gollakota, N. Watson, Contactless sleep apnea detection on smartphones, in: Proc. 13th Annu. Int. Conf. Mob. Syst. Appl. Serv. – MobiSys ’15, 2015, pp. 45–57.
– volume: 9
  start-page: 203
  year: 2013
  end-page: 215
  ident: b0335
  article-title: The mobile fitness coach: towards individualized skill assessment using personalized mobile devices
  publication-title: Pervasive Mob. Comput.
– reference: H. Chen, F. Perich, T. Finin, A. Joshi, SOUPA : Standard Ontology for Ubiquitous and Pervasive Applications.
– reference: E.M. Tapia, S.S. Intille, W. Haskell, K. Larson, J. Wright, A. King, R. Friedman, Real-time recognition of physical activities and their intensities using wireless accelerometers and a heart rate monitor, in: 11th IEEE Int. Symp. Wearable Comput., 2007, pp. 1–4.
– volume: 36
  start-page: 147
  year: 2014
  end-page: 168
  ident: b0115
  article-title: Monitoring human health behaviour in one’s living environment: a technological review
  publication-title: Med. Eng. Phys.
– volume: 59
  start-page: 687
  year: 2012
  end-page: 696
  ident: b0155
  article-title: Multisensor data fusion for physical activity assessment
  publication-title: IEEE Trans. Biomed. Eng.
– volume: 50
  start-page: 1633
  year: 2014
  end-page: 1635
  ident: b0755
  article-title: Continuous fine-grained arm action recognition using motion spectrum mixture models
  publication-title: Electron. Lett.
– volume: 3
  start-page: 50
  year: 2004
  end-page: 57
  ident: b0310
  article-title: Inferring activities from interactions with objects
  publication-title: IEEE Pervasive Comput.
– volume: 33
  start-page: 205
  year: 2009
  end-page: 212
  ident: b0075
  article-title: Fall detection and activity recognition with machine learning
  publication-title: Informatica
– reference: M.A. Stelios, A.D. Nick, M.T. Effie, K.M. Dimitris, S.C.A. Thomopoulos, An indoor localization platform for ambient assisted living using UWB, in: Proc. 6th Int. Conf. Adv. Mob. Comput. Multimed. – MoMM ’08, 2008, p. 178.
– start-page: 1
  year: 2008
  end-page: 8
  ident: b0660
  article-title: Fast and scalable training of semisupervised crfs with application to activity recognition
  publication-title: Adv. Neural Inf.
– volume: 24
  start-page: 961
  year: 2012
  end-page: 974
  ident: b0405
  article-title: A knowledge-driven approach to activity recognition in smart homes
  publication-title: IEEE Trans. Knowl. Data Eng.
– volume: 18
  start-page: 369
  year: 2010
  end-page: 380
  ident: b0225
  article-title: Multimodal physical activity recognition by fusing temporal and cepstral information
  publication-title: IEEE Trans. Neural Syst. Rehabil. Eng.
– volume: 21
  start-page: 624
  year: 2013
  end-page: 633
  ident: b0520
  article-title: Quantitative description of the lie-to-sit-to-stand-to-walk transfer by a single body-fixed sensor
  publication-title: IEEE Trans. Neural Syst. Rehabil. Eng.
– volume: 6
  start-page: 43
  year: 2010
  end-page: 57
  ident: b0630
  article-title: Object relevance weight pattern mining for activity recognition and segmentation
  publication-title: Pervasive Mob. Comput.
– reference: M.D. Oresti Banos, Rafael Garcia, Juan A. Holgado-Terriza, C.V. Hector Pomares, Ignacio Rojas, Alejandro Saez, mHealthDroid: a novel framework for agile development of mobile health applications, in: Ambient Assisted Living and Daily Activities, Springer International Publishing, 2014, pp. 91–98.
– reference: I.E. Achumba, S. Bersch, R. Khusainov, D. Azzi, U. Kamalu, Activity classification, 2012, pp. 427–430.
– volume: 58
  start-page: 2656
  year: 2011
  end-page: 2663
  ident: b0195
  article-title: Identifying types of physical activity with a single accelerometer: evaluating laboratory-trained algorithms in daily life
  publication-title: IEEE Trans. Biomed. Eng.
– volume: 171
  start-page: 311
  year: 2007
  end-page: 331
  ident: b0295
  article-title: Learning and inferring transportation routines
  publication-title: Artif. Intell.
– reference: T. Maekawa, S. Watanabe, Unsupervised activity recognition with user’s physical characteristics data, in: Proc. – Int. Symp. Wearable Comput. ISWC, 2011, pp. 89–96.
– reference: F. Paganelli, D. Giuli, An ontology-based context model for home health monitoring and alerting in chronic patient care networks, in: Proc. – 21st Int. Conf. Adv. Inf. Netw. Appl. Work. AINAW’07, vol. 1, no. Iccc, 2007, pp. 838–845.
– volume: 13
  start-page: 1133
  year: 2012
  end-page: 1145
  ident: b0375
  article-title: Activity recognition on streaming sensor data
  publication-title: Pervasive Mob. Comput.
– start-page: 1
  year: 2009
  end-page: 4
  ident: b0595
  article-title: Activity monitoring system using Dynamic Time Warping for the elderly and disabled people
  publication-title: Int. Conf. Comput. Control Commun.
– volume: 15
  start-page: 667
  year: 2011
  end-page: 678
  ident: b0705
  article-title: Accurate monitoring of human physical activity levels for medical diagnosis and monitoring using off-the-shelf cellular handsets
  publication-title: Pers. Ubiquitous Comput.
– reference: J. Wu, G. Pan, D. Zhang, G. Qi, Gesture recognition with a 3-d accelerometer, in: Proc. 6th Int. Conf. Ubiquitous Intell. Comput., 2009, pp. 25–38.
– reference: D. Anguita, A. Ghio, L. Oneto, X. Parra, J.L. Reyes-Ortiz, Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 7657 LNCS, 2012, pp. 216–223.
– volume: 48
  start-page: 1271
  year: 2010
  end-page: 1279
  ident: b0245
  article-title: Accelerometer’s position independent physical activity recognition system for long-term activity monitoring in the elderly
  publication-title: Med. Biol. Eng. Comput.
– volume: 2
  start-page: 311
  year: 2010
  end-page: 325
  ident: b0555
  article-title: Activity recognition using semi-Markov models on real world smart home datasets
  publication-title: Environments
– volume: 9
  start-page: 113
  year: 2001
  end-page: 125
  ident: b0080
  article-title: A reliable gait phase detection system
  publication-title: IEEE Trans. Neural Syst. Rehabil. Eng.
