Online updating extended belief rule-based system for sensor-based activity recognition

Sensor-based activity recognition (AR) is a core problem with the research domain of smart environments. It has, however, the potential to provide solutions to address the problems associated with the growing size and ageing profile of the global population. The work presented within this paper focu...
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Gespeichert in:

Autor*in:	Yang, Long-Hao [verfasserIn] Liu, Jun [verfasserIn] Wang, Ying-Ming [verfasserIn] Nugent, Chris [verfasserIn] Martínez, Luis [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Extended belief rule base Online model updating Feature selection Activity recognition Smart environment

Übergeordnetes Werk:	Enthalten in: Expert systems with applications - Amsterdam [u.a.] : Elsevier Science, 1990, 186
Übergeordnetes Werk:	volume:186

DOI / URN:	10.1016/j.eswa.2021.115737

Katalog-ID:	ELV006790771

Internformat


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520			\|a Sensor-based activity recognition (AR) is a core problem with the research domain of smart environments. It has, however, the potential to provide solutions to address the problems associated with the growing size and ageing profile of the global population. The work presented within this paper focuses on the extended belief rule-based system (EBRBS), which offered promising performance compared with popular benchmark AR models and exhibited a high robustness in the situation of sensor failure. Nevertheless, efficiency remains one of the major issues to be improved for determining and updating the extended belief rule base (EBRB) within the EBRBS. This is critical for further utilizing the EBRBS in AR situations within dynamic smart environments. An eigendecomposition-based sensor selection method is firstly proposed to select an effective subset of sensors and to also enable efficient implementation to facilitate online AR. A novel domain division-based rule generation method is also proposed to generate and update an EBRB efficiently when new sensor data are available or when some sensors should be included or excluded in the EBRB. The combination of these two methods leads to an enhanced EBRBS, called online updating EBRBS. Two datasets (in a balanced class situation) obtained from simulation and actual environments are studied to provide detailed experimental analysis as a preliminary study and basis to handle further the imbalanced situation of real AR. The experimental results demonstrate an enhanced performance of the online updating EBRBS compared with the original EBRBS and some benchmark AR models, in terms of efficiency and effectiveness.
650		4	\|a Extended belief rule base
650		4	\|a Online model updating
650		4	\|a Feature selection
650		4	\|a Activity recognition
650		4	\|a Smart environment
700	1		\|a Liu, Jun \|e verfasserin \|4 aut
700	1		\|a Wang, Ying-Ming \|e verfasserin \|4 aut
700	1		\|a Nugent, Chris \|e verfasserin \|4 aut
700	1		\|a Martínez, Luis \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Expert systems with applications \|d Amsterdam [u.a.] : Elsevier Science, 1990 \|g 186 \|h Online-Ressource \|w (DE-627)320577961 \|w (DE-600)2017237-0 \|w (DE-576)11481807X \|7 nnns
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Indexfelder

