An Activity-Aware Sampling Scheme for Mobile Phones in Activity Recognition
In recent years, sensors in smartphones have been widely used in applications, e.g., human activity recognition (HAR). However, the power of smartphone constrains the applications of HAR due to the computations. To combat it, energy efficiency should be considered in the applications of HAR with sma...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/8/2189 |
| _version_ | 1797570763945934848 |
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| author | Zhimin Chen Jianxin Chen Xiangjun Huang |
| author_facet | Zhimin Chen Jianxin Chen Xiangjun Huang |
| author_sort | Zhimin Chen |
| collection | DOAJ |
| description | In recent years, sensors in smartphones have been widely used in applications, e.g., human activity recognition (HAR). However, the power of smartphone constrains the applications of HAR due to the computations. To combat it, energy efficiency should be considered in the applications of HAR with smartphones. In this paper, we improve energy efficiency for smartphones by adaptively controlling the sampling rate of the sensors during HAR. We collect the sensor samples, depending on the activity changing, based on the magnitude of acceleration. Besides that, we use linear discriminant analysis (LDA) to select the feature and machine learning methods for activity classification. Our method is verified on the UCI (University of California, Irvine) dataset; and it achieves an overall 56.39% of energy saving and the recognition accuracy of 99.58% during the HAR applications with smartphone. |
| first_indexed | 2024-03-10T20:30:00Z |
| format | Article |
| id | doaj.art-5c2804ae00c04b5bb321701df412beba |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T20:30:00Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5c2804ae00c04b5bb321701df412beba2023-11-19T21:27:34ZengMDPI AGSensors1424-82202020-04-01208218910.3390/s20082189An Activity-Aware Sampling Scheme for Mobile Phones in Activity RecognitionZhimin Chen0Jianxin Chen1Xiangjun Huang2College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaCollege of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaCollege of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaIn recent years, sensors in smartphones have been widely used in applications, e.g., human activity recognition (HAR). However, the power of smartphone constrains the applications of HAR due to the computations. To combat it, energy efficiency should be considered in the applications of HAR with smartphones. In this paper, we improve energy efficiency for smartphones by adaptively controlling the sampling rate of the sensors during HAR. We collect the sensor samples, depending on the activity changing, based on the magnitude of acceleration. Besides that, we use linear discriminant analysis (LDA) to select the feature and machine learning methods for activity classification. Our method is verified on the UCI (University of California, Irvine) dataset; and it achieves an overall 56.39% of energy saving and the recognition accuracy of 99.58% during the HAR applications with smartphone.https://www.mdpi.com/1424-8220/20/8/2189activity recognitionmachine learningfeature selectionpower consumption |
| spellingShingle | Zhimin Chen Jianxin Chen Xiangjun Huang An Activity-Aware Sampling Scheme for Mobile Phones in Activity Recognition Sensors activity recognition machine learning feature selection power consumption |
| title | An Activity-Aware Sampling Scheme for Mobile Phones in Activity Recognition |
| title_full | An Activity-Aware Sampling Scheme for Mobile Phones in Activity Recognition |
| title_fullStr | An Activity-Aware Sampling Scheme for Mobile Phones in Activity Recognition |
| title_full_unstemmed | An Activity-Aware Sampling Scheme for Mobile Phones in Activity Recognition |
| title_short | An Activity-Aware Sampling Scheme for Mobile Phones in Activity Recognition |
| title_sort | activity aware sampling scheme for mobile phones in activity recognition |
| topic | activity recognition machine learning feature selection power consumption |
| url | https://www.mdpi.com/1424-8220/20/8/2189 |
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