Physical Activity Recognition From Smartphone Accelerometer Data for User Context Awareness Sensing
Physical activity recognition of everyday activities such as sitting, standing, laying, walking, and jogging was performed, through the use of smartphone accelerometer data. Activity classification was done on a remote server through the use of machine learning algorithms, data was received from the...
Uloženo v:
| Vydáno v: | IEEE transactions on systems, man, and cybernetics. Systems Ročník 47; číslo 12; s. 3142 - 3149 |
|---|---|
| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
New York
IEEE
01.12.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Témata: | |
| ISSN: | 2168-2216, 2168-2232 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Physical activity recognition of everyday activities such as sitting, standing, laying, walking, and jogging was performed, through the use of smartphone accelerometer data. Activity classification was done on a remote server through the use of machine learning algorithms, data was received from the smartphone wirelessly. The smartphone was placed in the subject's trouser pocket while data was gathered. A large sample set was used to train the classifiers and then a test set was used to verify the algorithm accuracies. Ten different classifier algorithm configurations were evaluated to determine which performed best overall, as well as, which algorithms performed best for specific activity classes. Based on the results obtained, very accurate predictions could be made for offline activity recognition. The kNN and kStar algorithms both obtained an overall accuracy of 99.01%. |
|---|---|
| AbstractList | Physical activity recognition of everyday activities such as sitting, standing, laying, walking, and jogging was performed, through the use of smartphone accelerometer data. Activity classification was done on a remote server through the use of machine learning algorithms, data was received from the smartphone wirelessly. The smartphone was placed in the subject's trouser pocket while data was gathered. A large sample set was used to train the classifiers and then a test set was used to verify the algorithm accuracies. Ten different classifier algorithm configurations were evaluated to determine which performed best overall, as well as, which algorithms performed best for specific activity classes. Based on the results obtained, very accurate predictions could be made for offline activity recognition. The kNN and kStar algorithms both obtained an overall accuracy of 99.01%. |
| Author | Wannenburg, Johan Malekian, Reza |
| Author_xml | – sequence: 1 givenname: Johan surname: Wannenburg fullname: Wannenburg, Johan email: johan.wannenburg@gmail.com organization: Dept. of Electr., Electron., & Comput. Eng., Univ. of Pretoria, Pretoria, South Africa – sequence: 2 givenname: Reza surname: Malekian fullname: Malekian, Reza email: reza.malekian@ieee.org organization: Dept. of Electr., Electron., & Comput. Eng., Univ. of Pretoria, Pretoria, South Africa |
| BookMark | eNp9kE1PAjEQhhujiYj8AOOliWew7W67u0eyippgNALnTSmzULK02BaVf283EA4ePHU6877z8Vyhc2MNIHRDyYBSUtxPJ6_lgBEqBowLxklxhjqMirzPWMLOTzEVl6jn_ZoQQlkuEiI6SL2v9l4r2eChCvpLhz3-AGWXRgdtDR45u8GTjXRhu4ozo0hBAzEJARx-kEHi2jo88_FXWhPgJ-Dht3RgwHs8AeO1WV6ji1o2HnrHt4tmo8dp-dwfvz29lMNxXyW8CH0JaVrMOSVikamcy7zmitTFgkEd92dEzpVcgFA5SWOpKLgkiZJ8zvI6FVmiki66O_TdOvu5Ax-qtd05E0dWjGZpKhIedV2UHVTKWe8d1JXSQbbXBid1U1FStVCrFmrVQq2OUKOT_nFunY5s9v96bg8eDQAnfZZmIhdF8guBWIVY |
| CODEN | ITSMFE |