– volume: 12
  start-page: 20
  year: 2008
  end-page: 26
  ident: b0190
  article-title: Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions
  publication-title: IEEE Trans. Inf. Technol. Biomed.
– volume: 33
  start-page: 1127
  year: 2011
  end-page: 1135
  ident: b0220
  article-title: Activity classification using a single chest mounted tri-axial accelerometer
  publication-title: Med. Eng. Phys.
– start-page: 23
  year: 2015
  end-page: 31
  ident: b0355
  article-title: A portable fall detection and alerting system based on k-NN algorithm and remote medicine
  publication-title: China Commun.
– volume: 22
  start-page: 285
  year: 2000
  end-page: 291
  ident: b0490
  article-title: Classification of waist-acceleration signals in a continuous walking record
  publication-title: Med. Eng. Phys.
– volume: 18
  start-page: 1
  year: 2013
  end-page: 11
  ident: b0230
  article-title: Designing a robust activity recognition framework for health and exergaming using wearable sensors
  publication-title: IEEE J. Biomed. Heal. Inf.
– volume: 59
  start-page: 2635
  year: 2012
  end-page: 2641
  ident: b0750
  article-title: Shoulder and elbow joint angle tracking with inertial sensors
  publication-title: IEEE Trans. Biomed. Eng.
– reference: S. Mika, G. Ratsch, J. Weston, B. Schölkopf, K.-R. Muller, Fisher discriminant analysis with kernels, in: IEEE, 1999, pp. 41–48.
– volume: 15
  start-page: 13159
  year: 2015
  end-page: 13183
  ident: b0720
  article-title: MDurance: a novel mobile health system to support trunk endurance assessment
  publication-title: Sensors (Switzerland)
– reference: C. Rodriguez, D.M. Castro, W. Coral, J.L. Cabra, N. Velasquez, J. Colorado, D. Mendez, L.C. Trujillo, IoT system for human activity recognition using BioHarness 3 and smartphone, in: Proc. Int. Conf. Futur. Networks Distrib. Syst. – ICFNDS ’17, 2017, pp. 1–7.
– reference: K. Van Laerhoven, E. Berlin, and B. Schiele, “Enabling efficient time series analysis for wearable activity data, in: 8th Int. Conf. Mach. Learn. Appl., ICMLA 2009, 2009, pp. 392–397.
– volume: 100
  start-page: 126
  year: 1985
  end-page: 131
  ident: b0005
  article-title: Physical activity, exercise, and physical fitness: definitions and distinctions for health-related research
  publication-title: Public Health Rep.
– start-page: 1
  year: 2004
  end-page: 17
  ident: b0370
  article-title: Activity recognition from user-annotated acceleration data
  publication-title: Pervasive Comput.
– reference: T. van Kasteren, A. Noulas, G. Englebienne, B. Kröse, Accurate activity recognition in a home setting, 2008, pp. 1–9.
– reference: T. Huỳnh, B. Schiele, Towards less supervision in activity recognition from wearable sensors, in: Proc. – Int. Symp. Wearable Comput. ISWC, 2007, pp. 3–10.
– volume: 18
  start-page: 1225
  year: 2014
  end-page: 1231
  ident: b0185
  article-title: Characterization of physical activity in COPD patients: validation of a robust algorithm for actigraphic measurements in living situations
  publication-title: IEEE J. Biomed. Heal. Inf.
– volume: 132
  start-page: 919
  year: 1985
  end-page: 923
  ident: b0015
  article-title: The 6-minute walk: a new measure of exercise capacity in patients with chronic heart failure
  publication-title: Can. Med. Assoc. J.
– volume: 13
  start-page: 13099
  year: 2013
  end-page: 13122
  ident: b0250
  article-title: Exploratory data analysis of acceleration signals to select light-weight and accurate features for real-time activity recognition on smartphones
  publication-title: Sensors
– volume: 10
  start-page: 829
  year: 2013
  end-page: 835
  ident: b0745
  article-title: An unsupervised approach for automatic activity recognition based on hidden markov model regression
  publication-title: IEEE Trans. Autom. Sci. Eng.
– reference: C. Lombriser, N. B. Bharatula, D. Roggen, G. Tröster, On-body activity recognition in a dynamic sensor network, in: Proc. ICST 2nd Int. Conf. Body Area Networks, 2007, pp. 1–6.
– reference: T.V. Duong, H.H. Bui, D.Q. Phung, S. Venkatesh, Activity recognition and abnormality detection with the switching hidden semi-Markov model, in: Proc. Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. I, 2005, pp. 838–845.
– reference: F. Adib, H. Mao, Z. Kabelac, D. Katabi, R.C. Miller, Smart homes that monitor breathing and heart rate, in: Proc. 33rd Annu. ACM Conf. Hum. Factors Comput. Syst. – CHI ’15, 2015, pp. 837–846.
– ident: 10.1016/j.jbi.2018.09.002_b0615
  doi: 10.1109/ISWC.2011.24
– ident: 10.1016/j.jbi.2018.09.002_b0060
  doi: 10.1109/AINAW.2007.90
– ident: 10.1016/j.jbi.2018.09.002_b0430
  doi: 10.1109/ICMLA.2009.112
– volume: 17
  start-page: 1300
  year: 2009
  ident: 10.1016/j.jbi.2018.09.002_b0535
  article-title: An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer
  publication-title: J. Appl. Physiol.
  doi: 10.1152/japplphysiol.00465.2009
– volume: 35
  start-page: 1245
  issue: 7
  year: 2014
  ident: 10.1016/j.jbi.2018.09.002_b0105
  article-title: Activity classification based on inertial and barometric pressure sensors at different anatomical locations
  publication-title: Physiol. Meas.
  doi: 10.1088/0967-3334/35/7/1245
– volume: 18
  start-page: 619
  issue: 6
  year: 2010
  ident: 10.1016/j.jbi.2018.09.002_b0110
  article-title: Barometric pressure and triaxial accelerometry-based falls event detection
  publication-title: IEEE Trans. Neural Syst. Rehabil. Eng.
  doi: 10.1109/TNSRE.2010.2070807
– start-page: 598
  year: 2009
  ident: 10.1016/j.jbi.2018.09.002_b0340
  article-title: Robust location-aware activity recognition using wireless sensor network in an attentive home
  publication-title: IEEE Trans. Autom. Sci. Eng.
