Loading…
Context-Aware Human Activity Recognition (CAHAR) in-the-Wild Using Smartphone Accelerometer
Smartphones are a promising platform for continuous monitoring of human behavior. However, the ability to capture people's behavioral patterns in-the-wild is a challenge, as the user's behavior and physical activities can vary, given the variability of settings and environments. Modeling a...
Saved in:
| Published in: | IEEE sensors journal 2020-04, Vol.20 (8), p.4361-4371 |
|---|---|
| Main Authors: | , , , , |
| Format: | Article |
| Language: | English |
| Subjects: | |
| Citations: | Items that this one cites Items that cite this one |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| cited_by | cdi_FETCH-LOGICAL-c384t-237e8c01a0de5551992847b7eac09bac750735e650a9e2b15259a9553f71e99a3 |
|---|---|
| cites | cdi_FETCH-LOGICAL-c384t-237e8c01a0de5551992847b7eac09bac750735e650a9e2b15259a9553f71e99a3 |
| container_end_page | 4371 |
| container_issue | 8 |
| container_start_page | 4361 |
| container_title | IEEE sensors journal |
| container_volume | 20 |
| creator | Asim, Yusra Azam, Muhammad Awais Ehatisham-ul-Haq, Muhammad Naeem, Usman Khalid, Asra |
| description | Smartphones are a promising platform for continuous monitoring of human behavior. However, the ability to capture people's behavioral patterns in-the-wild is a challenge, as the user's behavior and physical activities can vary, given the variability of settings and environments. Modeling and understanding of human activity in-the-wild must not overlook a user's behavioral context, which is just as crucial as recognizing the range of physical activities. The work in this paper presents a novel framework for context-aware human activity recognition by incorporating human behavioral contexts with physical activities. The proposed framework utilizes a series of machine learning classifiers to validate the efficiency of the proposed method. |
| doi_str_mv | 10.1109/JSEN.2020.2964278 |
| format | article |
| fullrecord | <record><control><sourceid>proquest_ieee_</sourceid><recordid>TN_cdi_proquest_journals_2379362031</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>8950427</ieee_id><sourcerecordid>2379362031</sourcerecordid><originalsourceid>FETCH-LOGICAL-c384t-237e8c01a0de5551992847b7eac09bac750735e650a9e2b15259a9553f71e99a3</originalsourceid><addsrcrecordid>eNo9kE9PAjEQxRujiYh-AONlEy96KPbPlm6PG4KiIZqARBMPTSkDlECL3aLy7d0NxtNMMu-9efkhdElJh1Ki7p7G_ecOI4x0mOrmTBZHqEWFKDCVeXHc7JzgnMv3U3RWVStCqJJCttBHL_gEPwmX3yZCNthtjM9Km9yXS_tsBDYsvEsu-OymVw7K0W3mPE5LwG9uPcsmlfOLbLwxMW2XwUPttLCGGDaQIJ6jk7lZV3DxN9toct9_7Q3w8OXhsVcOseVFnjDjEgpLqCEzEEJQpViRy6kEY4maGisFkVxAVxCjgE2pYEIZJQSfSwpKGd5G14fcbQyfO6iSXoVd9PVLXWcr3mWE01pFDyobQ1VFmOttdHXzvaZENwx1w1A3DPUfw9pzdfA4APjXF0qQ-sx_AU4wa_g</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2379362031</pqid></control><display><type>article</type><title>Context-Aware Human Activity Recognition (CAHAR) in-the-Wild Using Smartphone Accelerometer</title><source>IEEE Xplore (Online service)</source><creator>Asim, Yusra ; Azam, Muhammad Awais ; Ehatisham-ul-Haq, Muhammad ; Naeem, Usman ; Khalid, Asra</creator><creatorcontrib>Asim, Yusra ; Azam, Muhammad Awais ; Ehatisham-ul-Haq, Muhammad ; Naeem, Usman ; Khalid, Asra</creatorcontrib><description>Smartphones are a promising platform for continuous monitoring of human behavior. However, the ability to capture people's behavioral patterns in-the-wild is a challenge, as the user's behavior and physical activities can vary, given the variability of settings and environments. Modeling and understanding of human activity in-the-wild must not overlook a user's behavioral context, which is just as crucial as recognizing the range of physical activities. The work in this paper presents a novel framework for context-aware human activity recognition by incorporating human behavioral contexts with physical activities. The proposed framework utilizes a series of machine learning classifiers to validate the efficiency of the proposed method.