| CitedBy_id | crossref_primary_10_1016_j_enbuild_2021_111699 crossref_primary_10_1109_ACCESS_2020_2964237 crossref_primary_10_1007_s12652_017_0592_3 crossref_primary_10_1109_JSEN_2020_3023964 crossref_primary_10_3390_s18113604 crossref_primary_10_1016_j_ijhcs_2021_102650 crossref_primary_10_1093_gigascience_giab044 crossref_primary_10_1109_JSEN_2019_2911204 crossref_primary_10_1109_JIOT_2020_3033173 crossref_primary_10_1007_s11277_020_07903_0 crossref_primary_10_1177_1550147719894532 crossref_primary_10_1109_JSEN_2023_3282171 crossref_primary_10_1016_j_future_2023_01_006 crossref_primary_10_3390_s18061965 crossref_primary_10_1109_ACCESS_2023_3338137 crossref_primary_10_1007_s11042_022_12261_z crossref_primary_10_1109_ACCESS_2021_3111323 crossref_primary_10_3390_s18092822 crossref_primary_10_1109_JIOT_2019_2936580 crossref_primary_10_1109_TSMC_2018_2824903 crossref_primary_10_1007_s41870_024_02305_y crossref_primary_10_1016_j_procs_2020_10_007 crossref_primary_10_1038_s41746_021_00514_4 crossref_primary_10_3390_s20185114 crossref_primary_10_1109_ACCESS_2017_2775180 crossref_primary_10_1109_JERM_2018_2858562 crossref_primary_10_1007_s10044_021_00995_9 crossref_primary_10_1049_iet_net_2018_5122 crossref_primary_10_1109_JSYST_2018_2890571 crossref_primary_10_1007_s10796_020_09992_5 crossref_primary_10_1109_ACCESS_2022_3140373 crossref_primary_10_1007_s11036_024_02362_4 crossref_primary_10_1038_s41598_024_63195_5 crossref_primary_10_1016_j_neunet_2021_11_011 crossref_primary_10_1109_TIP_2021_3086590 crossref_primary_10_3390_info9040094 crossref_primary_10_1016_j_measurement_2018_01_027 crossref_primary_10_1109_TSMC_2023_3292146 crossref_primary_10_1007_s12652_018_0766_7 crossref_primary_10_1016_j_cie_2020_106953 crossref_primary_10_3390_s21165479 crossref_primary_10_1080_10400435_2023_2177775 crossref_primary_10_1109_COMST_2019_2914030 crossref_primary_10_1016_j_future_2022_03_006 crossref_primary_10_1145_3569484 crossref_primary_10_3390_s22239176 crossref_primary_10_3390_s22239451 crossref_primary_10_1002_spe_2846 crossref_primary_10_1155_2022_4356974 crossref_primary_10_1109_ACCESS_2021_3105581 crossref_primary_10_1109_TSMC_2020_2970905 crossref_primary_10_1109_ACCESS_2020_3037715 crossref_primary_10_1109_TNSRE_2024_3366907 crossref_primary_10_3390_s21144638 crossref_primary_10_1016_j_robot_2022_104353 crossref_primary_10_1109_JSEN_2018_2844122 crossref_primary_10_1587_transinf_2020EDP7228 crossref_primary_10_3390_s18030838 crossref_primary_10_3390_s19194242 crossref_primary_10_1109_TSMC_2018_2791603 crossref_primary_10_3390_s23115155 crossref_primary_10_1016_j_enbuild_2023_113216 crossref_primary_10_3390_s20051523 crossref_primary_10_1016_j_enbenv_2023_09_001 crossref_primary_10_3389_frobt_2021_749274 crossref_primary_10_3390_s19163468 crossref_primary_10_1109_JIOT_2023_3282680 crossref_primary_10_1109_JIOT_2022_3209970 crossref_primary_10_1109_TSMC_2018_2819026 crossref_primary_10_3390_s20154117 crossref_primary_10_1088_1742_6596_1341_4_042002 crossref_primary_10_1109_TSMC_2019_2912206 crossref_primary_10_1016_j_enbuild_2023_113808 crossref_primary_10_1109_ACCESS_2019_2913393 crossref_primary_10_3390_s21103551 crossref_primary_10_1155_2022_9540033 crossref_primary_10_1007_s12652_017_0668_0 crossref_primary_10_1007_s11042_020_10046_w crossref_primary_10_1109_JSEN_2022_3153610 crossref_primary_10_1109_MITS_2019_2903551 crossref_primary_10_1109_TITS_2024_3387834 crossref_primary_10_3390_electronics9030509 crossref_primary_10_3390_s18041061 crossref_primary_10_1016_j_neucom_2021_10_044 crossref_primary_10_1007_s11042_021_11410_0 crossref_primary_10_3390_app9224833 crossref_primary_10_3390_s20236786 crossref_primary_10_1109_TSMC_2021_3102834 crossref_primary_10_1109_JSEN_2017_2782492 crossref_primary_10_3390_s20082216 crossref_primary_10_1016_j_eswa_2024_123143 |