author_variant	l h y lhy j l jl y m w ymw c n cn l m lm
matchkey_str	yanglonghaoliujunwangyingmingnugentchris:2021----:niepaigxeddeifueaessefresrae
hierarchy_sort_str	2021
bklnumber	54.72
publishDate	2021
allfields	10.1016/j.eswa.2021.115737 doi (DE-627)ELV006790771 (ELSEVIER)S0957-4174(21)01116-7 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Yang, Long-Hao verfasserin aut Online updating extended belief rule-based system for sensor-based activity recognition 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Sensor-based activity recognition (AR) is a core problem with the research domain of smart environments. It has, however, the potential to provide solutions to address the problems associated with the growing size and ageing profile of the global population. The work presented within this paper focuses on the extended belief rule-based system (EBRBS), which offered promising performance compared with popular benchmark AR models and exhibited a high robustness in the situation of sensor failure. Nevertheless, efficiency remains one of the major issues to be improved for determining and updating the extended belief rule base (EBRB) within the EBRBS. This is critical for further utilizing the EBRBS in AR situations within dynamic smart environments. An eigendecomposition-based sensor selection method is firstly proposed to select an effective subset of sensors and to also enable efficient implementation to facilitate online AR. A novel domain division-based rule generation method is also proposed to generate and update an EBRB efficiently when new sensor data are available or when some sensors should be included or excluded in the EBRB. The combination of these two methods leads to an enhanced EBRBS, called online updating EBRBS. Two datasets (in a balanced class situation) obtained from simulation and actual environments are studied to provide detailed experimental analysis as a preliminary study and basis to handle further the imbalanced situation of real AR. The experimental results demonstrate an enhanced performance of the online updating EBRBS compared with the original EBRBS and some benchmark AR models, in terms of efficiency and effectiveness. Extended belief rule base Online model updating Feature selection Activity recognition Smart environment Liu, Jun verfasserin aut Wang, Ying-Ming verfasserin aut Nugent, Chris verfasserin aut Martínez, Luis verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 186 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:186 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz VZ AR 186
spelling	10.1016/j.eswa.2021.115737 doi (DE-627)ELV006790771 (ELSEVIER)S0957-4174(21)01116-7 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Yang, Long-Hao verfasserin aut Online updating extended belief rule-based system for sensor-based activity recognition 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Sensor-based activity recognition (AR) is a core problem with the research domain of smart environments. It has, however, the potential to provide solutions to address the problems associated with the growing size and ageing profile of the global population. The work presented within this paper focuses on the extended belief rule-based system (EBRBS), which offered promising performance compared with popular benchmark AR models and exhibited a high robustness in the situation of sensor failure. Nevertheless, efficiency remains one of the major issues to be improved for determining and updating the extended belief rule base (EBRB) within the EBRBS. This is critical for further utilizing the EBRBS in AR situations within dynamic smart environments. An eigendecomposition-based sensor selection method is firstly proposed to select an effective subset of sensors and to also enable efficient implementation to facilitate online AR. A novel domain division-based rule generation method is also proposed to generate and update an EBRB efficiently when new sensor data are available or when some sensors should be included or excluded in the EBRB. The combination of these two methods leads to an enhanced EBRBS, called online updating EBRBS. Two datasets (in a balanced class situation) obtained from simulation and actual environments are studied to provide detailed experimental analysis as a preliminary study and basis to handle further the imbalanced situation of real AR. The experimental results demonstrate an enhanced performance of the online updating EBRBS compared with the original EBRBS and some benchmark AR models, in terms of efficiency and effectiveness. Extended belief rule base Online model updating Feature selection Activity recognition Smart environment Liu, Jun verfasserin aut Wang, Ying-Ming verfasserin aut Nugent, Chris verfasserin aut Martínez, Luis verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 186 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:186 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz VZ AR 186
allfields_unstemmed	10.1016/j.eswa.2021.115737 doi (DE-627)ELV006790771 (ELSEVIER)S0957-4174(21)01116-7 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Yang, Long-Hao verfasserin aut Online updating extended belief rule-based system for sensor-based activity recognition 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Sensor-based activity recognition (AR) is a core problem with the research domain of smart environments. It has, however, the potential to provide solutions to address the problems associated with the growing size and ageing profile of the global population. The work presented within this paper focuses on the extended belief rule-based system (EBRBS), which offered promising performance compared with popular benchmark AR models and exhibited a high robustness in the situation of sensor failure. Nevertheless, efficiency remains one of the major issues to be improved for determining and updating the extended belief rule base (EBRB) within the EBRBS. This is critical for further utilizing the EBRBS in AR situations within dynamic smart environments. An eigendecomposition-based sensor selection method is firstly proposed to select an effective subset of sensors and to also enable efficient implementation to facilitate online AR. A novel domain division-based rule generation method is also proposed to generate and update an EBRB efficiently when new sensor data are available or when some sensors should be included or excluded in the EBRB. The combination of these two methods leads to an enhanced EBRBS, called online updating EBRBS. Two datasets (in a balanced class situation) obtained from simulation and actual environments are studied to provide detailed experimental analysis as a preliminary study and basis to handle further the imbalanced situation of real AR. The experimental results demonstrate an enhanced performance of the online updating EBRBS compared with the original EBRBS and some benchmark AR models, in terms of efficiency and effectiveness. Extended belief rule base Online model updating Feature selection Activity recognition Smart environment Liu, Jun verfasserin aut Wang, Ying-Ming verfasserin aut Nugent, Chris verfasserin aut Martínez, Luis verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 186 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:186 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz VZ AR 186
