A knowledge-driven approach for activity recognition in smart homes based on activity profiling
The Internet of Things (IoT) is a technology for seamlessly connecting a large number of small-end devices and enabling the development of many smart applications to control different aspects of our life; shifting us, ever-closer to living in a smart city. IoT makes it possible to convert our homes...
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| Published in | Future generation computer systems Vol. 107; pp. 924 - 941 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.06.2020
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0167-739X 1872-7115 |
| DOI | 10.1016/j.future.2017.10.031 |
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| Abstract | The Internet of Things (IoT) is a technology for seamlessly connecting a large number of small-end devices and enabling the development of many smart applications to control different aspects of our life; shifting us, ever-closer to living in a smart city. IoT makes it possible to convert our homes to smart environments in which sensors are responsible for handling inhabitants’ behaviours and monitor their daily activities. Activity Recognition (AR) is a new service within smart homes. It has been introduced as a solution to improve the quality of life of people such as elderly and children. AR is concerned with the assignment of an activity label to a sequence of sensors’ events that are generated from the smart infrastructure. To help in effectively recognizing home activities, classification algorithms are applied on segmented sequences that are extracted automatically. Segments are subject to error due to the existence of irrelevant data and difficulties in how segmentation is applied. This negatively affects the accuracy on the classification task. In addition, the data generated from the network is streamed in nature, and big data techniques need to be utilized. In this paper, we propose a model to improve Activity Recognition in smart homes. The proposed technique is based on defining a profile for each activity from training datasets. The profile will be used to induce extra features and will help in distinguishing residents’ activities (fingerprinting). To validate our model, real datasets have been used for the experiments, and results show a significant enhancement in accuracy, compared with traditional techniques.
•Knowledge base solution for activity recognition in smart homes.•A comprehensive framework for data collection, processing and real-time recognition.•Building a distinguished profile for activities based on the chronological order of their sensors. |
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| AbstractList | The Internet of Things (IoT) is a technology for seamlessly connecting a large number of small-end devices and enabling the development of many smart applications to control different aspects of our life; shifting us, ever-closer to living in a smart city. IoT makes it possible to convert our homes to smart environments in which sensors are responsible for handling inhabitants’ behaviours and monitor their daily activities. Activity Recognition (AR) is a new service within smart homes. It has been introduced as a solution to improve the quality of life of people such as elderly and children. AR is concerned with the assignment of an activity label to a sequence of sensors’ events that are generated from the smart infrastructure. To help in effectively recognizing home activities, classification algorithms are applied on segmented sequences that are extracted automatically. Segments are subject to error due to the existence of irrelevant data and difficulties in how segmentation is applied. This negatively affects the accuracy on the classification task. In addition, the data generated from the network is streamed in nature, and big data techniques need to be utilized. In this paper, we propose a model to improve Activity Recognition in smart homes. The proposed technique is based on defining a profile for each activity from training datasets. The profile will be used to induce extra features and will help in distinguishing residents’ activities (fingerprinting). To validate our model, real datasets have been used for the experiments, and results show a significant enhancement in accuracy, compared with traditional techniques.
