Sensitive Integration of Multilevel Optimization Model in Human Activity Recognition for Smartphone and Smartwatch Applications
This study proposes an intelligent data analysis model for finding optimal patterns in human activities on the basis of biometric features obtained from four sensors installed on smartphone and smartwatch devices. The proposed model, referred to as Scheduling Activities of smartphone and smartwatch...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2021-06-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2020.9020022 |
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| author | Samaher Al-Janabi Ali Hamza Salman |
| author_facet | Samaher Al-Janabi Ali Hamza Salman |
| author_sort | Samaher Al-Janabi |
| collection | DOAJ |
| description | This study proposes an intelligent data analysis model for finding optimal patterns in human activities on the basis of biometric features obtained from four sensors installed on smartphone and smartwatch devices. The proposed model, referred to as Scheduling Activities of smartphone and smartwatch based on Optimal Pattern Model (SA-OPM), consists of four main stages. The first stage relates to the collection of data from four sensors in real time (i.e., two smartphone sensors called accelerometer and gyroscope and two smartwatch sensors of the same name). The second stage involves the preprocessing of the data by converting them into graphs. As graphs are difficult to deal with directly, a deterministic selection algorithm is proposed as a new method to find the optimal root to split the graphs into multiple subgraphs. The third stage entails determining the number of samples related to each subgraph by using the optimization technique called the lion optimization algorithm. The final stage involves the generation of patterns from the optimal subgraph by using the association pattern algorithm called gSpan. The pattern finder based on Forward-Backward Rules (FBR) generates the optimal patterns and thus aids humans in organizing their activities. Results indicate that the proposed SA-OPM model generates robust and authentic patterns of human activities. |
| format | Article |
| id | doaj-art-82abe90aab734e659734348da5f1754e |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2021-06-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-82abe90aab734e659734348da5f1754e2025-02-02T23:47:56ZengTsinghua University PressBig Data Mining and Analytics2096-06542021-06-014212413810.26599/BDMA.2020.9020022Sensitive Integration of Multilevel Optimization Model in Human Activity Recognition for Smartphone and Smartwatch ApplicationsSamaher Al-Janabi0Ali Hamza Salman1<institution content-type="dept">Department of Computer Science, Faculty of Science for Women</institution>, <institution>University of Babylon</institution>, <city>Babylon</city> <postal-code>964</postal-code>, <country>Iraq</country><institution content-type="dept">Department of Computer Science, Faculty of Science for Women</institution>, <institution>University of Babylon</institution>, <city>Babylon</city> <postal-code>964</postal-code>, <country>Iraq</country>This study proposes an intelligent data analysis model for finding optimal patterns in human activities on the basis of biometric features obtained from four sensors installed on smartphone and smartwatch devices. The proposed model, referred to as Scheduling Activities of smartphone and smartwatch based on Optimal Pattern Model (SA-OPM), consists of four main stages. The first stage relates to the collection of data from four sensors in real time (i.e., two smartphone sensors called accelerometer and gyroscope and two smartwatch sensors of the same name). The second stage involves the preprocessing of the data by converting them into graphs. As graphs are difficult to deal with directly, a deterministic selection algorithm is proposed as a new method to find the optimal root to split the graphs into multiple subgraphs. The third stage entails determining the number of samples related to each subgraph by using the optimization technique called the lion optimization algorithm. The final stage involves the generation of patterns from the optimal subgraph by using the association pattern algorithm called gSpan. The pattern finder based on Forward-Backward Rules (FBR) generates the optimal patterns and thus aids humans in organizing their activities. Results indicate that the proposed SA-OPM model generates robust and authentic patterns of human activities.https://www.sciopen.com/article/10.26599/BDMA.2020.9020022optimizationant lion optimization (alo)gspanforward-backward rules (fbr)internet of things (iot)smartwatchsmartphone |
| spellingShingle | Samaher Al-Janabi Ali Hamza Salman Sensitive Integration of Multilevel Optimization Model in Human Activity Recognition for Smartphone and Smartwatch Applications Big Data Mining and Analytics optimization ant lion optimization (alo) gspan forward-backward rules (fbr) internet of things (iot) smartwatch smartphone |
| title | Sensitive Integration of Multilevel Optimization Model in Human Activity Recognition for Smartphone and Smartwatch Applications |
| title_full | Sensitive Integration of Multilevel Optimization Model in Human Activity Recognition for Smartphone and Smartwatch Applications |
| title_fullStr | Sensitive Integration of Multilevel Optimization Model in Human Activity Recognition for Smartphone and Smartwatch Applications |
| title_full_unstemmed | Sensitive Integration of Multilevel Optimization Model in Human Activity Recognition for Smartphone and Smartwatch Applications |
| title_short | Sensitive Integration of Multilevel Optimization Model in Human Activity Recognition for Smartphone and Smartwatch Applications |
| title_sort | sensitive integration of multilevel optimization model in human activity recognition for smartphone and smartwatch applications |
| topic | optimization ant lion optimization (alo) gspan forward-backward rules (fbr) internet of things (iot) smartwatch smartphone |
| url | https://www.sciopen.com/article/10.26599/BDMA.2020.9020022 |
| work_keys_str_mv | AT samaheraljanabi sensitiveintegrationofmultileveloptimizationmodelinhumanactivityrecognitionforsmartphoneandsmartwatchapplications AT alihamzasalman sensitiveintegrationofmultileveloptimizationmodelinhumanactivityrecognitionforsmartphoneandsmartwatchapplications |