Using model’s temporal features and hierarchical structure for similar activity recognition
Abstract Using Sensor-based approach in activity recognition usually requires the deployment of many ambient sensors to objects and environments. Each sensor can be triggered by more than one activity, e.g., a touch sensor of a cooker can be triggered by cooking, doing dish and so on. An activity co...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Li, Qingjuan [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag GmbH Germany, part of Springer Nature 2020 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of ambient intelligence and humanized computing - Berlin : Springer, 2010, 14(2020), 5 vom: 26. Mai, Seite 5239-5248 |
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| Übergeordnetes Werk: |
volume:14 ; year:2020 ; number:5 ; day:26 ; month:05 ; pages:5239-5248 |
| Links: |
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| DOI / URN: |
10.1007/s12652-020-02035-6 |
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| Katalog-ID: |
SPR051607840 |
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| 520 | |a Abstract Using Sensor-based approach in activity recognition usually requires the deployment of many ambient sensors to objects and environments. Each sensor can be triggered by more than one activity, e.g., a touch sensor of a cooker can be triggered by cooking, doing dish and so on. An activity consists of some sensor events. When the number of same sensors are in the majority of two activities, the two activities are defined as similar activities which are difficult to distinguish. To address the challenge of recognizing similar activities, this paper conceives a new activity recognition approach incorporating high-dimensional features of duration and time block characteristics to improve the inference performance. In a further step, we take advantage of these similar activities to build a hierarchical structure model which can improve capacities of expandability and standardization. We design experiments of similar activity in our daily life to evaluate this solution. The results show that high-dimensional temporal features improved similar activity inference accuracy on an average of 1.88 times, and the use of hierarchical structure can generalize specific rules to standard ones which decreases similar activity recognition computation time on an average of 0.36 times. | ||
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| 650 | 4 | |a Hierarchical structure |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Mao, Lingfeng |4 aut | |
| 700 | 1 | |a Chen, Liming |4 aut | |
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10.1007/s12652-020-02035-6 doi (DE-627)SPR051607840 (SPR)s12652-020-02035-6-e DE-627 ger DE-627 rakwb eng Li, Qingjuan verfasserin aut Using model’s temporal features and hierarchical structure for similar activity recognition 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract Using Sensor-based approach in activity recognition usually requires the deployment of many ambient sensors to objects and environments. Each sensor can be triggered by more than one activity, e.g., a touch sensor of a cooker can be triggered by cooking, doing dish and so on. An activity consists of some sensor events. When the number of same sensors are in the majority of two activities, the two activities are defined as similar activities which are difficult to distinguish. To address the challenge of recognizing similar activities, this paper conceives a new activity recognition approach incorporating high-dimensional features of duration and time block characteristics to improve the inference performance. In a further step, we take advantage of these similar activities to build a hierarchical structure model which can improve capacities of expandability and standardization. We design experiments of similar activity in our daily life to evaluate this solution. The results show that high-dimensional temporal features improved similar activity inference accuracy on an average of 1.88 times, and the use of hierarchical structure can generalize specific rules to standard ones which decreases similar activity recognition computation time on an average of 0.36 times. Activity recognition (dpeaa)DE-He213 Similar activity (dpeaa)DE-He213 Hierarchical structure (dpeaa)DE-He213 Markov logic network (dpeaa)DE-He213 Ning, Huansheng (orcid)0000-0001-6413-193X aut Mao, Lingfeng aut Chen, Liming aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 14(2020), 5 vom: 26. Mai, Seite 5239-5248 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:14 year:2020 number:5 day:26 month:05 pages:5239-5248 https://dx.doi.org/10.1007/s12652-020-02035-6 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_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_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_4277 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 14 2020 5 26 05 5239-5248 |
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10.1007/s12652-020-02035-6 doi (DE-627)SPR051607840 (SPR)s12652-020-02035-6-e DE-627 ger DE-627 rakwb eng Li, Qingjuan verfasserin aut Using model’s temporal features and hierarchical structure for similar activity recognition 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract Using Sensor-based approach in activity recognition usually requires the deployment of many ambient sensors to objects and environments. Each sensor can be triggered by more than one activity, e.g., a touch sensor of a cooker can be triggered by cooking, doing dish and so on. An activity consists of some sensor events. When the number of same sensors are in the majority of two activities, the two activities are defined as similar activities which are difficult to distinguish. To address the challenge of recognizing similar activities, this paper conceives a new activity recognition approach incorporating high-dimensional features of duration and time block characteristics to improve the inference performance. In a further step, we take advantage of these similar activities to build a hierarchical structure model which can improve capacities of expandability and standardization. We design experiments of similar activity in our daily life to evaluate this solution. The results show that high-dimensional temporal features improved similar activity inference accuracy on an average of 1.88 times, and the use of hierarchical structure can generalize specific rules to standard ones which decreases similar activity recognition computation time on an average of 0.36 times. Activity recognition (dpeaa)DE-He213 Similar activity (dpeaa)DE-He213 Hierarchical structure (dpeaa)DE-He213 Markov logic network (dpeaa)DE-He213 Ning, Huansheng (orcid)0000-0001-6413-193X aut Mao, Lingfeng aut Chen, Liming aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 14(2020), 5 vom: 26. Mai, Seite 5239-5248 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:14 year:2020 number:5 day:26 month:05 pages:5239-5248 https://dx.doi.org/10.1007/s12652-020-02035-6 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_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_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_4277 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 14 2020 5 26 05 5239-5248 |