•Knowledge base solution for activity recognition in smart homes.•A comprehensive framework for data collection, processing and real-time recognition.•Building a distinguished profile for activities based on the chronological order of their sensors. |
| Author | Muhammad, Ghulam Hossain, M. Shamim Samarah, Samer Rawashdeh, Majdi Al Zamil, Mohammed GH |
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| Cites_doi | 10.1016/j.procs.2015.05.130 10.1145/1553374.1553453 10.1109/MC.2012.328 10.1016/j.eswa.2012.08.066 10.5339/qfarc.2014.ITPP0366 10.3109/17538157.2010.506252 10.1002/9780470379424.ch37 10.3390/s120505363 10.1504/IJES.2017.086723 10.1088/0967-3334/30/4/R01 10.1186/1743-0003-9-21 10.1109/MPRV.2014.52 10.1016/j.pmcj.2016.09.010 10.1016/j.pmcj.2012.11.004 10.1109/ACCESS.2017.2685531 10.1109/TSMCB.2012.2216873 10.1016/j.pmcj.2012.06.002 10.1016/j.pmcj.2012.07.003 10.1109/TSMCA.2009.2025137 10.1016/j.neucom.2014.08.069 10.1504/IJICT.2016.074840 10.1016/j.patcog.2017.02.028 10.1145/2499621 10.1016/j.jpdc.2016.10.005 10.1081/SAC-120017494 10.1145/2556288.2557278 |
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| Keywords | Activity recognition Smart cities Internet of Things Data mining Smart homes |
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| References | H. Lee, R. Grosse, R. Ranganath, A.Y. Ng, Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations, in: Proceedings of the 26th Annual International Conference on Machine Learning, ICML, Montreal, QC, Canada, 14–18 June 2009, pp. 609–616. Kranz, Möller, Hammerla, Diewald, Plötz, Olivier, Roalter (b19) 2013; 9 Cook, Krishnan, Rashidi (b24) 2013; 43 A. Avci, S. Bosch, M. Marin-Perianu, R. Marin-Perianu, P. Havinga, Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: A survey, in: Architecture of computing systems, ARCS, 2010 23rd international conference on 2010, February pp. 1-10, VDE. Patel, Park, Bonato, Chan, Rodgers (b20) 2012; 9 Hossain, Rahman, Muhammad (b34) 2017; 103 Samarah, Al Zamil, Aleroud, Rawashdeh, Alhamid, Alamri (b4) 2017; 5 Moskvina, Zhigljavsky (b2) 2003; 32 Al Zamil, Samarah, Rawashdeh, Hossain (b5) 2017 S. Mazilu, U. Blanke, M. Hardegger, G. Tröster, E. Gazit, J.M. Hausdorff, GaitAssist: A daily-life support and training system for parkinson’s disease patients with freezing of gait, in: Proceedings of the ACM Conference on Human Factors in Computing Systems, SIGCHI, Toronto, ON, Canada, 26 April–1 May 2014. Figo, Diniz, Ferreira, Cardoso (b27) 2010; 14 Zamil, Samarah (b13) 2015; 7 M.Gh.Al. Zamil, S. Samarah, Application of design for verification to smart sensory systems, in: Qatar Foundation Annual Research Conference, 2014, November, (No. 1, p. ITPP0366). Zamil, Samarah (b12) 2016; 8 Tapia, Intille, Larson (b6) 2004; 4 Cook, Crandall, Thomas, Krishnan (b7) 2013; 46 Liu, Wang, Su, Huang, Liu (b9) 2017; 68 Fahad, Khan, Rajarajan (b25) 2015; 149 Ordonez, Englebienne, de Toledo, van Kasteren, Sanchis, Krose (b23) 2014; 13 Al Zamil, Can (b18) 2011; 36 Preece, Goulermas, Kenney, Howard, Meijer, Crompton (b22) 2009; 30 Cook, Youngblood, Heierman, Gopalratnam, Rao, Litvin, Khawaja (b10) 2003 De la Torre, Hodgins, Bargteil, Martin, Macey, Collado, Beltran (b8) 2008 L. Liao, D. Fox, H. Kautz, Location-based activity recognition, in: Advances in Neural Information Processing Systems, 2006, pp. 787-794. Hossain, Muhammad, Alamri (b35) 2017 Hossain, Muhammad, Abdul, Song, Gupta (b15) 2017 Minor, Cook (b32) 2017; 38 Zamil (b36) 2017; 9 S. Helal, R. Bose, S. Pickles, H. Elzabadani, J. King, Y. Kaddourah, The gator tech smart house: A programmable pervasive space, in: The Engineering Handbook of Smart Technology for Aging, Disability, and Independence, 2008, pp. 693-709. Bulling, Blanke, Schiele (b21) 2014; 46 Krishnan, Cook (b37) 2014; 10 Okeyo, Chen, Wang, Sterritt (b3) 2014; 10 R.S. Huang, B.C. Chien, Activity recognition on multi-sensor data streams using distinguishing sequential patterns, in: The 27th Annual