– ident: 10.1016/j.jbi.2018.09.002_b0145
  doi: 10.1109/ISWC.2007.4373774
– volume: 132
  start-page: 919
  issue: 8
  year: 1985
  ident: 10.1016/j.jbi.2018.09.002_b0015
  article-title: The 6-minute walk: a new measure of exercise capacity in patients with chronic heart failure
  publication-title: Can. Med. Assoc. J.
– volume: 18
  start-page: 1225
  issue: 4
  year: 2014
  ident: 10.1016/j.jbi.2018.09.002_b0185
  article-title: Characterization of physical activity in COPD patients: validation of a robust algorithm for actigraphic measurements in living situations
  publication-title: IEEE J. Biomed. Heal. Inf.
  doi: 10.1109/JBHI.2013.2282617
– ident: 10.1016/j.jbi.2018.09.002_b0455
– ident: 10.1016/j.jbi.2018.09.002_b0650
  doi: 10.1109/RTCSA.2007.17
– volume: 41
  start-page: 341
  issue: 6
  year: 1977
  ident: 10.1016/j.jbi.2018.09.002_b0485
  article-title: Ambulatory cardiac monitoring
  publication-title: Conn. Med.
– volume: 58
  start-page: 2656
  issue: 9
  year: 2011
  ident: 10.1016/j.jbi.2018.09.002_b0195
  article-title: Identifying types of physical activity with a single accelerometer: evaluating laboratory-trained algorithms in daily life
  publication-title: IEEE Trans. Biomed. Eng.
  doi: 10.1109/TBME.2011.2160723
– volume: 50
  start-page: 711
  issue: 6
  year: 2003
  ident: 10.1016/j.jbi.2018.09.002_b0500
  article-title: Ambulatory system for human motion analysis using a kinematic sensor: Monitoring of daily physical activity in the elderly
  publication-title: IEEE Trans. Biomed. Eng.
  doi: 10.1109/TBME.2003.812189
– volume: 7
  start-page: 32
  issue: 2
  year: 2008
  ident: 10.1016/j.jbi.2018.09.002_b0150
  article-title: An embedded activity recognition system
  publication-title: IEEE Pervasive Comput.
  doi: 10.1109/MPRV.2008.39
– ident: 10.1016/j.jbi.2018.09.002_b0410
  doi: 10.1145/1409635.1409637
– ident: 10.1016/j.jbi.2018.09.002_b0445
  doi: 10.1109/UIC-ATC.2010.26
– ident: 10.1016/j.jbi.2018.09.002_b0170
  doi: 10.1109/ICME.2005.1521728
– ident: 10.1016/j.jbi.2018.09.002_b0020
– ident: 10.1016/j.jbi.2018.09.002_b0320
– ident: 10.1016/j.jbi.2018.09.002_b0475
  doi: 10.1007/978-3-642-02830-4_4
– ident: 10.1016/j.jbi.2018.09.002_b0715
  doi: 10.1007/978-3-319-13105-4_14
– volume: 40
  start-page: 1662
  issue: 5
  year: 2013
  ident: 10.1016/j.jbi.2018.09.002_b0700
  article-title: Elderly activities recognition and classification for applications in assisted living
  publication-title: Expert Syst. Appl.
  doi: 10.1016/j.eswa.2012.09.004
– ident: 10.1016/j.jbi.2018.09.002_b0395
  doi: 10.4108/bodynets.2007.114
– ident: 10.1016/j.jbi.2018.09.002_b0515
– volume: 10
  start-page: 188
  issue: 3
  year: 2002
  ident: 10.1016/j.jbi.2018.09.002_b0495
  article-title: Discrimination of walking patterns using wavelet-based fractal analysis
  publication-title: IEEE Trans. Neural Syst. Rehabil. Eng.
  doi: 10.1109/TNSRE.2002.802879
– volume: 33
  start-page: 1127
  issue: 9
  year: 2011
  ident: 10.1016/j.jbi.2018.09.002_b0220
  article-title: Activity classification using a single chest mounted tri-axial accelerometer
  publication-title: Med. Eng. Phys.
  doi: 10.1016/j.medengphy.2011.05.002
– volume: 171
  start-page: 754
  year: 2015
  ident: 10.1016/j.jbi.2018.09.002_b0345
  article-title: Transition-aware human activity recognition using smartphones
  publication-title: Neurocomputing
  doi: 10.1016/j.neucom.2015.07.085
– ident: 10.1016/j.jbi.2018.09.002_b0425
  doi: 10.1109/ICME.2010.5583013
– volume: 16
  start-page: 178
  issue: 2
  year: 2008
  ident: 10.1016/j.jbi.2018.09.002_b0120
  article-title: Portable preimpact fall detector with inertial sensors
  publication-title: IEEE Trans. Neural Syst. Rehabil. Eng.
  doi: 10.1109/TNSRE.2007.916282
– ident: 10.1016/j.jbi.2018.09.002_b0725
  doi: 10.1109/CVPR.2005.61
– volume: 14
  start-page: pp
  issue: 6
  year: 2014
  ident: 10.1016/j.jbi.2018.09.002_b0690
  article-title: Fusion of smartphone motion sensors for physical activity recognition
  publication-title: Sensors
  doi: 10.3390/s140610146
– volume: 29
  start-page: 2213
  issue: 16
  year: 2008
  ident: 10.1016/j.jbi.2018.09.002_b0465
  article-title: Using acceleration measurements for activity recognition: an effective learning algorithm for constructing neural classifiers
  publication-title: Pattern Recognit. Lett.
  doi: 10.1016/j.patrec.2008.08.002
– volume: 41
  start-page: 6067
  issue: 14
  year: 2014
  ident: 10.1016/j.jbi.2018.09.002_b0685
  article-title: Unsupervised learning for human activity recognition using smartphone sensors
  publication-title: Expert Syst. Appl.
  doi: 10.1016/j.eswa.2014.04.037
– volume: 28
  start-page: 1553
  issue: 10
  year: 2006
  ident: 10.1016/j.jbi.2018.09.002_b0175
  article-title: Activity recognition of assembly tasks using body-worn microphones and accelerometers
  publication-title: IEEE Trans. Pattern Anal. Mach. Intell.
  doi: 10.1109/TPAMI.2006.197
– ident: 10.1016/j.jbi.2018.09.002_b0045
  doi: 10.1145/2742647.2742674
– volume: 41
  start-page: 2010
  issue: 6
  year: 2008
  ident: 10.1016/j.jbi.2018.09.002_b0420
  article-title: Gesture spotting with body-worn inertial sensors to detect user activities
  publication-title: Pattern Recognit.
