Exploratory Analysis of Smartphone Sensor Data for Human Activity Recognition
Precise recognition of human activities in any smart environment such as smart homes or smart healthcare centers is vital for child care, elder care, disabled patient monitoring, self-management systems, safety, tracking healthcare functionality, etc. Automatic human activity recognition (HAR) based...
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Veröffentlicht in: | IEEE access 2023, Vol.11, p.99481-99498 |
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description | Precise recognition of human activities in any smart environment such as smart homes or smart healthcare centers is vital for child care, elder care, disabled patient monitoring, self-management systems, safety, tracking healthcare functionality, etc. Automatic human activity recognition (HAR) based on smartphone sensor data is becoming widespread day by day. However, it is challenging to understand human activities using sensor data and machine learning and so the recognition accuracy of many state-of-the-art methods is relatively low. It requires high computational overhead to improve recognition accuracy. The goal of this paper is to use exploratory data analysis (EDA) to deal with this strain and after analyzing, visualizations and dimensionality reductions are obtained which assists in deciding the data mining techniques. The HAR method based on smartphone accelerometer and gyroscope sensors' data, EDA, and prediction models proposed in this paper is a high-precision method, and its highest accuracy is 97.12% for the HAR smartphone dataset. Heterogeneous models-based two ensembles: stacking and voting are used in this study to identify human activities of daily living (ADL). Three estimators are used: Linear Discriminant Analysis, Linear Support Vector Machines, and Logistic Regression for both stacked and voting generalization. The experimental results show that the generalization algorithms provide an automatic and precise HAR system and can serve as a decision-making tool to identify ADL in any smart environment. |
doi_str_mv | 10.1109/ACCESS.2023.3314651 |
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M. Mohidul ; Talukder, Kamrul Hasan</creator><creatorcontrib>Islam, S. M. Mohidul ; Talukder, Kamrul Hasan</creatorcontrib><description>Precise recognition of human activities in any smart environment such as smart homes or smart healthcare centers is vital for child care, elder care, disabled patient monitoring, self-management systems, safety, tracking healthcare functionality, etc. Automatic human activity recognition (HAR) based on smartphone sensor data is becoming widespread day by day. However, it is challenging to understand human activities using sensor data and machine learning and so the recognition accuracy of many state-of-the-art methods is relatively low. It requires high computational overhead to improve recognition accuracy. The goal of this paper is to use exploratory data analysis (EDA) to deal with this strain and after analyzing, visualizations and dimensionality reductions are obtained which assists in deciding the data mining techniques. The HAR method based on smartphone accelerometer and gyroscope sensors' data, EDA, and prediction models proposed in this paper is a high-precision method, and its highest accuracy is 97.12% for the HAR smartphone dataset. Heterogeneous models-based two ensembles: stacking and voting are used in this study to identify human activities of daily living (ADL). Three estimators are used: Linear Discriminant Analysis, Linear Support Vector Machines, and Logistic Regression for both stacked and voting generalization. The experimental results show that the generalization algorithms provide an automatic and precise HAR system and can serve as a decision-making tool to identify ADL in any smart environment.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2023.3314651</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Accelerometers ; Accuracy ; Activities of daily living ; Algorithms ; Data analysis ; Data mining ; Data models ; Discriminant analysis ; exploratory data analysis ; hard voting ; Health care ; heterogeneous model ; Human activity recognition ; Legged locomotion ; Machine learning ; Management systems ; Prediction models ; Robot sensing systems ; Safety management ; Sensors ; Smart buildings ; Smart phones ; smartphone sensor ; Smartphones ; stacked generalization ; Strain analysis ; Support vector machines ; Training</subject><ispartof>IEEE access, 2023, Vol.11, p.99481-99498</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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Mohidul</creatorcontrib><creatorcontrib>Talukder, Kamrul Hasan</creatorcontrib><title>Exploratory Analysis of Smartphone Sensor Data for Human Activity Recognition</title><title>IEEE access</title><addtitle>Access</addtitle><description>Precise recognition of human activities in any smart environment such as smart homes or smart healthcare centers is vital for child care, elder care, disabled patient monitoring, self-management systems, safety, tracking healthcare functionality, etc. Automatic human activity recognition (HAR) based on smartphone sensor data is becoming widespread day by day. However, it is challenging to understand human activities using sensor data and machine learning and so the recognition accuracy of many state-of-the-art methods is relatively low. It requires high computational overhead to improve recognition accuracy. The goal of this paper is to use exploratory data analysis (EDA) to deal with this strain and after analyzing, visualizations and dimensionality reductions are obtained which assists in deciding the data mining techniques. The HAR method based on smartphone accelerometer and gyroscope sensors' data, EDA, and prediction models proposed in this paper is a high-precision method, and its highest accuracy is 97.12% for the HAR smartphone dataset. Heterogeneous models-based two ensembles: stacking and voting are used in this study to identify human activities of daily living (ADL). Three estimators are used: Linear Discriminant Analysis, Linear Support Vector Machines, and Logistic Regression for both stacked and voting generalization. The experimental results show that the generalization algorithms provide an automatic and precise HAR system and can serve as a decision-making tool to identify ADL in any smart environment.