Conference of the Japanese Society for Artificial Intelligence, 2013, 2A1-IOS-3b-1. Ordóñez, Iglesias, De Toledo, Ledezma, Sanchis (b28) 2013; 40 Han, Han, Lee, Sarkar, Lee (b33) 2012; 12 Rashidi, Cook (b16) 2009; 39 Zamil (b26) 2015; 52 Minor (10.1016/j.future.2017.10.031_b32) 2017; 38 Kranz (10.1016/j.future.2017.10.031_b19) 2013; 9 Cook (10.1016/j.future.2017.10.031_b7) 2013; 46 Rashidi (10.1016/j.future.2017.10.031_b16) 2009; 39 10.1016/j.future.2017.10.031_b31 Zamil (10.1016/j.future.2017.10.031_b12) 2016; 8 10.1016/j.future.2017.10.031_b30 Okeyo (10.1016/j.future.2017.10.031_b3) 2014; 10 Moskvina (10.1016/j.future.2017.10.031_b2) 2003; 32 Tapia (10.1016/j.future.2017.10.031_b6) 2004; 4 Liu (10.1016/j.future.2017.10.031_b9) 2017; 68 De la Torre (10.1016/j.future.2017.10.031_b8) 2008 Zamil (10.1016/j.future.2017.10.031_b26) 2015; 52 Zamil (10.1016/j.future.2017.10.031_b13) 2015; 7 Al Zamil (10.1016/j.future.2017.10.031_b5) 2017 Patel (10.1016/j.future.2017.10.031_b20) 2012; 9 Figo (10.1016/j.future.2017.10.031_b27) 2010; 14 Ordóñez (10.1016/j.future.2017.10.031_b28) 2013; 40 Bulling (10.1016/j.future.2017.10.031_b21) 2014; 46 Zamil (10.1016/j.future.2017.10.031_b36) 2017; 9 10.1016/j.future.2017.10.031_b29 Cook (10.1016/j.future.2017.10.031_b10) 2003 Ordonez (10.1016/j.future.2017.10.031_b23) 2014; 13 Samarah (10.1016/j.future.2017.10.031_b4) 2017; 5 Preece (10.1016/j.future.2017.10.031_b22) 2009; 30 Han (10.1016/j.future.2017.10.031_b33) 2012; 12 Fahad (10.1016/j.future.2017.10.031_b25) 2015; 149 Hossain (10.1016/j.future.2017.10.031_b34) 2017; 103 Krishnan (10.1016/j.future.2017.10.031_b37) 2014; 10 10.1016/j.future.2017.10.031_b1 10.1016/j.future.2017.10.031_b14 Cook (10.1016/j.future.2017.10.031_b24) 2013; 43 Hossain (10.1016/j.future.2017.10.031_b35) 2017 10.1016/j.future.2017.10.031_b17 10.1016/j.future.2017.10.031_b11 Hossain (10.1016/j.future.2017.10.031_b15) 2017 Al Zamil (10.1016/j.future.2017.10.031_b18) 2011; 36 |
| References_xml | – volume: 40 start-page: 1248 year: 2013 end-page: 1255 ident: b28 article-title: Online activity recognition using evolving classifiers publication-title: Expert Syst. Appl. – volume: 10 start-page: 155 year: 2014 end-page: 172 ident: b3 article-title: Dynamic sensor data segmentation for real-time knowledge-driven activity recognition publication-title: Perv. Mob. Comput. – volume: 9 start-page: 413 year: 2017 end-page: 425 ident: b36 article-title: A verifiable framework for smart sensory systems publication-title: Int. J. Embed. Syst. – volume: 9 start-page: 203 year: 2013 end-page: 215 ident: b19 article-title: The mobile fitness coach: Towards individualized skill assessment using personalized mobile devices publication-title: Perv. Mob. Comput. – start-page: 1 year: 2017 end-page: 11 ident: b35 article-title: Smart healthcare monitoring: A voice pathology detection paradigm for smart cities publication-title: Multimedia Syst. – volume: 5 start-page: 3848 year: 2017 end-page: 3859 ident: b4 article-title: An efficient activity recognition framework: Toward privacy-sensitive health data sensing publication-title: IEEE Access – start-page: 135 year: 2008 ident: b8 publication-title: Guide to the Carnegiemellon University Multimodal Activity (cmu-mmac) Database – volume: 52 start-page: 1126 year: 2015 end-page: 1132 ident: b26 article-title: Verifying smart sensory systems on cloud computing frameworks publication-title: Procedia Comput. Sci. – start-page: 521 year: 2003 end-page: 524 ident: b10 article-title: MavHome: An agent-based smart home publication-title: Pervasive Computing and Communications, 2003(PerCom 2003) Proceedings of the First IEEE International Conference on – reference: A. Avci, S. Bosch, M. Marin-Perianu, R. Marin-Perianu, P. Havinga, Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: A survey, in: Architecture of computing systems, ARCS, 2010 23rd international conference on 2010, February pp. 1-10, VDE. – volume: 46 start-page: 62 year: 2013 end-page: 69 ident: b7 article-title: CASAS: A smart home in a box publication-title: Computer – volume: 43 start-page: 820 year: 2013 end-page: 828 ident: b24 article-title: Activity discovery and activity recognition: A new partnership publication-title: IEEE Trans. Cybernet. – volume: 32 start-page: 319 year: 2003 end-page: 352 ident: b2 article-title: An algorithm based on singular spectrum analysis for change-point detection publication-title: Commun. Statist.