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10.1007/s12652-020-02035-6 doi (DE-627)SPR051607840 (SPR)s12652-020-02035-6-e DE-627 ger DE-627 rakwb eng Li, Qingjuan verfasserin aut Using model’s temporal features and hierarchical structure for similar activity recognition 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract Using Sensor-based approach in activity recognition usually requires the deployment of many ambient sensors to objects and environments. Each sensor can be triggered by more than one activity, e.g., a touch sensor of a cooker can be triggered by cooking, doing dish and so on. An activity consists of some sensor events. When the number of same sensors are in the majority of two activities, the two activities are defined as similar activities which are difficult to distinguish. To address the challenge of recognizing similar activities, this paper conceives a new activity recognition approach incorporating high-dimensional features of duration and time block characteristics to improve the inference performance. In a further step, we take advantage of these similar activities to build a hierarchical structure model which can improve capacities of expandability and standardization. We design experiments of similar activity in our daily life to evaluate this solution. The results show that high-dimensional temporal features improved similar activity inference accuracy on an average of 1.88 times, and the use of hierarchical structure can generalize specific rules to standard ones which decreases similar activity recognition computation time on an average of 0.36 times. Activity recognition (dpeaa)DE-He213 Similar activity (dpeaa)DE-He213 Hierarchical structure (dpeaa)DE-He213 Markov logic network (dpeaa)DE-He213 Ning, Huansheng (orcid)0000-0001-6413-193X aut Mao, Lingfeng aut Chen, Liming aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 14(2020), 5 vom: 26. Mai, Seite 5239-5248 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:14 year:2020 number:5 day:26 month:05 pages:5239-5248 https://dx.doi.org/10.1007/s12652-020-02035-6 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_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_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_4277 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 14 2020 5 26 05 5239-5248 |
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10.1007/s12652-020-02035-6 doi (DE-627)SPR051607840 (SPR)s12652-020-02035-6-e DE-627 ger DE-627 rakwb eng Li, Qingjuan verfasserin aut Using model’s temporal features and hierarchical structure for similar activity recognition 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract Using Sensor-based approach in activity recognition usually requires the deployment of many ambient sensors to objects and environments. Each sensor can be triggered by more than one activity, e.g., a touch sensor of a cooker can be triggered by cooking, doing dish and so on. An activity consists of some sensor events. When the number of same sensors are in the majority of two activities, the two activities are defined as similar activities which are difficult to distinguish. To address the challenge of recognizing similar activities, this paper conceives a new activity recognition approach incorporating high-dimensional features of duration and time block characteristics to improve the inference performance. In a further step, we take advantage of these similar activities to build a hierarchical structure model which can improve capacities of expandability and standardization. We design experiments of similar activity in our daily life to evaluate this solution. The results show that high-dimensional temporal features improved similar activity inference accuracy on an average of 1.88 times, and the use of hierarchical structure can generalize specific rules to standard ones which decreases similar activity recognition computation time on an average of 0.36 times. Activity recognition (dpeaa)DE-He213 Similar activity (dpeaa)DE-He213 Hierarchical structure (dpeaa)DE-He213 Markov logic network (dpeaa)DE-He213 Ning, Huansheng (orcid)0000-0001-6413-193X aut Mao, Lingfeng aut Chen, Liming aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 14(2020), 5 vom: 26. Mai, Seite 5239-5248 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:14 year:2020 number:5 day:26 month:05 pages:5239-5248 https://dx.doi.org/10.1007/s12652-020-02035-6 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_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_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_4277 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 14 2020 5 26 05 5239-5248 |