allfieldsGer	10.1016/j.eswa.2021.115737 doi (DE-627)ELV006790771 (ELSEVIER)S0957-4174(21)01116-7 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Yang, Long-Hao verfasserin aut Online updating extended belief rule-based system for sensor-based activity recognition 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Sensor-based activity recognition (AR) is a core problem with the research domain of smart environments. It has, however, the potential to provide solutions to address the problems associated with the growing size and ageing profile of the global population. The work presented within this paper focuses on the extended belief rule-based system (EBRBS), which offered promising performance compared with popular benchmark AR models and exhibited a high robustness in the situation of sensor failure. Nevertheless, efficiency remains one of the major issues to be improved for determining and updating the extended belief rule base (EBRB) within the EBRBS. This is critical for further utilizing the EBRBS in AR situations within dynamic smart environments. An eigendecomposition-based sensor selection method is firstly proposed to select an effective subset of sensors and to also enable efficient implementation to facilitate online AR. A novel domain division-based rule generation method is also proposed to generate and update an EBRB efficiently when new sensor data are available or when some sensors should be included or excluded in the EBRB. The combination of these two methods leads to an enhanced EBRBS, called online updating EBRBS. Two datasets (in a balanced class situation) obtained from simulation and actual environments are studied to provide detailed experimental analysis as a preliminary study and basis to handle further the imbalanced situation of real AR. The experimental results demonstrate an enhanced performance of the online updating EBRBS compared with the original EBRBS and some benchmark AR models, in terms of efficiency and effectiveness. Extended belief rule base Online model updating Feature selection Activity recognition Smart environment Liu, Jun verfasserin aut Wang, Ying-Ming verfasserin aut Nugent, Chris verfasserin aut Martínez, Luis verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 186 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:186 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz VZ AR 186
allfieldsSound	10.1016/j.eswa.2021.115737 doi (DE-627)ELV006790771 (ELSEVIER)S0957-4174(21)01116-7 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Yang, Long-Hao verfasserin aut Online updating extended belief rule-based system for sensor-based activity recognition 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Sensor-based activity recognition (AR) is a core problem with the research domain of smart environments. It has, however, the potential to provide solutions to address the problems associated with the growing size and ageing profile of the global population. The work presented within this paper focuses on the extended belief rule-based system (EBRBS), which offered promising performance compared with popular benchmark AR models and exhibited a high robustness in the situation of sensor failure. Nevertheless, efficiency remains one of the major issues to be improved for determining and updating the extended belief rule base (EBRB) within the EBRBS. This is critical for further utilizing the EBRBS in AR situations within dynamic smart environments. An eigendecomposition-based sensor selection method is firstly proposed to select an effective subset of sensors and to also enable efficient implementation to facilitate online AR. A novel domain division-based rule generation method is also proposed to generate and update an EBRB efficiently when new sensor data are available or when some sensors should be included or excluded in the EBRB. The combination of these two methods leads to an enhanced EBRBS, called online updating EBRBS. Two datasets (in a balanced class situation) obtained from simulation and actual environments are studied to provide detailed experimental analysis as a preliminary study and basis to handle further the imbalanced situation of real AR. The experimental results demonstrate an enhanced performance of the online updating EBRBS compared with the original EBRBS and some benchmark AR models, in terms of efficiency and effectiveness. Extended belief rule base Online model updating Feature selection Activity recognition Smart environment Liu, Jun verfasserin aut Wang, Ying-Ming verfasserin aut Nugent, Chris verfasserin aut Martínez, Luis verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 186 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:186 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz VZ AR 186
language	English
source	Enthalten in Expert systems with applications 186 volume:186
sourceStr	Enthalten in Expert systems with applications 186 volume:186
format_phy_str_mv	Article
bklname	Künstliche Intelligenz
institution	findex.gbv.de
topic_facet	Extended belief rule base Online model updating Feature selection Activity recognition Smart environment
dewey-raw	004
isfreeaccess_bool	false
container_title	Expert systems with applications
authorswithroles_txt_mv	Yang, Long-Hao @@aut@@ Liu, Jun @@aut@@ Wang, Ying-Ming @@aut@@ Nugent, Chris @@aut@@ Martínez, Luis @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	320577961
dewey-sort	14
id	ELV006790771
language_de	englisch
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author	Yang, Long-Hao
spellingShingle	Yang, Long-Hao ddc 004 bkl 54.72 misc Extended belief rule base misc Online model updating misc Feature selection misc Activity recognition misc Smart environment Online updating extended belief rule-based system for sensor-based activity recognition