-Simul. Comput. – reference: R.S. Huang, B.C. Chien, Activity recognition on multi-sensor data streams using distinguishing sequential patterns, in: The 27th Annual Conference of the Japanese Society for Artificial Intelligence, 2013, 2A1-IOS-3b-1. – volume: 7 start-page: 265 year: 2015 end-page: 285 ident: b13 article-title: Dynamic rough-based clustering for vehicular ad-hoc networks publication-title: Int. J. Inform. Decis. Sci. – volume: 14 start-page: 645 year: 2010 end-page: 662 ident: b27 article-title: Preprocessing techniques for context recognition from accelerometer data publication-title: Perv. Mob. Comput. – volume: 46 start-page: 1 year: 2014 end-page: 33 ident: b21 article-title: A tutorial on human activity recognition using body-worn inertial sensors publication-title: ACM Comput. Surv. – volume: 149 start-page: 1286 year: 2015 end-page: 1298 ident: b25 article-title: Activity recognition in smart homes with self-verification of assignments publication-title: Neurocomputing – volume: 103 start-page: 11 year: 2017 end-page: 21 ident: b34 article-title: Cyber–physical cloud-oriented multi-sensory smart home framework for elderly people: An energy efficiency perspective publication-title: J. Parallel Distrib. Comput. – volume: 36 start-page: 100 year: 2011 end-page: 115 ident: b18 article-title: A model based on multi-features to enhance healthcare and medical document retrieval publication-title: Inform. Health Soc. Care – volume: 38 start-page: 77 year: 2017 end-page: 91 ident: b32 article-title: Forecasting occurrences of activities publication-title: Perv. Mob. Comput. – volume: 39 start-page: 949 year: 2009 end-page: 959 ident: b16 article-title: Keeping the resident in the loop: adapting the smart home to the user publication-title: IEEE Trans. Syst. Man Cybernet. A – volume: 12 start-page: 5363 year: 2012 end-page: 5379 ident: b33 article-title: A framework for supervising lifestyle diseases using long-term activity monitoring publication-title: Sensors – reference: M.Gh.Al. Zamil, S. Samarah, Application of design for verification to smart sensory systems, in: Qatar Foundation Annual Research Conference, 2014, November, (No. 1, p. ITPP0366). – volume: 4 start-page: 158 year: 2004 end-page: 175 ident: b6 article-title: , March, Activity recognition in the home using simple and ubiquitous sensors publication-title: Pervasive – reference: H. Lee, R. Grosse, R. Ranganath, A.Y. Ng, Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations, in: Proceedings of the 26th Annual International Conference on Machine Learning, ICML, Montreal, QC, Canada, 14–18 June 2009, pp. 609–616. – volume: 8 start-page: 140 year: 2016 end-page: 164 ident: b12 article-title: Dynamic event classification for intrusion and false alarm detection in vehicular ad hoc networks publication-title: Int. J. Inform. Commun. Technol. – volume: 68 start-page: 295 year: 2017 end-page: 309 ident: b9 article-title: Towards complex activity recognition using a Bayesian network-based probabilistic generative framework publication-title: Pattern Recognit. – reference: S. Helal, R. Bose, S. Pickles, H. Elzabadani, J. King, Y. Kaddourah, The gator tech smart house: A programmable pervasive space, in: The Engineering Handbook of Smart Technology for Aging, Disability, and Independence, 2008, pp. 693-709. – year: 2017 ident: b15 article-title: Cloud-assisted secure video transmission and sharing framework for smart cities publication-title: Future Gener. Comput. Syst. – volume: 9 start-page: 21 year: 2012 ident: b20 article-title: A review of wearable sensors and systems with application in rehabilitation publication-title: J. Neuroeng. Rehab. – reference: L. Liao, D. Fox, H. Kautz, Location-based activity recognition, in: Advances in Neural Information Processing Systems, 2006, pp. 787-794. – reference: S. Mazilu, U. Blanke, M. Hardegger, G. Tröster, E. Gazit, J.M. Hausdorff, GaitAssist: A daily-life support and training system for parkinson’s disease patients with freezing of gait, in: Proceedings of the ACM Conference on Human Factors in Computing Systems, SIGCHI, Toronto, ON, Canada, 26 April–1 May 2014. – start-page: 1 year: 2017 end-page: 15 ident: b5 article-title: An ODT-based abstraction for mining closed sequential temporal patterns in IoT-cloud smart homes publication-title: Cluster Comput. – volume: 13 start-page: 67 year: 2014 end-page: 75 ident: b23 article-title: In-home activity recognition: Bayesian inference for hidden markov models publication-title: Perv. Comput. IEEE – volume: 30 start-page: 21 year: 2009 end-page: 27 ident: b22 article-title: Activity identification using body-mounted sensors: A review of classification techniques publication-title: Physiol. Meas. – volume: 10 start-page: 138 year: 2014 end-page: 154 ident: b37 article-title: Activity recognition on streaming sensor data publication-title: Perv. Mob. Comput. – volume: 52 start-page: 1126 year: 2015 ident: 10.1016/j.future.2017.10.031_b26 article-title: Verifying smart sensory systems on cloud computing frameworks publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2015.05.130 – ident: 10.1016/j.future.2017.10.031_b29 doi: 10.1145/1553374.1553453 – volume: 46 start-page: 62 issue: 7 year: 2013 ident: 10.1016/j.future.2017.10.031_b7 article-title: CASAS: A smart home in a box publication-title: Computer doi: 10.1109/MC.2012.328 – volume: 40 start-page: 1248 issue: 4 year: 2013 ident: 10.1016/j.future.2017.10.031_b28 article-title: Online activity recognition using evolving classifiers publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2012.08.066 – ident: 10.1016/j.future.2017.10.031_b14 doi: 10.5339/qfarc.2014.ITPP0366 – volume: 36 start-page: 100 issue: 2 year: 2011 ident: 10.1016/j.future.2017.10.031_b18 article-title: A model based on multi-features to enhance healthcare and medical document retrieval publication-title: Inform. Health Soc. Care doi: 10.3109/17538157.2010.506252 – ident: 10.1016/j.future.2017.10.031_b11 doi: 10.1002/9780470379424.ch37 – volume: 14 start-page: 645 year: 2010 ident: 10.1016/j.future.2017.10.031_b27 article-title: Preprocessing techniques for context recognition from accelerometer data publication-title: Perv. Mob. Comput. – start-page: 521 year: 2003 ident: 10.1016/j.future.2017.10.031_b10 article-title: MavHome: An agent-based smart home – start-page: 135 year: 2008 ident: 10.1016/j.future.2017.10.031_b8 – volume: 12 start-page: 5363 issue: 5 year: 2012 ident: 10.1016/j.future.2017.10.031_b33 article-title: A framework for supervising lifestyle diseases using long-term activity monitoring publication-title: Sensors doi: 10.3390/s120505363 – volume: 9 start-page: 413 issue: 5 year: 2017 ident: 10.1016/j.future.2017.10.031_b36 article-title: A verifiable framework for smart sensory systems publication-title: Int. J. Embed. Syst. doi: 10.1504/IJES.2017.086723 – volume: 30 start-page: 21 year: 2009 ident: 10.1016/j.future.2017.10.031_b22 article-title: Activity identification using body-mounted sensors: A review of classification techniques publication-title: Physiol. Meas. doi: 10.1088/0967-3334/30/4/R01 – volume: 9 start-page: 21 issue: 1 year: 2012 ident: 10.1016/j.future.2017.10.031_b20 article-title: A review of wearable sensors and systems with application in rehabilitation publication-title: J. Neuroeng. Rehab. doi: 10.1186/1743-0003-9-21 – volume: 13 start-page: 67 year: 2014 ident: 10.1016/j.future.2017.10.031_b23 article-title: In-home activity recognition: Bayesian inference for hidden markov models publication-title: Perv. Comput. IEEE doi: 10.1109/MPRV.2014.52 – start-page: 1 year: 2017 ident: 10.1016/j.future.2017.10.031_b35 article-title: Smart healthcare monitoring: A voice pathology detection paradigm for smart cities publication-title: Multimedia Syst. – volume: 38 start-page: 77 year: 2017 ident: 10.1016/j.future.2017.10.031_b32 article-title: Forecasting occurrences of activities publication-title: Perv. Mob. Comput. doi: 10.1016/j.pmcj.2016.09.010 – ident: 10.1016/j.future.2017.10.031_b1 – volume: 10 start-page: 155 year: 2014 