  doi: 10.1016/j.patcog.2007.11.016
– ident: 10.1016/j.jbi.2018.09.002_b0665
– ident: 10.1016/j.jbi.2018.09.002_b0305
  doi: 10.1145/2702123.2702178
– volume: 4
  start-page: 169
  issue: 2
  year: 2013
  ident: 10.1016/j.jbi.2018.09.002_b0400
  article-title: The adARC pattern analysis architecture for adaptive human activity recognition systems
  publication-title: J. Ambient Intell. Humaniz. Comput.
  doi: 10.1007/s12652-011-0064-0
– volume: 20
  start-page: 21
  issue: 1
  year: 2005
  ident: 10.1016/j.jbi.2018.09.002_b0635
  article-title: Unsupervised activity recognition using automatically mined common sense
  publication-title: Sensors
– volume: 1
  start-page: 43
  issue: 1–4
  year: 2010
  ident: 10.1016/j.jbi.2018.09.002_b0620
  article-title: Understanding bag-of-words model: a statistical framework
  publication-title: Int. J. Mach. Learn. Cybern.
  doi: 10.1007/s13042-010-0001-0
– ident: 10.1016/j.jbi.2018.09.002_b0050
  doi: 10.1145/2702123.2702200
– ident: 10.1016/j.jbi.2018.09.002_b0240
  doi: 10.1109/ICCAS.2007.4406764
– volume: 13
  start-page: 13099
  issue: 10
  year: 2013
  ident: 10.1016/j.jbi.2018.09.002_b0250
  article-title: Exploratory data analysis of acceleration signals to select light-weight and accurate features for real-time activity recognition on smartphones
  publication-title: Sensors
  doi: 10.3390/s131013099
– volume: 12
  start-page: 74
  year: 2011
  ident: 10.1016/j.jbi.2018.09.002_b0350
  article-title: Activity recognition using cell phone accelerometers
  publication-title: ACM SIGKDD Explor. Newsl.
  doi: 10.1145/1964897.1964918
– volume: 69
  start-page: 131
  issue: 2
  year: 2011
  ident: 10.1016/j.jbi.2018.09.002_b0270
  article-title: Sensor technology for smart homes
  publication-title: Maturitas
  doi: 10.1016/j.maturitas.2011.03.016
– volume: 14
  start-page: 301
  issue: 2
  year: 2010
  ident: 10.1016/j.jbi.2018.09.002_b0590
  article-title: Twin SVM for gesture classification using the surface electromyogram
  publication-title: IEEE Trans. Inf. Technol. Biomed.
  doi: 10.1109/TITB.2009.2037752
– volume: 10
  start-page: 156
  issue: 1
  year: 2006
  ident: 10.1016/j.jbi.2018.09.002_b0215
  article-title: Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring
  publication-title: IEEE Trans. Inf. Technol. Biomed.
  doi: 10.1109/TITB.2005.856864
– volume: 171
  start-page: 311
  issue: 5–6
  year: 2007
  ident: 10.1016/j.jbi.2018.09.002_b0295
  article-title: Learning and inferring transportation routines
  publication-title: Artif. Intell.
  doi: 10.1016/j.artint.2007.01.006
– ident: 10.1016/j.jbi.2018.09.002_b0655
  doi: 10.1109/ISWC.2006.286336
– volume: 32
  start-page: 1396
  issue: 11
  year: 1989
  ident: 10.1016/j.jbi.2018.09.002_b0010
  article-title: Efficacy of physical conditioning exercise in patients with rheumatoid arthritis and osteoarthritis
  publication-title: Arthritis Rheum.
  doi: 10.1002/anr.1780321108
– start-page: 1
  year: 2004
  ident: 10.1016/j.jbi.2018.09.002_b0370
  article-title: Activity recognition from user-annotated acceleration data
  publication-title: Pervasive Comput.
– volume: 18
  start-page: 446
  issue: 4
  year: 2010
  ident: 10.1016/j.jbi.2018.09.002_b0470
  article-title: Mobile health monitoring system based on activity recognition using accelerometer
  publication-title: Simul. Model. Pract. Theory
  doi: 10.1016/j.simpat.2009.09.002
– start-page: 460
  year: 2011
  ident: 10.1016/j.jbi.2018.09.002_b0550
  article-title: Activity recognition using hierarchical hidden markov models on a smartphone with 3D accelerometer
  publication-title: Hybrid Artif. Intell. Syst.
  doi: 10.1007/978-3-642-21219-2_58
– volume: 50
  start-page: 1633
  issue: 22
  year: 2014
  ident: 10.1016/j.jbi.2018.09.002_b0755
  article-title: Continuous fine-grained arm action recognition using motion spectrum mixture models
  publication-title: Electron. Lett.
  doi: 10.1049/el.2014.2611
– start-page: 1
  year: 2009
  ident: 10.1016/j.jbi.2018.09.002_b0595
  article-title: Activity monitoring system using Dynamic Time Warping for the elderly and disabled people
  publication-title: Int. Conf. Comput. Control Commun.
– start-page: 4
  year: 2006
  ident: 10.1016/j.jbi.2018.09.002_b0165
  article-title: Activity recognition and monitoring using multiple sensors on different body positions
  publication-title: Int. Work. Wearable Implant. Body Sens. Netw.
  doi: 10.1109/BSN.2006.24
– volume: 19
  start-page: 1567
  issue: 5
  year: 2015
  ident: 10.1016/j.jbi.2018.09.002_b0205
  article-title: Identifying physical activity profiles in COPD patients using topic models
  publication-title: IEEE J. Biomed. Health Inf.
  doi: 10.1109/JBHI.2015.2432033
– volume: 2
  start-page: 311
  year: 2010
  ident: 10.1016/j.jbi.2018.09.002_b0555
  article-title: Activity recognition using semi-Markov models on real world smart home datasets
  publication-title: Environments
– volume: 36
  start-page: 147
  issue: 2
  year: 2014
  ident: 10.1016/j.jbi.2018.09.002_b0115
  article-title: Monitoring human health behaviour in one’s living environment: a technological review
  publication-title: Med. Eng. Phys.
  doi: 10.1016/j.medengphy.2013.11.010
– volume: 12
  start-page: 20
  issue: 1
  year: 2008
  ident: 10.1016/j.jbi.2018.09.002_b0190
  article-title: Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions
  publication-title: IEEE Trans. Inf. Technol. Biomed.