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topic_title	004 VZ 54.72 bkl Online updating extended belief rule-based system for sensor-based activity recognition Extended belief rule base Online model updating Feature selection Activity recognition Smart environment
topic	ddc 004 bkl 54.72 misc Extended belief rule base misc Online model updating misc Feature selection misc Activity recognition misc Smart environment
topic_unstemmed	ddc 004 bkl 54.72 misc Extended belief rule base misc Online model updating misc Feature selection misc Activity recognition misc Smart environment
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title	Online updating extended belief rule-based system for sensor-based activity recognition
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title_full	Online updating extended belief rule-based system for sensor-based activity recognition
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title_sort	online updating extended belief rule-based system for sensor-based activity recognition
title_auth	Online updating extended belief rule-based system for sensor-based activity recognition
abstract	Sensor-based activity recognition (AR) is a core problem with the research domain of smart environments. It has, however, the potential to provide solutions to address the problems associated with the growing size and ageing profile of the global population. The work presented within this paper focuses on the extended belief rule-based system (EBRBS), which offered promising performance compared with popular benchmark AR models and exhibited a high robustness in the situation of sensor failure. Nevertheless, efficiency remains one of the major issues to be improved for determining and updating the extended belief rule base (EBRB) within the EBRBS. This is critical for further utilizing the EBRBS in AR situations within dynamic smart environments. An eigendecomposition-based sensor selection method is firstly proposed to select an effective subset of sensors and to also enable efficient implementation to facilitate online AR. A novel domain division-based rule generation method is also proposed to generate and update an EBRB efficiently when new sensor data are available or when some sensors should be included or excluded in the EBRB. The combination of these two methods leads to an enhanced EBRBS, called online updating EBRBS. Two datasets (in a balanced class situation) obtained from simulation and actual environments are studied to provide detailed experimental analysis as a preliminary study and basis to handle further the imbalanced situation of real AR. The experimental results demonstrate an enhanced performance of the online updating EBRBS compared with the original EBRBS and some benchmark AR models, in terms of efficiency and effectiveness.
abstractGer	Sensor-based activity recognition (AR) is a core problem with the research domain of smart environments. It has, however, the potential to provide solutions to address the problems associated with the growing size and ageing profile of the global population. The work presented within this paper focuses on the extended belief rule-based system (EBRBS), which offered promising performance compared with popular benchmark AR models and exhibited a high robustness in the situation of sensor failure. Nevertheless, efficiency remains one of the major issues to be improved for determining and updating the extended belief rule base (EBRB) within the EBRBS. This is critical for further utilizing the EBRBS in AR situations within dynamic smart environments. An eigendecomposition-based sensor selection method is firstly proposed to select an effective subset of sensors and to also enable efficient implementation to facilitate online AR. A novel domain division-based rule generation method is also proposed to generate and update an EBRB efficiently when new sensor data are available or when some sensors should be included or excluded in the EBRB. The combination of these two methods leads to an enhanced EBRBS, called online updating EBRBS. Two datasets (in a balanced class situation) obtained from simulation and actual environments are studied to provide detailed experimental analysis as a preliminary study and basis to handle further the imbalanced situation of real AR. The experimental results demonstrate an enhanced performance of the online updating EBRBS compared with the original EBRBS and some benchmark AR models, in terms of efficiency and effectiveness.
abstract_unstemmed	Sensor-based activity recognition (AR) is a core problem with the research domain of smart environments. It has, however, the potential to provide solutions to address the problems associated with the growing size and ageing profile of the global population. The work presented within this paper focuses on the extended belief rule-based system (EBRBS), which offered promising performance compared with popular benchmark AR models and exhibited a high robustness in the situation of sensor failure. Nevertheless, efficiency remains one of the major issues to be improved for determining and updating the extended belief rule base (EBRB) within the EBRBS. This is critical for further utilizing the EBRBS in AR situations within dynamic smart environments. An eigendecomposition-based sensor selection method is firstly proposed to select an effective subset of sensors and to also enable efficient implementation to facilitate online AR. A novel domain division-based rule generation method is also proposed to generate and update an EBRB efficiently when new sensor data are available or when some sensors should be included or excluded in the EBRB. The combination of these two methods leads to an enhanced EBRBS, called online updating EBRBS. Two datasets (in a balanced class situation) obtained from simulation and actual environments are studied to provide detailed experimental analysis as a preliminary study and basis to handle further the imbalanced situation of real AR. The experimental results demonstrate an enhanced performance of the online updating EBRBS compared with the original EBRBS and some benchmark AR models, in terms of efficiency and effectiveness.
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title_short	Online updating extended belief rule-based system for sensor-based activity recognition
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author2	Liu, Jun Wang, Ying-Ming Nugent, Chris Martínez, Luis
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up_date	2024-07-06T22:34:27.869Z
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Online updating extended belief rule-based system for sensor-based activity recognition

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