</description><identifier>ISSN: 1530-437X</identifier><identifier>EISSN: 1558-1748</identifier><identifier>DOI: 10.1109/JSEN.2020.2964278</identifier><identifier>CODEN: ISJEAZ</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>accelerometer ; Accelerometers ; Activity recognition ; Behavior ; behavioral context ; Biomedical monitoring ; context-aware ; Environment models ; Feature extraction ; Human activity recognition ; Human performance ; Machine learning ; Sensor fusion ; smartphone ; Smartphones ; Support vector machines ; ubiquitous computing ; Wearable sensors</subject><ispartof>IEEE sensors journal, 2020-04, Vol.20 (8), p.4361-4371</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c384t-237e8c01a0de5551992847b7eac09bac750735e650a9e2b15259a9553f71e99a3</citedby><cites>FETCH-LOGICAL-c384t-237e8c01a0de5551992847b7eac09bac750735e650a9e2b15259a9553f71e99a3</cites><orcidid>0000-0003-0488-4598 ; 0000-0001-5250-1390 ; 0000-0003-1055-4173 ; 0000-0001-9949-6664</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8950427$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,777,781,27905,27906,54777</link.rule.ids></links><search><creatorcontrib>Asim, Yusra</creatorcontrib><creatorcontrib>Azam, Muhammad Awais</creatorcontrib><creatorcontrib>Ehatisham-ul-Haq, Muhammad</creatorcontrib><creatorcontrib>Naeem, Usman</creatorcontrib><creatorcontrib>Khalid, Asra</creatorcontrib><title>Context-Aware Human Activity Recognition (CAHAR) in-the-Wild Using Smartphone Accelerometer</title><title>IEEE sensors journal</title><addtitle>JSEN</addtitle><description>Smartphones are a promising platform for continuous monitoring of human behavior. However, the ability to capture people's behavioral patterns in-the-wild is a challenge, as the user's behavior and physical activities can vary, given the variability of settings and environments. Modeling and understanding of human activity in-the-wild must not overlook a user's behavioral context, which is just as crucial as recognizing the range of physical activities. The work in this paper presents a novel framework for context-aware human activity recognition by incorporating human behavioral contexts with physical activities. The proposed framework utilizes a series of machine learning classifiers to validate the efficiency of the proposed method.</description><subject>accelerometer</subject><subject>Accelerometers</subject><subject>Activity recognition</subject><subject>Behavior</subject><subject>behavioral context</subject><subject>Biomedical monitoring</subject><subject>context-aware</subject><subject>Environment models</subject><subject>Feature extraction</subject><subject>Human activity recognition</subject><subject>Human performance</subject><subject>Machine learning</subject><subject>Sensor fusion</subject><subject>smartphone</subject><subject>Smartphones</subject><subject>Support vector machines</subject><subject>ubiquitous computing</subject><subject>Wearable sensors</subject><issn>1530-437X</issn><issn>1558-1748</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNo9kE9PAjEQxRujiYh-AONlEy96KPbPlm6PG4KiIZqARBMPTSkDlECL3aLy7d0NxtNMMu-9efkhdElJh1Ki7p7G_ecOI4x0mOrmTBZHqEWFKDCVeXHc7JzgnMv3U3RWVStCqJJCttBHL_gEPwmX3yZCNthtjM9Km9yXS_tsBDYsvEsu-OymVw7K0W3mPE5LwG9uPcsmlfOLbLwxMW2XwUPttLCGGDaQIJ6jk7lZV3DxN9toct9_7Q3w8OXhsVcOseVFnjDjEgpLqCEzEEJQpViRy6kEY4maGisFkVxAVxCjgE2pYEIZJQSfSwpKGd5G14fcbQyfO6iSXoVd9PVLXWcr3mWE01pFDyobQ1VFmOttdHXzvaZENwx1w1A3DPUfw9pzdfA4APjXF0qQ-sx_AU4wa_g</recordid><startdate>20200415</startdate><enddate>20200415</enddate><creator>Asim, Yusra</creator><creator>Azam, Muhammad Awais</creator><creator>Ehatisham-ul-Haq, Muhammad</creator><creator>Naeem, Usman</creator><creator>Khalid, Asra</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7U5</scope><scope>8FD</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0003-0488-4598</orcidid><orcidid>https://orcid.org/0000-0001-5250-1390</orcidid><orcidid>https://orcid.org/0000-0003-1055-4173</orcidid><orcidid>https://orcid.org/0000-0001-9949-6664</orcidid></search><sort><creationdate>20200415</creationdate><title>Context-Aware Human Activity Recognition (CAHAR) in-the-Wild Using Smartphone Accelerometer</title><author>Asim, Yusra ; Azam, Muhammad Awais ; Ehatisham-ul-Haq, Muhammad ; Naeem, Usman ; Khalid, Asra</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c384t-237e8c01a0de5551992847b7eac09bac750735e650a9e2b15259a9553f71e99a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>accelerometer</topic><topic>Accelerometers</topic><topic>Activity recognition</topic><topic>Behavior</topic><topic>behavioral context</topic><topic>Biomedical monitoring</topic><topic>context-aware</topic><topic>Environment models</topic><topic>Feature extraction</topic><topic>Human activity recognition</topic><topic>Human performance</topic><topic>Machine learning</topic><topic>Sensor fusion</topic><topic>smartphone</topic><topic>Smartphones</topic><topic>Support vector machines</topic><topic>ubiquitous computing</topic><topic>Wearable sensors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Asim, Yusra</creatorcontrib><creatorcontrib>Azam, Muhammad Awais</creatorcontrib><creatorcontrib>Ehatisham-ul-Haq, Muhammad</creatorcontrib><creatorcontrib>Naeem, Usman</creatorcontrib><creatorcontrib>Khalid, Asra</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Xplore</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE sensors journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Asim, Yusra</au><au>Azam, Muhammad Awais</au><au>Ehatisham-ul-Haq, Muhammad</au><au>Naeem, Usman</au><au>Khalid, Asra</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Context-Aware Human Activity Recognition (CAHAR) in-the-Wild Using Smartphone Accelerometer</atitle><jtitle>IEEE sensors journal</jtitle><stitle>JSEN</stitle><date>2020-04-15</date><risdate>2020</risdate><volume>20</volume><issue>8</issue><spage>4361</spage><epage>4371</epage><pages>4361-4371</pages><issn>1530-437X</issn><eissn>1558-1748</eissn><coden>ISJEAZ</coden><abstract>Smartphones are a promising platform for continuous monitoring of human behavior. However, the ability to capture people's behavioral patterns in-the-wild is a challenge, as the user's behavior and physical activities can vary, given the variability of settings and environments. Modeling and understanding of human activity in-the-wild must not overlook a user's behavioral context, which is just as crucial as recognizing the range of physical activities. The work in this paper presents a novel framework for context-aware human activity recognition by incorporating human behavioral contexts with physical activities. The proposed framework utilizes a series of machine learning classifiers to validate the efficiency of the proposed method.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/JSEN.2020.2964278</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0003-0488-4598</orcidid><orcidid>https://orcid.org/0000-0001-5250-1390</orcidid><orcidid>https://orcid.org/0000-0003-1055-4173</orcidid><orcidid>https://orcid.org/0000-0001-9949-6664</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1530-437X |
| ispartof | IEEE sensors journal, 2020-04, Vol.20 (8), p.4361-4371 |
| issn | 1530-437X 1558-1748 |
| language | eng |
| recordid | cdi_proquest_journals_2379362031 |
| source | IEEE Xplore (Online service) |
| subjects | accelerometer Accelerometers Activity recognition Behavior behavioral context Biomedical monitoring context-aware Environment models Feature extraction Human activity recognition Human performance Machine learning Sensor fusion smartphone Smartphones Support vector machines ubiquitous computing Wearable sensors |
| title | Context-Aware Human Activity Recognition (CAHAR) in-the-Wild Using Smartphone Accelerometer |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-20T06%3A05%3A50IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_ieee_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Context-Aware%20Human%20Activity%20Recognition%20(CAHAR)%20in-the-Wild%20Using%20Smartphone%20Accelerometer&rft.jtitle=IEEE%20sensors%20journal&rft.au=Asim,%20Yusra&rft.date=2020-04-15&rft.volume=20&rft.issue=8&rft.spage=4361&rft.epage=4371&rft.pages=4361-4371&rft.issn=1530-437X&rft.eissn=1558-1748&rft.coden=ISJEAZ&rft_id=info:doi/10.1109/JSEN.2020.2964278&rft_dat=%3Cproquest_ieee_%3E2379362031%3C/proquest_ieee_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c384t-237e8c01a0de5551992847b7eac09bac750735e650a9e2b15259a9553f71e99a3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2379362031&rft_id=info:pmid/&rft_ieee_id=8950427&rfr_iscdi=true |