Gait Adaptive Duty Cycle: Optimize the QoS of WBSN-HAR

Abstract To proliferate the traditional healthcare industry, the deep research is going on Wireless Body Sensor Network (WBSN). Many healthcare deployment models are designed for continuous and uninterrupted remote health monitoring system. But the Quality of Standard like energy, reliability and ac...
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Gespeichert in:

Autor*in:	Gandhi, Vidhyotma [verfasserIn] Singh, Jaiteg

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	WBSN Human activity recognition Gait-cycle Energy efficiency Duty-cycle

Anmerkung:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021

Übergeordnetes Werk:	Enthalten in: Wireless personal communications - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994, 123(2021), 3 vom: 10. Nov., Seite 1967-1985
Übergeordnetes Werk:	volume:123 ; year:2021 ; number:3 ; day:10 ; month:11 ; pages:1967-1985

Links:	Volltext

DOI / URN:	10.1007/s11277-021-09224-2

Katalog-ID:	SPR046563083

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allfields	10.1007/s11277-021-09224-2 doi (DE-627)SPR046563083 (SPR)s11277-021-09224-2-e DE-627 ger DE-627 rakwb eng Gandhi, Vidhyotma verfasserin (orcid)0000-0003-2359-4553 aut Gait Adaptive Duty Cycle: Optimize the QoS of WBSN-HAR 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract To proliferate the traditional healthcare industry, the deep research is going on Wireless Body Sensor Network (WBSN). Many healthcare deployment models are designed for continuous and uninterrupted remote health monitoring system. But the Quality of Standard like energy, reliability and accuracy are to be pinned more. The Gait Adaptive Duty Cycle-Human Activity Recognition (GADC-HAR) is proposed with better performance in terms of energy, reliability and accuracy. To enhance the performance of model the two techniques were adopted: (i) design Energy Efficient and Reliable algorithm for the network coding (ii) optimization of sleep/wake timer to synchronise Controller Node with relay node. The performance validation is done with the real time implementation of GADC-HAR. The forty subjects (young and adult with same gender ratio) are examined with a 360 s activity pattern, a strategic process of self-optimization is adopted for gait cycle synchronization. At the end model is evaluated as 48.5% more energy efficient and packet loss ratio is reduced by 7.92%. Based on the gait cycle, population categories are sub-categorised in normal young/adult and fast young/adult subjects. GADC-HAR young normal 10.45%, young fast 11.28%, adult normal 25% and adult fast 25.13% were more accurate than WBSN-HAR generic model. WBSN (dpeaa)DE-He213 Human activity recognition (dpeaa)DE-He213 Gait-cycle (dpeaa)DE-He213 Energy efficiency (dpeaa)DE-He213 Duty-cycle (dpeaa)DE-He213 Singh, Jaiteg aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 123(2021), 3 vom: 10. Nov., Seite 1967-1985 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:123 year:2021 number:3 day:10 month:11 pages:1967-1985 https://dx.doi.org/10.1007/s11277-021-09224-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 123 2021 3 10 11 1967-1985
spelling	10.1007/s11277-021-09224-2 doi (DE-627)SPR046563083 (SPR)s11277-021-09224-2-e DE-627 ger DE-627 rakwb eng Gandhi, Vidhyotma verfasserin (orcid)0000-0003-2359-4553 aut Gait Adaptive Duty Cycle: Optimize the QoS of WBSN-HAR 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract To proliferate the traditional healthcare industry, the deep research is going on Wireless Body Sensor Network (WBSN). Many healthcare deployment models are designed for continuous and uninterrupted remote health monitoring system. But the Quality of Standard like energy, reliability and accuracy are to be pinned more. The Gait Adaptive Duty Cycle-Human Activity Recognition (GADC-HAR) is proposed with better performance in terms of energy, reliability and accuracy. To enhance the performance of model the two techniques were adopted: (i) design Energy Efficient and Reliable algorithm for the network coding (ii) optimization of sleep/wake timer to synchronise Controller Node with relay node. The performance validation is done with the real time implementation of GADC-HAR. The forty subjects (young and adult with same gender ratio) are examined with a 360 s activity pattern, a strategic process of self-optimization is adopted for gait cycle synchronization. At the end model is evaluated as 48.5% more energy efficient and packet loss ratio is reduced by 7.92%. Based on the gait cycle, population categories are sub-categorised in normal young/adult and fast young/adult subjects. GADC-HAR young normal 10.45%, young fast 11.28%, adult normal 25% and adult fast 25.13% were more accurate than WBSN-HAR generic model. WBSN (dpeaa)DE-He213 Human activity recognition (dpeaa)DE-He213 Gait-cycle (dpeaa)DE-He213 Energy efficiency (dpeaa)DE-He213 Duty-cycle (dpeaa)DE-He213 Singh, Jaiteg aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 123(2021), 3 vom: 10. Nov., Seite 1967-1985 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:123 year:2021 number:3 day:10 month:11 pages:1967-1985 https://dx.doi.org/10.1007/s11277-021-09224-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 123 2021 3 10 11 1967-1985