  doi: 10.1109/TITB.2007.899496
– ident: 10.1016/j.jbi.2018.09.002_b0385
– volume: 15
  start-page: 13159
  issue: 6
  year: 2015
  ident: 10.1016/j.jbi.2018.09.002_b0720
  article-title: MDurance: a novel mobile health system to support trunk endurance assessment
  publication-title: Sensors (Switzerland)
  doi: 10.3390/s150613159
– volume: 61
  start-page: 2262
  issue: 8
  year: 2012
  ident: 10.1016/j.jbi.2018.09.002_b0130
  article-title: Estimation of attitude and external acceleration using inertial sensor measurement during various dynamic conditions
  publication-title: IEEE Trans. Instrum. Meas.
  doi: 10.1109/TIM.2012.2187245
– volume: 4
  start-page: 375
  issue: 4
  year: 1996
  ident: 10.1016/j.jbi.2018.09.002_b0065
  article-title: Detection of static and dynamic activities using uniaxial\naccelerometers
  publication-title: IEEE Trans. Rehabil. Eng.
  doi: 10.1109/86.547939
– volume: 54
  start-page: 313
  issue: 2
  year: 2007
  ident: 10.1016/j.jbi.2018.09.002_b0100
  article-title: Quantification of tremor and bradykinesia in Parkinson’s disease using a novel ambulatory monitoring system
  publication-title: IEEE Trans. Biomed. Eng.
  doi: 10.1109/TBME.2006.886670
– start-page: 1
  year: 2003
  ident: 10.1016/j.jbi.2018.09.002_b0360
  article-title: Segmenting time series: a survey and novel approach
  publication-title: Data Min. Time Ser. Databases
– volume: 18
  start-page: 1253
  issue: 4
  year: 2014
  ident: 10.1016/j.jbi.2018.09.002_b0740
  article-title: Detecting periods of eating during free-living by tracking wrist motion
  publication-title: IEEE J. Biomed. Heal. Inf.
  doi: 10.1109/JBHI.2013.2282471
– volume: 27
  start-page: 497
  issue: 6
  year: 2005
  ident: 10.1016/j.jbi.2018.09.002_b0070
  article-title: A description of an accelerometer-based mobility monitoring technique
  publication-title: Med. Eng. Phys.
  doi: 10.1016/j.medengphy.2004.11.006
– ident: 10.1016/j.jbi.2018.09.002_b0160
  doi: 10.1109/ICSMC.2001.973004
– volume: 14
  start-page: 16181
  year: 2014
  ident: 10.1016/j.jbi.2018.09.002_b0300
  article-title: A lightweight hierarchical activity recognition framework using smartphone sensors
  publication-title: Sensors
  doi: 10.3390/s140916181
– ident: 10.1016/j.jbi.2018.09.002_b0315
  doi: 10.1109/ICCV.2007.4408865
– ident: 10.1016/j.jbi.2018.09.002_b0625
  doi: 10.1145/1409635.1409638
– volume: 7
  start-page: 22
  issue: 2
  year: 2008
  ident: 10.1016/j.jbi.2018.09.002_b0435
  article-title: Rapid prototyping of activity recognition applications
  publication-title: Pervasive Comput. IEEE
  doi: 10.1109/MPRV.2008.36
– volume: 21
  start-page: 660
  issue: 3
  year: 1991
  ident: 10.1016/j.jbi.2018.09.002_b0565
  article-title: “A survey of decision tree classifier methodology
  publication-title: IEEE Trans. Syst. Man, Cybern.
  doi: 10.1109/21.97458
– ident: 10.1016/j.jbi.2018.09.002_b0030
  doi: 10.1145/3102304.3105828
– volume: 9
  start-page: 113
  issue: 2
  year: 2001
  ident: 10.1016/j.jbi.2018.09.002_b0080
  article-title: A reliable gait phase detection system
  publication-title: IEEE Trans. Neural Syst. Rehabil. Eng.
  doi: 10.1109/7333.928571
– volume: 12
  start-page: 1
  issue: 2
  year: 2013
  ident: 10.1016/j.jbi.2018.09.002_b0180
  article-title: Physical activity recognition using multiple sensors embedded in a wearable device
  publication-title: ACM Trans. Embed. Comput. Syst.
  doi: 10.1145/2423636.2423644
– volume: 11
  start-page: 10
  issue: 1
  year: 2009
  ident: 10.1016/j.jbi.2018.09.002_b0575
  article-title: The WEKA data mining software
  publication-title: ACM SIGKDD Explor.
  doi: 10.1145/1656274.1656278
– ident: 10.1016/j.jbi.2018.09.002_b0365
– volume: 33
  start-page: 205
  issue: 2
  year: 2009
  ident: 10.1016/j.jbi.2018.09.002_b0075
  article-title: Fall detection and activity recognition with machine learning
  publication-title: Informatica
– ident: 10.1016/j.jbi.2018.09.002_b0560
  doi: 10.1109/WMVC.2007.12
– volume: 3
  start-page: 50
  year: 2004
  ident: 10.1016/j.jbi.2018.09.002_b0310
  article-title: Inferring activities from interactions with objects
  publication-title: IEEE Pervasive Comput.
  doi: 10.1109/MPRV.2004.7
– volume: 24
  start-page: 961
  issue: 6
  year: 2012
  ident: 10.1016/j.jbi.2018.09.002_b0405
  article-title: A knowledge-driven approach to activity recognition in smart homes
  publication-title: IEEE Trans. Knowl. Data Eng.
  doi: 10.1109/TKDE.2011.51
– ident: 10.1016/j.jbi.2018.09.002_b0330
  doi: 10.1109/EMBC.2014.6944533
– ident: 10.1016/j.jbi.2018.09.002_b0505
  doi: 10.1109/NNSP.1999.788121
– volume: 100
  start-page: 126
  issue: 2
  year: 1985
  ident: 10.1016/j.jbi.2018.09.002_b0005
  article-title: Physical activity, exercise, and physical fitness: definitions and distinctions for health-related research
  publication-title: Public Health Rep.