| Cites_doi | 10.1016/S0966-6362(01)00199-0 10.1109/TITB.2005.856864 10.1016/j.procs.2014.07.009 10.3390/s100201154 10.1109/TBME.2003.812189 10.1016/j.medengphy.2011.05.002 10.1109/ICSMC.2009.5346042 10.1007/s12668-013-0088-3 10.1016/j.gaitpost.2006.09.012 10.3390/s100807772 10.1145/1964897.1964918 10.1016/j.eswa.2014.04.018 10.1109/CIDM.2013.6597218 10.1145/1656274.1656278 10.1109/TBME.2011.2160723 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2017 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2017 |
| DBID | 97E RIA RIE AAYXX CITATION 7SC 7SP 7TB 8FD FR3 H8D JQ2 L7M L~C L~D |
| DOI | 10.1109/TSMC.2016.2562509 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE/IET Electronic Library CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Mechanical & Transportation Engineering Abstracts Technology Research Database Engineering Research Database Aerospace Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | CrossRef Aerospace Database Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Engineering Research Database Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Aerospace Database |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 2168-2232 |
| EndPage | 3149 |
| ExternalDocumentID | 10_1109_TSMC_2016_2562509 7476869 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: National Research Foundation grantid: 90908 funderid: 10.13039/501100001321 – fundername: Research Development Programme grantid: AOX220 |
| GroupedDBID | 0R~ 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFS ACIWK AGQYO AGSQL AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD HZ~ IFIPE IPLJI JAVBF M43 O9- OCL PQQKQ RIA RIE RNS AAYXX CITATION 7SC 7SP 7TB 8FD FR3 H8D JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c359t-ae449b5106d7c85a8f5c0f9d2ef16820abcade6c8048f5995a03ca5b28f4673c3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 125 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000416262600004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2168-2216 |
| IngestDate | Sun Nov 09 08:39:00 EST 2025 Sat Nov 29 03:45:26 EST 2025 Tue Nov 18 22:31:27 EST 2025 Tue Aug 26 16:38:57 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 12 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c359t-ae449b5106d7c85a8f5c0f9d2ef16820abcade6c8048f5995a03ca5b28f4673c3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-2763-8085 |
| PQID | 2174463567 |
| PQPubID | 75739 |
| PageCount | 8 |
| ParticipantIDs | crossref_citationtrail_10_1109_TSMC_2016_2562509 proquest_journals_2174463567 crossref_primary_10_1109_TSMC_2016_2562509 ieee_primary_7476869 |
| PublicationCentury | 2000 |
| PublicationDate | 2017-12-01 |
| PublicationDateYYYYMMDD | 2017-12-01 |
| PublicationDate_xml | – month: 12 year: 2017 text: 2017-12-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | IEEE transactions on systems, man, and cybernetics. Systems |
| PublicationTitleAbbrev | TSMC |
| PublicationYear | 2017 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref13 ref12 ref15 ref14 kansiz (ref1) 2013; 2 ref20 ref11 mahmood (ref21) 2013; 15 ref10 bao (ref7) 2004 ref2 ref17 ref19 ravi (ref16) 2005; 3 ref8 ref9 ref4 ref3 ref6 ref5 morales (ref18) 2014; 2014 |
| References_xml | – ident: ref10 doi: 10.1016/S0966-6362(01)00199-0 – ident: ref17 doi: 10.1109/TITB.2005.856864 – volume: 3 start-page: 1541 year: 2005 ident: ref16 article-title: Activity recognition from accelerometer data publication-title: Proc Nat Conf Artif Intell – start-page: 1 year: 2004 ident: ref7 article-title: Activity recognition from user-annotated acceleration data publication-title: Proc Pervasive – volume: 2014 start-page: 1 year: 2014 ident: ref18 article-title: Human activity recognition by smartphones regardless of device orientation publication-title: Proc IS&T/SPIE Electron Imag – ident: ref3 doi: 10.1016/j.procs.2014.07.009 – ident: ref9 doi: 10.3390/s100201154 – ident: ref13 doi: 10.1109/TBME.2003.812189 – ident: ref15 doi: 10.1016/j.medengphy.2011.05.002 – ident: ref19 doi: 10.1109/ICSMC.2009.5346042 – ident: ref8 doi: 10.1007/s12668-013-0088-3 – volume: 2 start-page: 796 year: 2013 ident: ref1 article-title: Selection of time-domain features for fall detection based on supervised learning publication-title: Proc World Congr Eng Comput Sci – ident: ref11 doi: 10.1016/j.gaitpost.2006.09.012 – ident: ref4 doi: 10.3390/s100807772 – ident: ref6 doi: 10.1145/1964897.1964918 – ident: ref5 doi: 10.1016/j.eswa.2014.04.018 – ident: ref2 doi: 10.1109/CIDM.2013.6597218 – ident: ref12 doi: 10.1109/TITB.2005.856864 – ident: ref20 doi: 10.1145/1656274.1656278 – ident: ref14 doi: 10.1109/TBME.2011.2160723 – volume: 15 start-page: 107 year: 2013 ident: ref21 article-title: Intrusion detection system based on K-star classifier and feature set reduction publication-title: Int Org Sci Res J Comput Eng |
| SSID | ssj0001286306 |
| Score | 2.4373841 |
| Snippet | Physical activity recognition of everyday activities such as sitting, standing, laying, walking, and jogging was performed, through the use of smartphone... |
| SourceID | proquest crossref ieee |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 3142 |
| SubjectTerms | Accelerometer Accelerometers Activity recognition Algorithms Classifiers Exercise Feature extraction Hidden Markov models Machine learning Machine learning algorithms Sensors Smart phones smartphone Smartphones |
| Title | Physical Activity Recognition From Smartphone Accelerometer Data for User Context Awareness Sensing |
| URI | https://ieeexplore.ieee.org/document/7476869 https://www.proquest.com/docview/2174463567 |
| Volume | 47 |
| WOSCitedRecordID | wos000416262600004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 2168-2232 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001286306 issn: 2168-2216 databaseCode: RIE dateStart: 20130101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LSwMxEB5a8aAHX1WsL3LwJG673ezmcSxq8aCl2BZ6W7LZRATbSrs-fr6ZdFsKiuBl2cMEwnxJZpJ8-QbgkhthM5UhFUfmQawjE2Rc2oBbLY3MRB5qX7XkgXe7YjSSvQpcr97CGGM8-cw08Nff5edT_Y5HZU2X-jLBZBWqnLPFW6218xTBqC-lGbWYA999y0vMViibg_7jDfK4WCPCjB_ph2thyNdV-bEY-wjT2f1f3_Zgp8wkSXsB_T5UzOQAttf0BWugeyUKpK0XVSLI05IwNJ2Qzmw6Jv2xGzxIUDfOSLsghPoFztnkVhWKuJSWDN0oJV7F6qsg7U98PeaWR9JH6vvk-RCGnbvBzX1QVlUINE1kESgTxzJzU5HlXItECZvo0Mo8MtY5LgpVhsR8poWb2xblyFRItUqySFi3qFJNj2Bj4jp1DCRTrZxKk3CjaKxiIXMk2SiWtKiNqNJ1CJdOTnUpOY6VL15Tv_UIZYq4pIhLWuJSh6tVk7eF3sZfxjUEYmVYYlCHsyWSaTkj5yluvWIU4-Mnv7c6ha0IQ7anqpzBRjF7N-ewqT-Kl_nswg-2b2zq0zc |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NSwMxEB20CurBb7FaNQdP4uo22Y_kWKqlYluKreBtyWYTEbSVdqv-fDPpthQUwcuyhwmEeUlmkry8ATiPNTepTJGKIzIvUFR7aSyMFxsltEh55itXtaQVdzr86Ul0l-By_hZGa-3IZ_oKf91dfjZUEzwqu7apb8QjsQwrYRBQf_paa-FEhUfMFdOk1cjCb7_FNWbVF9f9XruOTK7oimLOjwTEhUDkKqv8WI5djGls_a9327BZ5JKkNgV_B5b0YBc2FhQG90B1CxxITU3rRJCHGWVoOCCN0fCN9N7s8EGKurZGyoYhVDCw7iY3MpfEJrXk0Y5T4nSsvnJS-8T3Y3aBJD0kvw-e9-GxcduvN72iroKnWChyT-ogEKmdjFEWKx5KbkLlG5FRbazjqC9TpOZHitvZbVCQTPpMyTCl3NhllSl2AKWB7dQhkFRWMyZ0GGvJAhlwkSHNRkZhlRnKpCqDP3NyogrRcax98Zq4zYcvEsQlQVySApcyXMybvE8VN_4y3kMg5oYFBmWozJBMijk5TnDzFaAcX3z0e6szWGv2262kdde5P4Z1igHcEVcqUMpHE30Cq-ojfxmPTt3A-waRztZ- |
| 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=Physical+Activity+Recognition+From+Smartphone+Accelerometer+Data+for+User+Context+Awareness+Sensing&rft.jtitle=IEEE+transactions+on+systems%2C+man%2C+and+cybernetics.+Systems&rft.au=Wannenburg%2C+Johan&rft.au=Malekian%2C+Reza&rft.date=2017-12-01&rft.pub=The+Institute+of+Electrical+and+Electronics+Engineers%2C+Inc.+%28IEEE%29&rft.issn=2168-2216&rft.eissn=2168-2232&rft.volume=47&rft.issue=12&rft.spage=3142&rft_id=info:doi/10.1109%2FTSMC.2016.2562509&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2168-2216&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2168-2216&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2168-2216&client=summon |