allfields_unstemmed	10.1007/s11277-021-09224-2 doi (DE-627)SPR046563083 (SPR)s11277-021-09224-2-e DE-627 ger DE-627 rakwb eng Gandhi, Vidhyotma verfasserin (orcid)0000-0003-2359-4553 aut Gait Adaptive Duty Cycle: Optimize the QoS of WBSN-HAR 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract To proliferate the traditional healthcare industry, the deep research is going on Wireless Body Sensor Network (WBSN). Many healthcare deployment models are designed for continuous and uninterrupted remote health monitoring system. But the Quality of Standard like energy, reliability and accuracy are to be pinned more. The Gait Adaptive Duty Cycle-Human Activity Recognition (GADC-HAR) is proposed with better performance in terms of energy, reliability and accuracy. To enhance the performance of model the two techniques were adopted: (i) design Energy Efficient and Reliable algorithm for the network coding (ii) optimization of sleep/wake timer to synchronise Controller Node with relay node. The performance validation is done with the real time implementation of GADC-HAR. The forty subjects (young and adult with same gender ratio) are examined with a 360 s activity pattern, a strategic process of self-optimization is adopted for gait cycle synchronization. At the end model is evaluated as 48.5% more energy efficient and packet loss ratio is reduced by 7.92%. Based on the gait cycle, population categories are sub-categorised in normal young/adult and fast young/adult subjects. GADC-HAR young normal 10.45%, young fast 11.28%, adult normal 25% and adult fast 25.13% were more accurate than WBSN-HAR generic model. WBSN (dpeaa)DE-He213 Human activity recognition (dpeaa)DE-He213 Gait-cycle (dpeaa)DE-He213 Energy efficiency (dpeaa)DE-He213 Duty-cycle (dpeaa)DE-He213 Singh, Jaiteg aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 123(2021), 3 vom: 10. Nov., Seite 1967-1985 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:123 year:2021 number:3 day:10 month:11 pages:1967-1985 https://dx.doi.org/10.1007/s11277-021-09224-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 123 2021 3 10 11 1967-1985
allfieldsGer	10.1007/s11277-021-09224-2 doi (DE-627)SPR046563083 (SPR)s11277-021-09224-2-e DE-627 ger DE-627 rakwb eng Gandhi, Vidhyotma verfasserin (orcid)0000-0003-2359-4553 aut Gait Adaptive Duty Cycle: Optimize the QoS of WBSN-HAR 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract To proliferate the traditional healthcare industry, the deep research is going on Wireless Body Sensor Network (WBSN). Many healthcare deployment models are designed for continuous and uninterrupted remote health monitoring system. But the Quality of Standard like energy, reliability and accuracy are to be pinned more. The Gait Adaptive Duty Cycle-Human Activity Recognition (GADC-HAR) is proposed with better performance in terms of energy, reliability and accuracy. To enhance the performance of model the two techniques were adopted: (i) design Energy Efficient and Reliable algorithm for the network coding (ii) optimization of sleep/wake timer to synchronise Controller Node with relay node. The performance validation is done with the real time implementation of GADC-HAR. The forty subjects (young and adult with same gender ratio) are examined with a 360 s activity pattern, a strategic process of self-optimization is adopted for gait cycle synchronization. At the end model is evaluated as 48.5% more energy efficient and packet loss ratio is reduced by 7.92%. Based on the gait cycle, population categories are sub-categorised in normal young/adult and fast young/adult subjects. GADC-HAR young normal 10.45%, young fast 11.28%, adult normal 25% and adult fast 25.13% were more accurate than WBSN-HAR generic model. WBSN (dpeaa)DE-He213 Human activity recognition (dpeaa)DE-He213 Gait-cycle (dpeaa)DE-He213 Energy efficiency (dpeaa)DE-He213 Duty-cycle (dpeaa)DE-He213 Singh, Jaiteg aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 123(2021), 3 vom: 10. Nov., Seite 1967-1985 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:123 year:2021 number:3 day:10 month:11 pages:1967-1985 https://dx.doi.org/10.1007/s11277-021-09224-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 123 2021 3 10 11 1967-1985