– ident: 10.1016/j.jbi.2018.09.002_b0605
  doi: 10.1109/IEMBS.2007.4353446
– volume: 52
  start-page: 1478
  issue: 8
  year: 2005
  ident: 10.1016/j.jbi.2018.09.002_b0095
  article-title: A new approach to accurate measurement of uniaxial joint angles based on a combination of accelerometers and gyroscopes
  publication-title: IEEE Trans. Biomed. Eng.
  doi: 10.1109/TBME.2005.851475
– ident: 10.1016/j.jbi.2018.09.002_b0415
  doi: 10.1109/HealthCom.2012.6379453
– volume: 48
  start-page: 1271
  issue: 12
  year: 2010
  ident: 10.1016/j.jbi.2018.09.002_b0245
  article-title: Accelerometer’s position independent physical activity recognition system for long-term activity monitoring in the elderly
  publication-title: Med. Biol. Eng. Comput.
  doi: 10.1007/s11517-010-0701-3
– volume: 59
  start-page: 575
  issue: 3
  year: 2010
  ident: 10.1016/j.jbi.2018.09.002_b0735
  article-title: Reducing drifts in the inertial measurements of wrist and elbow positions
  publication-title: IEEE Trans. Instrum. Meas.
  doi: 10.1109/TIM.2009.2025065
– volume: 14
  start-page: 1166
  issue: 5
  year: 2010
  ident: 10.1016/j.jbi.2018.09.002_b0210
  article-title: A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer
  publication-title: IEEE Trans. Inf. Technol. Biomed.
  doi: 10.1109/TITB.2010.2051955
– start-page: 220
  year: 2003
  ident: 10.1016/j.jbi.2018.09.002_b0450
  article-title: Multi-sensor activity context detection for wearable computing
  publication-title: Ambient Intell.
  doi: 10.1007/978-3-540-39863-9_17
– ident: 10.1016/j.jbi.2018.09.002_b0645
  doi: 10.1109/ISWC.2008.4911590
– volume: 8
  start-page: 14
  issue: 4
  year: 1991
  ident: 10.1016/j.jbi.2018.09.002_b0480
  article-title: Wavelets and signal processing
  publication-title: IEEE Signal Process Mag.
  doi: 10.1109/79.91217
– ident: 10.1016/j.jbi.2018.09.002_b0460
– year: 2015
  ident: 10.1016/j.jbi.2018.09.002_b0675
  article-title: MetaQ: A knowledge-driven framework for context-aware activity recognition combining SPARQL and OWL 2 activity patterns
  publication-title: Pervasive Mob. Comput.
– volume: 59
  start-page: 2635
  issue: 9
  year: 2012
  ident: 10.1016/j.jbi.2018.09.002_b0750
  article-title: Shoulder and elbow joint angle tracking with inertial sensors
  publication-title: IEEE Trans. Biomed. Eng.
  doi: 10.1109/TBME.2012.2208750
– volume: 10
  start-page: 829
  issue: 3
  year: 2013
  ident: 10.1016/j.jbi.2018.09.002_b0745
  article-title: An unsupervised approach for automatic activity recognition based on hidden markov model regression
  publication-title: IEEE Trans. Autom. Sci. Eng.
  doi: 10.1109/TASE.2013.2256349
– ident: 10.1016/j.jbi.2018.09.002_b0280
– volume: 6
  start-page: 28
  issue: 4
  year: 2017
  ident: 10.1016/j.jbi.2018.09.002_b0025
  article-title: Wearable-based human activity recognition using an IoT approach
  publication-title: J. Sens. Actuator Netw.
  doi: 10.3390/jsan6040028
– volume: 21
  start-page: 624
  issue: 4
  year: 2013
  ident: 10.1016/j.jbi.2018.09.002_b0520
  article-title: Quantitative description of the lie-to-sit-to-stand-to-walk transfer by a single body-fixed sensor
  publication-title: IEEE Trans. Neural Syst. Rehabil. Eng.
  doi: 10.1109/TNSRE.2012.2230189
– volume: 6
  start-page: 43
  issue: 1
  year: 2010
  ident: 10.1016/j.jbi.2018.09.002_b0630
  article-title: Object relevance weight pattern mining for activity recognition and segmentation
  publication-title: Pervasive Mob. Comput.
  doi: 10.1016/j.pmcj.2009.10.004
– volume: 16
  start-page: 82
  issue: 1
  year: 2008
  ident: 10.1016/j.jbi.2018.09.002_b0140
  article-title: Upper-extremity stroke therapy task discrimination using motion sensors and electromyography
  publication-title: IEEE Trans. Neural Syst. Rehabil. Eng.
  doi: 10.1109/TNSRE.2007.914454
– start-page: 1
  year: 1997
  ident: 10.1016/j.jbi.2018.09.002_b0510
  article-title: Between- and within-groups principal components analyses
  publication-title: Work. Pap.
– volume: 59
  start-page: 687
  issue: 3
  year: 2012
  ident: 10.1016/j.jbi.2018.09.002_b0155
  article-title: Multisensor data fusion for physical activity assessment
  publication-title: IEEE Trans. Biomed. Eng.
  doi: 10.1109/TBME.2011.2178070
– ident: 10.1016/j.jbi.2018.09.002_b0040
  doi: 10.1109/PERCOM.2015.7146519
– ident: 10.1016/j.jbi.2018.09.002_b0035
– volume: 9
  start-page: 1
  issue: 1
  year: 2012
  ident: 10.1016/j.jbi.2018.09.002_b0610
  article-title: Multimodal recognition of reading activity in transit using body-worn sensors
  publication-title: ACM Trans. Appl. Percept.
  doi: 10.1145/2134203.2134205
– volume: 2
  start-page: 253
  issue: 3
  year: 2010
  ident: 10.1016/j.jbi.2018.09.002_b0680
  article-title: Activity recognition using temporal evidence theory
  publication-title: J. Ambient Intell. Smart Environ.
  doi: 10.3233/AIS-2010-0071
– volume: 2194
  issue: c
  year: 2015
  ident: 10.1016/j.jbi.2018.09.002_b0235
  article-title: Wearable monitoring of physical functioning and disability changes, circadian rhythms and sleep patterns in nursing home residents
  publication-title: IEEE J. Biomed. Heal. Inf.
