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...
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Autor*in:	Lingxiang Zheng [verfasserIn] Dihong Wu [verfasserIn] Xiaoyang Ruan [verfasserIn] Shaolin Weng [verfasserIn] Ao Peng [verfasserIn] Biyu Tang [verfasserIn] Hai Lu [verfasserIn] Haibin Shi [verfasserIn] Huiru Zheng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	activity recognition low power consumption low sampling rate energy-efficient classifier

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 17(2017), 9, p 2064
Übergeordnetes Werk:	volume:17 ; year:2017 ; number:9, p 2064

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s17092064

Katalog-ID:	DOAJ085652237

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520			\|a 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.
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allfields	10.3390/s17092064 doi (DE-627)DOAJ085652237 (DE-599)DOAJ8d23d188b61047fb9d2f91bac7947b97 DE-627 ger DE-627 rakwb eng TP1-1185 Lingxiang Zheng verfasserin aut A Novel Energy-Efficient Approach for Human Activity Recognition 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. activity recognition low power consumption low sampling rate energy-efficient classifier Chemical technology Dihong Wu verfasserin aut Xiaoyang Ruan verfasserin aut Shaolin Weng verfasserin aut Ao Peng verfasserin aut Biyu Tang verfasserin aut Hai Lu verfasserin aut Haibin Shi verfasserin aut Huiru Zheng verfasserin aut In Sensors MDPI AG, 2003 17(2017), 9, p 2064 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:17 year:2017 number:9, p 2064 https://doi.org/10.3390/s17092064 kostenfrei https://doaj.org/article/8d23d188b61047fb9d2f91bac7947b97 kostenfrei https://www.mdpi.com/1424-8220/17/9/2064 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2017 9, p 2064
spelling	10.3390/s17092064 doi (DE-627)DOAJ085652237 (DE-599)DOAJ8d23d188b61047fb9d2f91bac7947b97 DE-627 ger DE-627 rakwb eng TP1-1185 Lingxiang Zheng verfasserin aut A Novel Energy-Efficient Approach for Human Activity Recognition 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. activity recognition low power consumption low sampling rate energy-efficient classifier Chemical technology Dihong Wu verfasserin aut Xiaoyang Ruan verfasserin aut Shaolin Weng verfasserin aut Ao Peng verfasserin aut Biyu Tang verfasserin aut Hai Lu verfasserin aut Haibin Shi verfasserin aut Huiru Zheng verfasserin aut In Sensors MDPI AG, 2003 17(2017), 9, p 2064 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:17 year:2017 number:9, p 2064 https://doi.org/10.3390/s17092064 kostenfrei https://doaj.org/article/8d23d188b61047fb9d2f91bac7947b97 kostenfrei https://www.mdpi.com/1424-8220/17/9/2064 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2017 9, p 2064
allfields_unstemmed	10.3390/s17092064 doi (DE-627)DOAJ085652237 (DE-599)DOAJ8d23d188b61047fb9d2f91bac7947b97 DE-627 ger DE-627 rakwb eng TP1-1185 Lingxiang Zheng verfasserin aut A Novel Energy-Efficient Approach for Human Activity Recognition 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. activity recognition low power consumption low sampling rate energy-efficient classifier Chemical technology Dihong Wu verfasserin aut Xiaoyang Ruan verfasserin aut Shaolin Weng verfasserin aut Ao Peng verfasserin aut Biyu Tang verfasserin aut Hai Lu verfasserin aut Haibin Shi verfasserin aut Huiru Zheng verfasserin aut In Sensors MDPI AG, 2003 17(2017), 9, p 2064 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:17 year:2017 number:9, p 2064 https://doi.org/10.3390/s17092064 kostenfrei https://doaj.org/article/8d23d188b61047fb9d2f91bac7947b97 kostenfrei https://www.mdpi.com/1424-8220/17/9/2064 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2017 9, p 2064