allfieldsSound	10.1007/s11277-021-09224-2 doi (DE-627)SPR046563083 (SPR)s11277-021-09224-2-e DE-627 ger DE-627 rakwb eng Gandhi, Vidhyotma verfasserin (orcid)0000-0003-2359-4553 aut Gait Adaptive Duty Cycle: Optimize the QoS of WBSN-HAR 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract To proliferate the traditional healthcare industry, the deep research is going on Wireless Body Sensor Network (WBSN). Many healthcare deployment models are designed for continuous and uninterrupted remote health monitoring system. But the Quality of Standard like energy, reliability and accuracy are to be pinned more. The Gait Adaptive Duty Cycle-Human Activity Recognition (GADC-HAR) is proposed with better performance in terms of energy, reliability and accuracy. To enhance the performance of model the two techniques were adopted: (i) design Energy Efficient and Reliable algorithm for the network coding (ii) optimization of sleep/wake timer to synchronise Controller Node with relay node. The performance validation is done with the real time implementation of GADC-HAR. The forty subjects (young and adult with same gender ratio) are examined with a 360 s activity pattern, a strategic process of self-optimization is adopted for gait cycle synchronization. At the end model is evaluated as 48.5% more energy efficient and packet loss ratio is reduced by 7.92%. Based on the gait cycle, population categories are sub-categorised in normal young/adult and fast young/adult subjects. GADC-HAR young normal 10.45%, young fast 11.28%, adult normal 25% and adult fast 25.13% were more accurate than WBSN-HAR generic model. WBSN (dpeaa)DE-He213 Human activity recognition (dpeaa)DE-He213 Gait-cycle (dpeaa)DE-He213 Energy efficiency (dpeaa)DE-He213 Duty-cycle (dpeaa)DE-He213 Singh, Jaiteg aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 123(2021), 3 vom: 10. Nov., Seite 1967-1985 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:123 year:2021 number:3 day:10 month:11 pages:1967-1985 https://dx.doi.org/10.1007/s11277-021-09224-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 123 2021 3 10 11 1967-1985
language	English
source	Enthalten in Wireless personal communications 123(2021), 3 vom: 10. Nov., Seite 1967-1985 volume:123 year:2021 number:3 day:10 month:11 pages:1967-1985
sourceStr	Enthalten in Wireless personal communications 123(2021), 3 vom: 10. Nov., Seite 1967-1985 volume:123 year:2021 number:3 day:10 month:11 pages:1967-1985
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	WBSN Human activity recognition Gait-cycle Energy efficiency Duty-cycle
isfreeaccess_bool	false
container_title	Wireless personal communications
authorswithroles_txt_mv	Gandhi, Vidhyotma @@aut@@ Singh, Jaiteg @@aut@@
publishDateDaySort_date	2021-11-10T00:00:00Z
hierarchy_top_id	271179120
id	SPR046563083
language_de	englisch
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author	Gandhi, Vidhyotma
spellingShingle	Gandhi, Vidhyotma misc WBSN misc Human activity recognition misc Gait-cycle misc Energy efficiency misc Duty-cycle Gait Adaptive Duty Cycle: Optimize the QoS of WBSN-HAR
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topic_title	Gait Adaptive Duty Cycle: Optimize the QoS of WBSN-HAR WBSN (dpeaa)DE-He213 Human activity recognition (dpeaa)DE-He213 Gait-cycle (dpeaa)DE-He213 Energy efficiency (dpeaa)DE-He213 Duty-cycle (dpeaa)DE-He213
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title	Gait Adaptive Duty Cycle: Optimize the QoS of WBSN-HAR
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title_full	Gait Adaptive Duty Cycle: Optimize the QoS of WBSN-HAR
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title_sort	gait adaptive duty cycle: optimize the qos of wbsn-har
title_auth	Gait Adaptive Duty Cycle: Optimize the QoS of WBSN-HAR
abstract	Abstract To proliferate the traditional healthcare industry, the deep research is going on Wireless Body Sensor Network (WBSN). Many healthcare deployment models are designed for continuous and uninterrupted remote health monitoring system. But the Quality of Standard like energy, reliability and accuracy are to be pinned more. The Gait Adaptive Duty Cycle-Human Activity Recognition (GADC-HAR) is proposed with better performance in terms of energy, reliability and accuracy. To enhance the performance of model the two techniques were adopted: (i) design Energy Efficient and Reliable algorithm for the network coding (ii) optimization of sleep/wake timer to synchronise Controller Node with relay node. The performance validation is done with the real time implementation of GADC-HAR. The forty subjects (young and adult with same gender ratio) are examined with a 360 s activity pattern, a strategic process of self-optimization is adopted for gait cycle synchronization. At the end model is evaluated as 48.5% more energy efficient and packet loss ratio is reduced by 7.92%. Based on the gait cycle, population categories are sub-categorised in normal young/adult and fast young/adult subjects. GADC-HAR young normal 10.45%, young fast 11.28%, adult normal 25% and adult fast 25.13% were more accurate than WBSN-HAR generic model. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021