– volume: 10
  start-page: 119
  issue: 1
  year: 2006
  ident: 10.1016/j.jbi.2018.09.002_b0570
  article-title: Activity classification using realistic data from wearable sensors
  publication-title: IEEE Trans. Inf. Technol. Biomed.
  doi: 10.1109/TITB.2005.856863
– volume: 37
  start-page: 1067
  issue: 6
  year: 2007
  ident: 10.1016/j.jbi.2018.09.002_b0285
  article-title: Survey of wireless indoor positioning techniques and systems
  publication-title: IEEE Trans. Syst. Man, Cybern. Part C Appl. Rev.
  doi: 10.1109/TSMCC.2007.905750
– ident: 10.1016/j.jbi.2018.09.002_b0260
– ident: 10.1016/j.jbi.2018.09.002_b0290
  doi: 10.1145/1497185.1497223
– volume: 4717
  start-page: 483
  year: 2007
  ident: 10.1016/j.jbi.2018.09.002_b0265
  article-title: A long-term evaluation of sensing modalities for activity recognition
– volume: 15
  start-page: 667
  issue: 7
  year: 2011
  ident: 10.1016/j.jbi.2018.09.002_b0705
  article-title: Accurate monitoring of human physical activity levels for medical diagnosis and monitoring using off-the-shelf cellular handsets
  publication-title: Pers. Ubiquitous Comput.
  doi: 10.1007/s00779-010-0345-1
– volume: 12
  start-page: 112
  issue: 1
  year: 2004
  ident: 10.1016/j.jbi.2018.09.002_b0125
  article-title: Inclination measurement of human movement using a 3-D accelerometer with autocalibration
  publication-title: IEEE Trans. Neural Syst. Rehabil. Eng.
  doi: 10.1109/TNSRE.2003.822759
– ident: 10.1016/j.jbi.2018.09.002_b0585
– ident: 10.1016/j.jbi.2018.09.002_b0640
  doi: 10.1145/279943.279962
– volume: 6
  start-page: 1
  issue: 2
  year: 2010
  ident: 10.1016/j.jbi.2018.09.002_b0525
  article-title: Using mobile phones to determine transportation modes
  publication-title: ACM Trans. Sens. Netw.
  doi: 10.1145/1689239.1689243
– volume: 34
  start-page: 607
  issue: 5
  year: 1997
  ident: 10.1016/j.jbi.2018.09.002_b0530
  article-title: Assessment of posture and motion by multichannel piezoresistive accelerometer recordings
  publication-title: Psychophysiology
  doi: 10.1111/j.1469-8986.1997.tb01747.x
– volume: 18
  start-page: 1
  issue: c
  year: 2013
  ident: 10.1016/j.jbi.2018.09.002_b0230
  article-title: Designing a robust activity recognition framework for health and exergaming using wearable sensors
  publication-title: IEEE J. Biomed. Heal. Inf.
– volume: 13
  start-page: 1133
  issue: 9
  year: 2012
  ident: 10.1016/j.jbi.2018.09.002_b0375
  article-title: Activity recognition on streaming sensor data
  publication-title: Pervasive Mob. Comput.
– volume: 3001
  start-page: 158
  year: 2004
  ident: 10.1016/j.jbi.2018.09.002_b0695
  article-title: Activity recognition in the home using simple and ubiquitous sensors
  publication-title: Pervasive Comput.
  doi: 10.1007/978-3-540-24646-6_10
– volume: 59
  start-page: 3177
  issue: 12 PART2
  year: 2012
  ident: 10.1016/j.jbi.2018.09.002_b0200
  article-title: SoM: A smart sensor for human activity monitoring and assisted healthy ageing
  publication-title: IEEE Trans. Biomed. Eng.
  doi: 10.1109/TBME.2012.2206384
– ident: 10.1016/j.jbi.2018.09.002_b0710
  doi: 10.1145/2493432.2493454
– ident: 10.1016/j.jbi.2018.09.002_b0390
  doi: 10.1109/ISWC.2012.23
– ident: 10.1016/j.jbi.2018.09.002_b0055
– volume: 2194
  start-page: 1
  issue: c
  year: 2014
  ident: 10.1016/j.jbi.2018.09.002_b0135
  article-title: Estimating energy expenditure using body-worn accelerometers: a comparison of methods, sensors number and positioning
  publication-title: IEEE J. Biomed. Heal. Inf.
– volume: 10
  start-page: 155
  year: 2014
  ident: 10.1016/j.jbi.2018.09.002_b0380
  article-title: Dynamic sensor data segmentation for real-time knowledge-driven activity recognition
  publication-title: Pervasive Mob. Comput.
  doi: 10.1016/j.pmcj.2012.11.004
– ident: 10.1016/j.jbi.2018.09.002_b0275
  doi: 10.1109/KICSS.2012.26
– start-page: 23
  issue: April
  year: 2015
  ident: 10.1016/j.jbi.2018.09.002_b0355
  article-title: A portable fall detection and alerting system based on k-NN algorithm and remote medicine
  publication-title: China Commun.
  doi: 10.1109/CC.2015.7114066
– volume: 16
  start-page: 31
  issue: 1
  year: 2002
  ident: 10.1016/j.jbi.2018.09.002_b0085
  article-title: Knee, and hip kinetics during normal stair climbing
  publication-title: Gait Post.
  doi: 10.1016/S0966-6362(01)00201-6
– volume: 22
  start-page: 285
  issue: 4
  year: 2000
  ident: 10.1016/j.jbi.2018.09.002_b0490
  article-title: Classification of waist-acceleration signals in a continuous walking record
  publication-title: Med. Eng. Phys.
  doi: 10.1016/S1350-4533(00)00041-2
– volume: 18
  start-page: 369
  issue: 4
  year: 2010
  ident: 10.1016/j.jbi.2018.09.002_b0225
  article-title: Multimodal physical activity recognition by fusing temporal and cepstral information
  publication-title: IEEE Trans. Neural Syst. Rehabil. Eng.
  doi: 10.1109/TNSRE.2010.2053217
– ident: 10.1016/j.jbi.2018.09.002_b0670
– volume: 9
  start-page: 48
  issue: 1
  year: 2010
  ident: 10.1016/j.jbi.2018.09.002_b0545
  article-title: Human activity recognition and pattern discovery
  publication-title: Pervasive Comput. IEEE
  doi: 10.1109/MPRV.2010.7
– ident: 10.1016/j.jbi.2018.09.002_b0540
– volume: 9
  start-page: 293
  issue: 3
  year: 1999
  ident: 10.1016/j.jbi.2018.09.002_b0580
  article-title: Least squares support vector machine classifiers
  publication-title: Neural Process. Lett.
  doi: 10.1023/A:1018628609742
– ident: 10.1016/j.jbi.2018.09.002_b0600
  doi: 10.1109/ISSNIP.2005.1595593
– volume: 19
  start-page: 47
  year: 2014
  ident: 10.1016/j.jbi.2018.09.002_b0730
  article-title: KCAR: A knowledge-driven approach for concurrent activity recognition
  publication-title: Pervasive Mob. Comput.