allfieldsGer	10.3390/s17092064 doi (DE-627)DOAJ085652237 (DE-599)DOAJ8d23d188b61047fb9d2f91bac7947b97 DE-627 ger DE-627 rakwb eng TP1-1185 Lingxiang Zheng verfasserin aut A Novel Energy-Efficient Approach for Human Activity Recognition 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. activity recognition low power consumption low sampling rate energy-efficient classifier Chemical technology Dihong Wu verfasserin aut Xiaoyang Ruan verfasserin aut Shaolin Weng verfasserin aut Ao Peng verfasserin aut Biyu Tang verfasserin aut Hai Lu verfasserin aut Haibin Shi verfasserin aut Huiru Zheng verfasserin aut In Sensors MDPI AG, 2003 17(2017), 9, p 2064 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:17 year:2017 number:9, p 2064 https://doi.org/10.3390/s17092064 kostenfrei https://doaj.org/article/8d23d188b61047fb9d2f91bac7947b97 kostenfrei https://www.mdpi.com/1424-8220/17/9/2064 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2017 9, p 2064
allfieldsSound	10.3390/s17092064 doi (DE-627)DOAJ085652237 (DE-599)DOAJ8d23d188b61047fb9d2f91bac7947b97 DE-627 ger DE-627 rakwb eng TP1-1185 Lingxiang Zheng verfasserin aut A Novel Energy-Efficient Approach for Human Activity Recognition 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. activity recognition low power consumption low sampling rate energy-efficient classifier Chemical technology Dihong Wu verfasserin aut Xiaoyang Ruan verfasserin aut Shaolin Weng verfasserin aut Ao Peng verfasserin aut Biyu Tang verfasserin aut Hai Lu verfasserin aut Haibin Shi verfasserin aut Huiru Zheng verfasserin aut In Sensors MDPI AG, 2003 17(2017), 9, p 2064 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:17 year:2017 number:9, p 2064 https://doi.org/10.3390/s17092064 kostenfrei https://doaj.org/article/8d23d188b61047fb9d2f91bac7947b97 kostenfrei https://www.mdpi.com/1424-8220/17/9/2064 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2017 9, p 2064
language	English
source	In Sensors 17(2017), 9, p 2064 volume:17 year:2017 number:9, p 2064
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topic_facet	activity recognition low power consumption low sampling rate energy-efficient classifier Chemical technology
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authorswithroles_txt_mv	Lingxiang Zheng @@aut@@ Dihong Wu @@aut@@ Xiaoyang Ruan @@aut@@ Shaolin Weng @@aut@@ Ao Peng @@aut@@ Biyu Tang @@aut@@ Hai Lu @@aut@@ Haibin Shi @@aut@@ Huiru Zheng @@aut@@
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callnumber-first	T - Technology
author	Lingxiang Zheng
spellingShingle	Lingxiang Zheng misc TP1-1185 misc activity recognition misc low power consumption misc low sampling rate misc energy-efficient classifier misc Chemical technology A Novel Energy-Efficient Approach for Human Activity Recognition
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topic_title	TP1-1185 A Novel Energy-Efficient Approach for Human Activity Recognition activity recognition low power consumption low sampling rate energy-efficient classifier
topic	misc TP1-1185 misc activity recognition misc low power consumption misc low sampling rate misc energy-efficient classifier misc Chemical technology
topic_unstemmed	misc TP1-1185 misc activity recognition misc low power consumption misc low sampling rate misc energy-efficient classifier misc Chemical technology
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title	A Novel Energy-Efficient Approach for Human Activity Recognition
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title_sort	novel energy-efficient approach for human activity recognition
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title_auth	A Novel Energy-Efficient Approach for Human Activity Recognition
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	A Novel Energy-Efficient Approach for Human Activity Recognition
url	https://doi.org/10.3390/s17092064 https://doaj.org/article/8d23d188b61047fb9d2f91bac7947b97 https://www.mdpi.com/1424-8220/17/9/2064 https://doaj.org/toc/1424-8220
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author2	Dihong Wu Xiaoyang Ruan Shaolin Weng Ao Peng Biyu Tang Hai Lu Haibin Shi Huiru Zheng
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up_date	2024-07-03T16:03:54.391Z
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A Novel Energy-Efficient Approach for Human Activity Recognition

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