Smart Suspenders with Sensors and Machine Learning for Human Activity Monitoring
Most of the Human Activity Recognition (HAR) research focuses on the use of smartphone in-built accelerometer sensors. As accelerometers are location-centric, they measure acceleration signals only at the installation points, increasing the number of sensors required to identify the whole human body...
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| Veröffentlicht in: | IEEE sensors journal 2023-05, Vol.23 (9), p.1-1 |
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| creator | Mani, Neelakandan Haridoss, Prathap George, Boby |
| description | Most of the Human Activity Recognition (HAR) research focuses on the use of smartphone in-built accelerometer sensors. As accelerometers are location-centric, they measure acceleration signals only at the installation points, increasing the number of sensors required to identify the whole human body activity. They have an inherent property of being noisy, thereby increasing the processing complexity and duration. This paper, for the first time, proposes a body-worn suspender integrated with a strain sensor system that captures the body movement's periodicity, resulting in less noisy readings with non-localized measurements. The proposed smart suspender system reduces the need for localized sensors to measure complex activities at various points and lessen the pre-processing time for smoothening the noise signals. The system recognizes three simple and eleven complex human activities using machine and deep learning algorithms with best accuracy value of 97.85%. A comparison of the performance between kernel and linear discriminant analysis (KDA and LDA) for this system is made and KDA outperformed LDA across most classifiers. |
| doi_str_mv | 10.1109/JSEN.2023.3263231 |
| format | Article |
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As accelerometers are location-centric, they measure acceleration signals only at the installation points, increasing the number of sensors required to identify the whole human body activity. They have an inherent property of being noisy, thereby increasing the processing complexity and duration. This paper, for the first time, proposes a body-worn suspender integrated with a strain sensor system that captures the body movement's periodicity, resulting in less noisy readings with non-localized measurements. The proposed smart suspender system reduces the need for localized sensors to measure complex activities at various points and lessen the pre-processing time for smoothening the noise signals. The system recognizes three simple and eleven complex human activities using machine and deep learning algorithms with best accuracy value of 97.85%. 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(IEEE) 2023</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c294t-8cd697caca0ad6cf15ac916af8ee1218cf61178b950e89c802d7880013448e4d3</citedby><cites>FETCH-LOGICAL-c294t-8cd697caca0ad6cf15ac916af8ee1218cf61178b950e89c802d7880013448e4d3</cites><orcidid>0000-0003-4178-7017 ; 0000-0001-9923-6328</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10092476$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/10092476$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Mani, Neelakandan</creatorcontrib><creatorcontrib>Haridoss, Prathap</creatorcontrib><creatorcontrib>George, Boby</creatorcontrib><title>Smart Suspenders with Sensors and Machine Learning for Human Activity Monitoring</title><title>IEEE sensors journal</title><addtitle>JSEN</addtitle><description>Most of the Human Activity Recognition (HAR) research focuses on the use of smartphone in-built accelerometer sensors. As accelerometers are location-centric, they measure acceleration signals only at the installation points, increasing the number of sensors required to identify the whole human body activity. They have an inherent property of being noisy, thereby increasing the processing complexity and duration. This paper, for the first time, proposes a body-worn suspender integrated with a strain sensor system that captures the body movement's periodicity, resulting in less noisy readings with non-localized measurements. The proposed smart suspender system reduces the need for localized sensors to measure complex activities at various points and lessen the pre-processing time for smoothening the noise signals. The system recognizes three simple and eleven complex human activities using machine and deep learning algorithms with best accuracy value of 97.85%. A comparison of the performance between kernel and linear discriminant analysis (KDA and LDA) for this system is made and KDA outperformed LDA across most classifiers.