  doi: 10.1016/j.pmcj.2014.02.003
– volume: 19
  start-page: 44
  issue: 1
  year: 2015
  ident: 10.1016/j.jbi.2018.09.002_b0325
  article-title: A smart phone-based pocket fall accident detection, positioning, and rescue system
  publication-title: IEEE J. Biomed. Heal. Inf.
  doi: 10.1109/JBHI.2014.2328593
– volume: 9
  start-page: 203
  issue: 2
  year: 2013
  ident: 10.1016/j.jbi.2018.09.002_b0335
  article-title: The mobile fitness coach: towards individualized skill assessment using personalized mobile devices
  publication-title: Pervasive Mob. Comput.
  doi: 10.1016/j.pmcj.2012.06.002
– volume: 17
  start-page: 675
  issue: 4
  year: 2013
  ident: 10.1016/j.jbi.2018.09.002_b0255
  article-title: Activity logging using lightweight classification techniques in mobile devices
  publication-title: Pers. Ubiquitous Comput.
  doi: 10.1007/s00779-012-0515-4
– ident: 10.1016/j.jbi.2018.09.002_b0440
  doi: 10.1109/ISWC.2009.23
– volume: 22
  start-page: 287
  issue: 4
  year: 2005
  ident: 10.1016/j.jbi.2018.09.002_b0090
  article-title: Stair climbing detection during daily physical activity using a miniature gyroscope
  publication-title: Gait Post.
  doi: 10.1016/j.gaitpost.2004.08.008
– start-page: 1
  year: 2008
  ident: 10.1016/j.jbi.2018.09.002_b0660
  article-title: Fast and scalable training of semisupervised crfs with application to activity recognition
  publication-title: Adv. Neural Inf.
SSID ssj0011556
Score 2.5629392
SecondaryResourceType review_article
Snippet •It summarises the state-of-the-art in traditional PARM methodologies for healthcare.•It identifies new research trends and challenges of PARM studies in IoT...
Due to importantly beneficial effects on physical and mental health and strong association with many rehabilitation programs, Physical Activity Recognition and...
SourceID proquest
pubmed
crossref
elsevier
SourceType Aggregation Database
Index Database
Enrichment Source
Publisher
StartPage 138
SubjectTerms Internet of Things
Physical activity monitoring
Physical activity recognition
Sensor-based
Systematic review
Title Examining sensor-based physical activity recognition and monitoring for healthcare using Internet of Things: A systematic review
URI https://dx.doi.org/10.1016/j.jbi.2018.09.002
https://www.ncbi.nlm.nih.gov/pubmed/30267895
https://www.proquest.com/docview/2114699717
Volume 87
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3faxQxEB5qBVGk6PnrrB4j-CSst91Ncru-XUvLqbQvWri3kGyyesXuHu0VfBL_dGc22dWC9sHHDRsSMpPMJPN9MwCvy6IuPXnCibSeKTneJTatfOKEEaJSvvQ1Pw0cn6jFqfiwlMstOOi5MAyrjGd_ONO70zq2TONqTter1fTTHtc0EEqQUpJaqSWdw7koOhLfcn-IJJC9VCFnasYwRtFHNjuM15ldMbqr6FKdxpeVv9imf_menQ06egA70XnEeZjfQ9jyzQju_ZFScAR3jmOwfAT3w5McBqbRI_h5-N2cd_Ug8JIur-1FwibM4TqKCpnjwKUkcEAVtQ2axuF5t_F5ACQfF78OmDFk2PwXDM-KfoNtjaEQ6Duc4-8k0RgIMo_h9Ojw88EiiQUYkkqobJNIUdNthsmojtykLHdeOsHIPiukl7lTTIutqswL5YqZtc7VLk1rW8mZdaKo8iew3bSNfwaYmVwZW5C_ZaSwRWoyO3PG1Z6ajJVuDGm_9LqK2cm5SMY33cPQzjRJS7O0dFpqktYY3gxd1iE1x00_i16e-pp-aTIdN3V71cte077jYIppfHt1qTOmc5cl3YbH8DQoxTCLnMt6FaV8_n-D7sJd_gqMxxewvbm48i_J9dnYCdx6-2NvArfn7z8uTiadpv8CKoEGFg
linkProvider Elsevier
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwEB6VIvFQhWB5bctjkDghRRsS25twa6tWW-j2QivtzbJjB7aiyardShz56czETgAJeuDqxLLlGXvGnu-bAXhbFnXpyRNOpPVMyfEusWnlEyeMEJXypa_5aWB-omZn4uNCLjZgv-fCMKwynv3hTO9O69gyias5WS2Xk8_vuaaBUIKUktRKLW7BbfIGFKv20WJvCCWQwVQhaWrGOEbRhzY7kNe5XTK8q-hyncanlb8Yp385n50ROnwID6L3iLthgo9gwzcjuP9bTsER3JnHaPkItsKbHAaq0WP4cfDdXHQFIfCKbq_tZcI2zOEqygqZ5MC1JHCAFbUNmsbhRbfzeQAkJxe_DqAxZNz8Fwzvin6NbY2hEugH3MVfWaIxMGSewNnhwen-LIkVGJJKqGydSFHTdYbZqI78pCx3XjrB0D4rpJe5U8yLrarMC-WKqbXO1S5Na1vJqXWiqPKnsNm0jX8OmJlcGVuQw2WksEVqMjt1xtWemoyVbgxpv_S6iunJuUrGN93j0M41SUuztHRaapLWGN4NXVYhN8dNP4tenvoPBdNkO27q9qaXvaaNx9EU0_j2-kpnzOcuS7oOj-FZUIphFjnX9SpKuf1_g76Gu7PT-bE-Pjr5tAP3-EugP76AzfXltX9JftDavur0_CcLJAax
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=Examining+sensor-based+physical+activity+recognition+and+monitoring+for+healthcare+using+Internet+of+Things%3A+A+systematic+review&rft.jtitle=Journal+of+biomedical+informatics&rft.au=Qi%2C+Jun&rft.au=Yang%2C+Po&rft.au=Waraich%2C+Atif&rft.au=Deng%2C+Zhikun&rft.date=2018-11-01&rft.issn=1532-0480&rft.eissn=1532-0480&rft.volume=87&rft.spage=138&rft_id=info:doi/10.1016%2Fj.jbi.2018.09.002&rft.externalDBID=NO_FULL_TEXT
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1532-0464&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1532-0464&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1532-0464&client=summon