</description><subject>Accelerometers</subject><subject>Accuracy</subject><subject>Activities of daily living</subject><subject>Algorithms</subject><subject>Data analysis</subject><subject>Data mining</subject><subject>Data models</subject><subject>Discriminant analysis</subject><subject>exploratory data analysis</subject><subject>hard voting</subject><subject>Health care</subject><subject>heterogeneous model</subject><subject>Human activity recognition</subject><subject>Legged locomotion</subject><subject>Machine learning</subject><subject>Management systems</subject><subject>Prediction models</subject><subject>Robot sensing systems</subject><subject>Safety management</subject><subject>Sensors</subject><subject>Smart buildings</subject><subject>Smart phones</subject><subject>smartphone sensor</subject><subject>Smartphones</subject><subject>stacked generalization</subject><subject>Strain analysis</subject><subject>Support vector machines</subject><subject>Training</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNkcFOAjEQhjdGEwnyBHrYxDPYdtpu90gQhQRjInpuhtLFEthiuxh5e4tLDHOZyWT-fybzZdktJQNKSfkwHI3G8_mAEQYDAMqloBdZh1FZ9kGAvDyrr7NejGuSQqWWKDrZy_hnt_EBGx8O-bDGzSG6mPsqn28xNLtPX9t8buvoQ_6IDeZVKib7Ldb50DTu2zWH_M0av6pd43x9k11VuIm2d8rd7ONp_D6a9Gevz9PRcNY3IMqmj4JJw8lSMgUEJVhboAIFFalUYRTjWDDOCiIoFYQzBoCqVFbIkqMRdAHdbNr6Lj2u9S64dOxBe3T6r-HDSqfrndlYjQtjyNIWalkuOOWIaDkQgVVFDHB59LpvvXbBf-1tbPTa70P6RNRMSckVKQVNU9BOmeBjDLb630qJPmLQLQZ9xKBPGJLqrlU5a-2ZgvEi0YBfioKCfA</recordid><startdate>2023</startdate><enddate>2023</enddate><creator>Islam, S. M. Mohidul</creator><creator>Talukder, Kamrul Hasan</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-8705-3636</orcidid></search><sort><creationdate>2023</creationdate><title>Exploratory Analysis of Smartphone Sensor Data for Human Activity Recognition</title><author>Islam, S. M. Mohidul ; Talukder, Kamrul Hasan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c359t-a526c40d62830a63ee7a8383f0f87c824a7242705115042233a898e5694ac51b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Accelerometers</topic><topic>Accuracy</topic><topic>Activities of daily living</topic><topic>Algorithms</topic><topic>Data analysis</topic><topic>Data mining</topic><topic>Data models</topic><topic>Discriminant analysis</topic><topic>exploratory data analysis</topic><topic>hard voting</topic><topic>Health care</topic><topic>heterogeneous model</topic><topic>Human activity recognition</topic><topic>Legged locomotion</topic><topic>Machine learning</topic><topic>Management systems</topic><topic>Prediction models</topic><topic>Robot sensing systems</topic><topic>Safety management</topic><topic>Sensors</topic><topic>Smart buildings</topic><topic>Smart phones</topic><topic>smartphone sensor</topic><topic>Smartphones</topic><topic>stacked generalization</topic><topic>Strain analysis</topic><topic>Support vector machines</topic><topic>Training</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Islam, S. 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Mohidul</creatorcontrib><creatorcontrib>Talukder, Kamrul Hasan</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005–Present</collection><collection>IEEE Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>IEEE access</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Islam, S. M. Mohidul</au><au>Talukder, Kamrul Hasan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Exploratory Analysis of Smartphone Sensor Data for Human Activity Recognition</atitle><jtitle>IEEE access</jtitle><stitle>Access</stitle><date>2023</date><risdate>2023</risdate><volume>11</volume><spage>99481</spage><epage>99498</epage><pages>99481-99498</pages><issn>2169-3536</issn><eissn>2169-3536</eissn><coden>IAECCG</coden><abstract>Precise recognition of human activities in any smart environment such as smart homes or smart healthcare centers is vital for child care, elder care, disabled patient monitoring, self-management systems, safety, tracking healthcare functionality, etc. Automatic human activity recognition (HAR) based on smartphone sensor data is becoming widespread day by day. However, it is challenging to understand human activities using sensor data and machine learning and so the recognition accuracy of many state-of-the-art methods is relatively low. It requires high computational overhead to improve recognition accuracy. The goal of this paper is to use exploratory data analysis (EDA) to deal with this strain and after analyzing, visualizations and dimensionality reductions are obtained which assists in deciding the data mining techniques. The HAR method based on smartphone accelerometer and gyroscope sensors' data, EDA, and prediction models proposed in this paper is a high-precision method, and its highest accuracy is 97.12% for the HAR smartphone dataset. Heterogeneous models-based two ensembles: stacking and voting are used in this study to identify human activities of daily living (ADL). Three estimators are used: Linear Discriminant Analysis, Linear Support Vector Machines, and Logistic Regression for both stacked and voting generalization. The experimental results show that the generalization algorithms provide an automatic and precise HAR system and can serve as a decision-making tool to identify ADL in any smart environment.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2023.3314651</doi><tpages>18</tpages><orcidid>https://orcid.org/0000-0002-8705-3636</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Accelerometers Accuracy Activities of daily living Algorithms Data analysis Data mining Data models Discriminant analysis exploratory data analysis hard voting Health care heterogeneous model Human activity recognition Legged locomotion Machine learning Management systems Prediction models Robot sensing systems Safety management Sensors Smart buildings Smart phones smartphone sensor Smartphones stacked generalization Strain analysis Support vector machines Training |
title | Exploratory Analysis of Smartphone Sensor Data for Human Activity Recognition |
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