ident: 10.1016/j.future.2017.10.031_b3 article-title: Dynamic sensor data segmentation for real-time knowledge-driven activity recognition publication-title: Perv. Mob. Comput. doi: 10.1016/j.pmcj.2012.11.004 – volume: 5 start-page: 3848 year: 2017 ident: 10.1016/j.future.2017.10.031_b4 article-title: An efficient activity recognition framework: Toward privacy-sensitive health data sensing publication-title: IEEE Access doi: 10.1109/ACCESS.2017.2685531 – ident: 10.1016/j.future.2017.10.031_b30 – volume: 43 start-page: 820 issue: 3 year: 2013 ident: 10.1016/j.future.2017.10.031_b24 article-title: Activity discovery and activity recognition: A new partnership publication-title: IEEE Trans. Cybernet. doi: 10.1109/TSMCB.2012.2216873 – volume: 7 start-page: 265 issue: 3 year: 2015 ident: 10.1016/j.future.2017.10.031_b13 article-title: Dynamic rough-based clustering for vehicular ad-hoc networks publication-title: Int. J. Inform. Decis. Sci. – year: 2017 ident: 10.1016/j.future.2017.10.031_b15 article-title: Cloud-assisted secure video transmission and sharing framework for smart cities publication-title: Future Gener. Comput. Syst. – volume: 9 start-page: 203 year: 2013 ident: 10.1016/j.future.2017.10.031_b19 article-title: The mobile fitness coach: Towards individualized skill assessment using personalized mobile devices publication-title: Perv. Mob. Comput. doi: 10.1016/j.pmcj.2012.06.002 – volume: 4 start-page: 158 year: 2004 ident: 10.1016/j.future.2017.10.031_b6 article-title: , March, Activity recognition in the home using simple and ubiquitous sensors publication-title: Pervasive – volume: 10 start-page: 138 year: 2014 ident: 10.1016/j.future.2017.10.031_b37 article-title: Activity recognition on streaming sensor data publication-title: Perv. Mob. Comput. doi: 10.1016/j.pmcj.2012.07.003 – volume: 39 start-page: 949 issue: 5 year: 2009 ident: 10.1016/j.future.2017.10.031_b16 article-title: Keeping the resident in the loop: adapting the smart home to the user publication-title: IEEE Trans. Syst. Man Cybernet. A doi: 10.1109/TSMCA.2009.2025137 – volume: 149 start-page: 1286 year: 2015 ident: 10.1016/j.future.2017.10.031_b25 article-title: Activity recognition in smart homes with self-verification of assignments publication-title: Neurocomputing doi: 10.1016/j.neucom.2014.08.069 – volume: 8 start-page: 140 issue: 2–3 year: 2016 ident: 10.1016/j.future.2017.10.031_b12 article-title: Dynamic event classification for intrusion and false alarm detection in vehicular ad hoc networks publication-title: Int. J. Inform. Commun. Technol. doi: 10.1504/IJICT.2016.074840 – volume: 68 start-page: 295 year: 2017 ident: 10.1016/j.future.2017.10.031_b9 article-title: Towards complex activity recognition using a Bayesian network-based probabilistic generative framework publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2017.02.028 – volume: 46 start-page: 1 year: 2014 ident: 10.1016/j.future.2017.10.031_b21 article-title: A tutorial on human activity recognition using body-worn inertial sensors publication-title: ACM Comput. Surv. doi: 10.1145/2499621 – volume: 103 start-page: 11 year: 2017 ident: 10.1016/j.future.2017.10.031_b34 article-title: Cyber–physical cloud-oriented multi-sensory smart home framework for elderly people: An energy efficiency perspective publication-title: J. Parallel Distrib. Comput. doi: 10.1016/j.jpdc.2016.10.005 – start-page: 1 year: 2017 ident: 10.1016/j.future.2017.10.031_b5 article-title: An ODT-based abstraction for mining closed sequential temporal patterns in IoT-cloud smart homes publication-title: Cluster Comput. – volume: 32 start-page: 319 issue: 2 year: 2003 ident: 10.1016/j.future.2017.10.031_b2 article-title: An algorithm based on singular spectrum analysis for change-point detection publication-title: Commun. Statist.-Simul. Comput. doi: 10.1081/SAC-120017494 – ident: 10.1016/j.future.2017.10.031_b17 doi: 10.1145/2556288.2557278 – ident: 10.1016/j.future.2017.10.031_b31 |
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| Title | A knowledge-driven approach for activity recognition in smart homes based on activity profiling |
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