A Lightweight Hierarchical Activity Recognition Framework Using Smartphone Sensors
Activity recognition for the purposes of recognizing a user’s intentions using multimodal sensors is becoming a widely researched topic largely based on the prevalence of the smartphone. Previous studies have reported the difficulty in recognizing life-logs by only using a smartphone due to the chal...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2014-09-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/14/9/16181 |
| _version_ | 1828272515017867264 |
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| author | Manhyung Han Jae Hun Bang Chris Nugent Sally McClean Sungyoung Lee |
| author_facet | Manhyung Han Jae Hun Bang Chris Nugent Sally McClean Sungyoung Lee |
| author_sort | Manhyung Han |
| collection | DOAJ |
| description | Activity recognition for the purposes of recognizing a user’s intentions using multimodal sensors is becoming a widely researched topic largely based on the prevalence of the smartphone. Previous studies have reported the difficulty in recognizing life-logs by only using a smartphone due to the challenges with activity modeling and real-time recognition. In addition, recognizing life-logs is difficult due to the absence of an established framework which enables the use of different sources of sensor data. In this paper, we propose a smartphone-based Hierarchical Activity Recognition Framework which extends the Naïve Bayes approach for the processing of activity modeling and real-time activity recognition. The proposed algorithm demonstrates higher accuracy than the Naïve Bayes approach and also enables the recognition of a user’s activities within a mobile environment. The proposed algorithm has the ability to classify fifteen activities with an average classification accuracy of 92.96%. |
| first_indexed | 2024-04-13T06:07:52Z |
| format | Article |
| id | doaj.art-5c2da6fc9a53499fb94af3a1aa375895 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T06:07:52Z |
| publishDate | 2014-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5c2da6fc9a53499fb94af3a1aa3758952022-12-22T02:59:10ZengMDPI AGSensors1424-82202014-09-01149161811619510.3390/s140916181s140916181A Lightweight Hierarchical Activity Recognition Framework Using Smartphone SensorsManhyung Han0Jae Hun Bang1Chris Nugent2Sally McClean3Sungyoung Lee4Ubiquitous Computing Laboratory, Department of Computer Engineering, Kyung Hee University, 1 Seocheon-Dong, Giheung-Gu, Yongin-Si, Gyeonggi-Do 446-701, KoreaUbiquitous Computing Laboratory, Department of Computer Engineering, Kyung Hee University, 1 Seocheon-Dong, Giheung-Gu, Yongin-Si, Gyeonggi-Do 446-701, KoreaSchool of Computing and Mathematics, Computer Science Research Institute, University of Ulster, Newtownabbey, Co. Antrim, BT37 0QB, UKSchool of Computing and Information Engineering, University of Ulster, Coleraine, Co. Londonderry, BT52 1SA, UKUbiquitous Computing Laboratory, Department of Computer Engineering, Kyung Hee University, 1 Seocheon-Dong, Giheung-Gu, Yongin-Si, Gyeonggi-Do 446-701, KoreaActivity recognition for the purposes of recognizing a user’s intentions using multimodal sensors is becoming a widely researched topic largely based on the prevalence of the smartphone. Previous studies have reported the difficulty in recognizing life-logs by only using a smartphone due to the challenges with activity modeling and real-time recognition. In addition, recognizing life-logs is difficult due to the absence of an established framework which enables the use of different sources of sensor data. In this paper, we propose a smartphone-based Hierarchical Activity Recognition Framework which extends the Naïve Bayes approach for the processing of activity modeling and real-time activity recognition. The proposed algorithm demonstrates higher accuracy than the Naïve Bayes approach and also enables the recognition of a user’s activities within a mobile environment. The proposed algorithm has the ability to classify fifteen activities with an average classification accuracy of 92.96%.http://www.mdpi.com/1424-8220/14/9/16181activity recognitionsmartphonemultimodal sensorsnaïve Bayeslife-log |
| spellingShingle | Manhyung Han Jae Hun Bang Chris Nugent Sally McClean Sungyoung Lee A Lightweight Hierarchical Activity Recognition Framework Using Smartphone Sensors Sensors activity recognition smartphone multimodal sensors naïve Bayes life-log |
| title | A Lightweight Hierarchical Activity Recognition Framework Using Smartphone Sensors |
| title_full | A Lightweight Hierarchical Activity Recognition Framework Using Smartphone Sensors |
| title_fullStr | A Lightweight Hierarchical Activity Recognition Framework Using Smartphone Sensors |
| title_full_unstemmed | A Lightweight Hierarchical Activity Recognition Framework Using Smartphone Sensors |
| title_short | A Lightweight Hierarchical Activity Recognition Framework Using Smartphone Sensors |
| title_sort | lightweight hierarchical activity recognition framework using smartphone sensors |
| topic | activity recognition smartphone multimodal sensors naïve Bayes life-log |
| url | http://www.mdpi.com/1424-8220/14/9/16181 |
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