abstractGer	Abstract To proliferate the traditional healthcare industry, the deep research is going on Wireless Body Sensor Network (WBSN). Many healthcare deployment models are designed for continuous and uninterrupted remote health monitoring system. But the Quality of Standard like energy, reliability and accuracy are to be pinned more. The Gait Adaptive Duty Cycle-Human Activity Recognition (GADC-HAR) is proposed with better performance in terms of energy, reliability and accuracy. To enhance the performance of model the two techniques were adopted: (i) design Energy Efficient and Reliable algorithm for the network coding (ii) optimization of sleep/wake timer to synchronise Controller Node with relay node. The performance validation is done with the real time implementation of GADC-HAR. The forty subjects (young and adult with same gender ratio) are examined with a 360 s activity pattern, a strategic process of self-optimization is adopted for gait cycle synchronization. At the end model is evaluated as 48.5% more energy efficient and packet loss ratio is reduced by 7.92%. Based on the gait cycle, population categories are sub-categorised in normal young/adult and fast young/adult subjects. GADC-HAR young normal 10.45%, young fast 11.28%, adult normal 25% and adult fast 25.13% were more accurate than WBSN-HAR generic model. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021
abstract_unstemmed	Abstract To proliferate the traditional healthcare industry, the deep research is going on Wireless Body Sensor Network (WBSN). Many healthcare deployment models are designed for continuous and uninterrupted remote health monitoring system. But the Quality of Standard like energy, reliability and accuracy are to be pinned more. The Gait Adaptive Duty Cycle-Human Activity Recognition (GADC-HAR) is proposed with better performance in terms of energy, reliability and accuracy. To enhance the performance of model the two techniques were adopted: (i) design Energy Efficient and Reliable algorithm for the network coding (ii) optimization of sleep/wake timer to synchronise Controller Node with relay node. The performance validation is done with the real time implementation of GADC-HAR. The forty subjects (young and adult with same gender ratio) are examined with a 360 s activity pattern, a strategic process of self-optimization is adopted for gait cycle synchronization. At the end model is evaluated as 48.5% more energy efficient and packet loss ratio is reduced by 7.92%. Based on the gait cycle, population categories are sub-categorised in normal young/adult and fast young/adult subjects. GADC-HAR young normal 10.45%, young fast 11.28%, adult normal 25% and adult fast 25.13% were more accurate than WBSN-HAR generic model. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021
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title_short	Gait Adaptive Duty Cycle: Optimize the QoS of WBSN-HAR
url	https://dx.doi.org/10.1007/s11277-021-09224-2
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Many healthcare deployment models are designed for continuous and uninterrupted remote health monitoring system. But the Quality of Standard like energy, reliability and accuracy are to be pinned more. The Gait Adaptive Duty Cycle-Human Activity Recognition (GADC-HAR) is proposed with better performance in terms of energy, reliability and accuracy. To enhance the performance of model the two techniques were adopted: (i) design Energy Efficient and Reliable algorithm for the network coding (ii) optimization of sleep/wake timer to synchronise Controller Node with relay node. The performance validation is done with the real time implementation of GADC-HAR. The forty subjects (young and adult with same gender ratio) are examined with a 360 s activity pattern, a strategic process of self-optimization is adopted for gait cycle synchronization. At the end model is evaluated as 48.5% more energy efficient and packet loss ratio is reduced by 7.92%. Based on the gait cycle, population categories are sub-categorised in normal young/adult and fast young/adult subjects. 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