A Novel Energy-Efficient Approach for Human Activity Recognition
In this paper, we propose a novel energy-efficient approach for mobile activity recognition system (ARS) to detect human activities. The proposed energy-efficient ARS, using low sampling rates, can achieve high recognition accuracy and low energy consumption. A novel classifier that integrates hiera...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2017-09-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/17/9/2064 |
| _version_ | 1811187778646441984 |
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| author | Lingxiang Zheng Dihong Wu Xiaoyang Ruan Shaolin Weng Ao Peng Biyu Tang Hai Lu Haibin Shi Huiru Zheng |
| author_facet | Lingxiang Zheng Dihong Wu Xiaoyang Ruan Shaolin Weng Ao Peng Biyu Tang Hai Lu Haibin Shi Huiru Zheng |
| author_sort | Lingxiang Zheng |
| collection | DOAJ |
| description | In this paper, we propose a novel energy-efficient approach for mobile activity recognition system (ARS) to detect human activities. The proposed energy-efficient ARS, using low sampling rates, can achieve high recognition accuracy and low energy consumption. A novel classifier that integrates hierarchical support vector machine and context-based classification (HSVMCC) is presented to achieve a high accuracy of activity recognition when the sampling rate is less than the activity frequency, i.e., the Nyquist sampling theorem is not satisfied. We tested the proposed energy-efficient approach with the data collected from 20 volunteers (14 males and six females) and the average recognition accuracy of around 96.0% was achieved. Results show that using a low sampling rate of 1Hz can save 17.3% and 59.6% of energy compared with the sampling rates of 5 Hz and 50 Hz. The proposed low sampling rate approach can greatly reduce the power consumption while maintaining high activity recognition accuracy. The composition of power consumption in online ARS is also investigated in this paper. |
| first_indexed | 2024-04-11T14:09:24Z |
| format | Article |
| id | doaj.art-8d23d188b61047fb9d2f91bac7947b97 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T14:09:24Z |
| publishDate | 2017-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8d23d188b61047fb9d2f91bac7947b972022-12-22T04:19:47ZengMDPI AGSensors1424-82202017-09-01179206410.3390/s17092064s17092064A Novel Energy-Efficient Approach for Human Activity RecognitionLingxiang Zheng0Dihong Wu1Xiaoyang Ruan2Shaolin Weng3Ao Peng4Biyu Tang5Hai Lu6Haibin Shi7Huiru Zheng8School of Information Science and Engineering, Xiamen University, Xiamen 361005, ChinaSchool of Information Science and Engineering, Xiamen University, Xiamen 361005, ChinaSchool of Information Science and Engineering, Xiamen University, Xiamen 361005, ChinaSchool of Information Science and Engineering, Xiamen University, Xiamen 361005, ChinaSchool of Information Science and Engineering, Xiamen University, Xiamen 361005, ChinaSchool of Information Science and Engineering, Xiamen University, Xiamen 361005, ChinaSchool of Information Science and Engineering, Xiamen University, Xiamen 361005, ChinaSchool of Information Science and Engineering, Xiamen University, Xiamen 361005, ChinaSchool of Computing, Ulster University, Newtownabbey, CO Antrim BT37 0QB, UKIn this paper, we propose a novel energy-efficient approach for mobile activity recognition system (ARS) to detect human activities. The proposed energy-efficient ARS, using low sampling rates, can achieve high recognition accuracy and low energy consumption. A novel classifier that integrates hierarchical support vector machine and context-based classification (HSVMCC) is presented to achieve a high accuracy of activity recognition when the sampling rate is less than the activity frequency, i.e., the Nyquist sampling theorem is not satisfied. We tested the proposed energy-efficient approach with the data collected from 20 volunteers (14 males and six females) and the average recognition accuracy of around 96.0% was achieved. Results show that using a low sampling rate of 1Hz can save 17.3% and 59.6% of energy compared with the sampling rates of 5 Hz and 50 Hz. The proposed low sampling rate approach can greatly reduce the power consumption while maintaining high activity recognition accuracy. The composition of power consumption in online ARS is also investigated in this paper.https://www.mdpi.com/1424-8220/17/9/2064activity recognitionlow power consumptionlow sampling rateenergy-efficient classifier |
| spellingShingle | Lingxiang Zheng Dihong Wu Xiaoyang Ruan Shaolin Weng Ao Peng Biyu Tang Hai Lu Haibin Shi Huiru Zheng A Novel Energy-Efficient Approach for Human Activity Recognition Sensors activity recognition low power consumption low sampling rate energy-efficient classifier |
| title | A Novel Energy-Efficient Approach for Human Activity Recognition |
| title_full | A Novel Energy-Efficient Approach for Human Activity Recognition |
| title_fullStr | A Novel Energy-Efficient Approach for Human Activity Recognition |
| title_full_unstemmed | A Novel Energy-Efficient Approach for Human Activity Recognition |
| title_short | A Novel Energy-Efficient Approach for Human Activity Recognition |
| title_sort | novel energy efficient approach for human activity recognition |
| topic | activity recognition low power consumption low sampling rate energy-efficient classifier |
| url | https://www.mdpi.com/1424-8220/17/9/2064 |
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