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10.1007/s12652-020-02035-6 doi (DE-627)SPR051607840 (SPR)s12652-020-02035-6-e DE-627 ger DE-627 rakwb eng Li, Qingjuan verfasserin aut Using model’s temporal features and hierarchical structure for similar activity recognition 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract Using Sensor-based approach in activity recognition usually requires the deployment of many ambient sensors to objects and environments. Each sensor can be triggered by more than one activity, e.g., a touch sensor of a cooker can be triggered by cooking, doing dish and so on. An activity consists of some sensor events. When the number of same sensors are in the majority of two activities, the two activities are defined as similar activities which are difficult to distinguish. To address the challenge of recognizing similar activities, this paper conceives a new activity recognition approach incorporating high-dimensional features of duration and time block characteristics to improve the inference performance. In a further step, we take advantage of these similar activities to build a hierarchical structure model which can improve capacities of expandability and standardization. We design experiments of similar activity in our daily life to evaluate this solution. The results show that high-dimensional temporal features improved similar activity inference accuracy on an average of 1.88 times, and the use of hierarchical structure can generalize specific rules to standard ones which decreases similar activity recognition computation time on an average of 0.36 times. Activity recognition (dpeaa)DE-He213 Similar activity (dpeaa)DE-He213 Hierarchical structure (dpeaa)DE-He213 Markov logic network (dpeaa)DE-He213 Ning, Huansheng (orcid)0000-0001-6413-193X aut Mao, Lingfeng aut Chen, Liming aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 14(2020), 5 vom: 26. Mai, Seite 5239-5248 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:14 year:2020 number:5 day:26 month:05 pages:5239-5248 https://dx.doi.org/10.1007/s12652-020-02035-6 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_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_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_4277 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 14 2020 5 26 05 5239-5248 |
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using model’s temporal features and hierarchical structure for similar activity recognition |
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Using model’s temporal features and hierarchical structure for similar activity recognition |
| abstract |
Abstract Using Sensor-based approach in activity recognition usually requires the deployment of many ambient sensors to objects and environments. Each sensor can be triggered by more than one activity, e.g., a touch sensor of a cooker can be triggered by cooking, doing dish and so on. An activity consists of some sensor events. When the number of same sensors are in the majority of two activities, the two activities are defined as similar activities which are difficult to distinguish. To address the challenge of recognizing similar activities, this paper conceives a new activity recognition approach incorporating high-dimensional features of duration and time block characteristics to improve the inference performance. In a further step, we take advantage of these similar activities to build a hierarchical structure model which can improve capacities of expandability and standardization. We design experiments of similar activity in our daily life to evaluate this solution. The results show that high-dimensional temporal features improved similar activity inference accuracy on an average of 1.88 times, and the use of hierarchical structure can generalize specific rules to standard ones which decreases similar activity recognition computation time on an average of 0.36 times. © Springer-Verlag GmbH Germany, part of Springer Nature 2020 |
| abstractGer |
Abstract Using Sensor-based approach in activity recognition usually requires the deployment of many ambient sensors to objects and environments. Each sensor can be triggered by more than one activity, e.g., a touch sensor of a cooker can be triggered by cooking, doing dish and so on. An activity consists of some sensor events. When the number of same sensors are in the majority of two activities, the two activities are defined as similar activities which are difficult to distinguish. To address the challenge of recognizing similar activities, this paper conceives a new activity recognition approach incorporating high-dimensional features of duration and time block characteristics to improve the inference performance. In a further step, we take advantage of these similar activities to build a hierarchical structure model which can improve capacities of expandability and standardization. We design experiments of similar activity in our daily life to evaluate this solution. The results show that high-dimensional temporal features improved similar activity inference accuracy on an average of 1.88 times, and the use of hierarchical structure can generalize specific rules to standard ones which decreases similar activity recognition computation time on an average of 0.36 times. © Springer-Verlag GmbH Germany, part of Springer Nature 2020 |
| abstract_unstemmed |
Abstract Using Sensor-based approach in activity recognition usually requires the deployment of many ambient sensors to objects and environments. Each sensor can be triggered by more than one activity, e.g., a touch sensor of a cooker can be triggered by cooking, doing dish and so on. An activity consists of some sensor events. When the number of same sensors are in the majority of two activities, the two activities are defined as similar activities which are difficult to distinguish. To address the challenge of recognizing similar activities, this paper conceives a new activity recognition approach incorporating high-dimensional features of duration and time block characteristics to improve the inference performance. In a further step, we take advantage of these similar activities to build a hierarchical structure model which can improve capacities of expandability and standardization. We design experiments of similar activity in our daily life to evaluate this solution. The results show that high-dimensional temporal features improved similar activity inference accuracy on an average of 1.88 times, and the use of hierarchical structure can generalize specific rules to standard ones which decreases similar activity recognition computation time on an average of 0.36 times. © Springer-Verlag GmbH Germany, part of Springer Nature 2020 |
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Using model’s temporal features and hierarchical structure for similar activity recognition |
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https://dx.doi.org/10.1007/s12652-020-02035-6 |
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Ning, Huansheng Mao, Lingfeng Chen, Liming |
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10.1007/s12652-020-02035-6 |
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| score |
7.401121 |