</description><subject>Acceleration measurement</subject><subject>Accelerometers</subject><subject>activity</subject><subject>Algorithms</subject><subject>Capacitive sensors</subject><subject>Complexity</subject><subject>Decision trees</subject><subject>Deep learning</subject><subject>Discriminant analysis</subject><subject>HAR</subject><subject>human</subject><subject>Human activity recognition</subject><subject>Human motion</subject><subject>Intelligent sensors</subject><subject>KDA</subject><subject>KNN</subject><subject>LDA</subject><subject>Logistic regression</subject><subject>LSTM</subject><subject>Machine learning</subject><subject>Noise measurement</subject><subject>Prototypes</subject><subject>Random Forest</subject><subject>recognition</subject><subject>sensor</subject><subject>Sensors</subject><subject>Smart sensors</subject><subject>Strain</subject><subject>strain sensor</subject><subject>SVM</subject><subject>Wearable</subject><issn>1530-437X</issn><issn>1558-1748</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkE1LAzEQhoMoWKs_QPAQ8Lw1k2Q3ybEUtUqrwip4CzGbtSk2W5Ndpf_eXdqDp5lh3nc-HoQugUwAiLp5LG-fJpRQNmG0YJTBERpBnssMBJfHQ85Ixpl4P0VnKa0JASVyMUIv5cbEFpdd2rpQuZjwr29XuHQhNX1hQoWXxq58cHjhTAw-fOK6iXjebUzAU9v6H9_u8LIJvm1i3z1HJ7X5Su7iEMfo7e72dTbPFs_3D7PpIrNU8TaTtiqUsMYaYqrC1pAbq6AwtXQOKEhbFwBCfqicOKmsJLQSUvZnM86l4xUbo-v93G1svjuXWr1uuhj6lZpKIiUjTPBeBXuVjU1K0dV6G33_8U4D0QM4PYDTAzh9ANd7rvYe75z7pyeKclGwP-Qmaco</recordid><startdate>20230501</startdate><enddate>20230501</enddate><creator>Mani, Neelakandan</creator><creator>Haridoss, Prathap</creator><creator>George, Boby</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/0000-0003-4178-7017</orcidid><orcidid>https://orcid.org/0000-0001-9923-6328</orcidid></search><sort><creationdate>20230501</creationdate><title>Smart Suspenders with Sensors and Machine Learning for Human Activity Monitoring</title><author>Mani, Neelakandan ; Haridoss, Prathap ; George, Boby</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c294t-8cd697caca0ad6cf15ac916af8ee1218cf61178b950e89c802d7880013448e4d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Acceleration measurement</topic><topic>Accelerometers</topic><topic>activity</topic><topic>Algorithms</topic><topic>Capacitive sensors</topic><topic>Complexity</topic><topic>Decision trees</topic><topic>Deep learning</topic><topic>Discriminant analysis</topic><topic>HAR</topic><topic>human</topic><topic>Human activity recognition</topic><topic>Human motion</topic><topic>Intelligent sensors</topic><topic>KDA</topic><topic>KNN</topic><topic>LDA</topic><topic>Logistic regression</topic><topic>LSTM</topic><topic>Machine learning</topic><topic>Noise measurement</topic><topic>Prototypes</topic><topic>Random Forest</topic><topic>recognition</topic><topic>sensor</topic><topic>Sensors</topic><topic>Smart sensors</topic><topic>Strain</topic><topic>strain sensor</topic><topic>SVM</topic><topic>Wearable</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mani, Neelakandan</creatorcontrib><creatorcontrib>Haridoss, Prathap</creatorcontrib><creatorcontrib>George, Boby</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</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_linktorsrc</fulltext></delivery><addata><au>Mani, Neelakandan</au><au>Haridoss, Prathap</au><au>George, Boby</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Smart Suspenders with Sensors and Machine Learning for Human Activity Monitoring</atitle><jtitle>IEEE sensors journal</jtitle><stitle>JSEN</stitle><date>2023-05-01</date><risdate>2023</risdate><volume>23</volume><issue>9</issue><spage>1</spage><epage>1</epage><pages>1-1</pages><issn>1530-437X</issn><eissn>1558-1748</eissn><coden>ISJEAZ</coden><abstract>Most of the Human Activity Recognition (HAR) research focuses on the use of smartphone in-built accelerometer sensors. As accelerometers are location-centric, they measure acceleration signals only at the installation points, increasing the number of sensors required to identify the whole human body activity. They have an inherent property of being noisy, thereby increasing the processing complexity and duration. This paper, for the first time, proposes a body-worn suspender integrated with a strain sensor system that captures the body movement's periodicity, resulting in less noisy readings with non-localized measurements. The proposed smart suspender system reduces the need for localized sensors to measure complex activities at various points and lessen the pre-processing time for smoothening the noise signals. The system recognizes three simple and eleven complex human activities using machine and deep learning algorithms with best accuracy value of 97.85%. A comparison of the performance between kernel and linear discriminant analysis (KDA and LDA) for this system is made and KDA outperformed LDA across most classifiers.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/JSEN.2023.3263231</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0003-4178-7017</orcidid><orcidid>https://orcid.org/0000-0001-9923-6328</orcidid></addata></record> |
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| source | IEEE Electronic Library (IEL) |
| subjects | Acceleration measurement Accelerometers activity Algorithms Capacitive sensors Complexity Decision trees Deep learning Discriminant analysis HAR human Human activity recognition Human motion Intelligent sensors KDA KNN LDA Logistic regression LSTM Machine learning Noise measurement Prototypes Random Forest recognition sensor Sensors Smart sensors Strain strain sensor SVM Wearable |
| title | Smart Suspenders with Sensors and Machine Learning for Human Activity Monitoring |
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