Asma Benmansour
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Abstract
Human activity recognition in ambient intelligent environments like homes, offices, and classrooms has been the center of a lot of research for many years now. The aim is to recognize the sequence of actions by a specific person using sensor readings. Most of the research has been devoted to activity recognition of single occupants in the environment. However, living environments are usually inhabited by more than one person and possibly with pets. Hence, human activity recognition in the context of multioccupancy is more general, but also more challenging. The difficulty comes from mainly two aspects: resident identification, known as data association, and diversity of human activities. The present survey article provides an overview of existing approaches and current practices for activity recognition in multioccupant smart homes. It presents the latest developments and highlights the open issues in this field.
- Giovanni Acampora, Diane J. Cook, Parisa Rashidi, and Athanasios V. Vasilakos. 2013. A survey on ambient intelligence in healthcare. Proc. IEEE 101, 12 (Dec. 2013), 2470--2494. DOI:http://dx.doi.org/10.1109/JPROC.2013.2262913Google Scholar
Cross Ref
- Hande Alerndar, Halil Ertan, Ozlern D. Incel, and Cem Ersoy. 2013. ARAS human activity datasets in multiple homes with multiple residents. In Proceedings of the 7th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth’13). 232--235. DOI:http://dx.doi.org/10.4108/icst.pervasivehealth.2013.252120 Google ScholarDigital Library
- Anon. 2014. Centers for Disease Control and Prevention. Retrieved October 13, 2014 from http://www.cdc.gov/.Google Scholar
- Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag, New York. Google ScholarDigital Library
- Ann Bookman, M. Harrington, L. Pass, and E. Reisner. 2007. Family Caregiver Handbook: Finding Elder Care Resources in Massachusetts. (2007). Retrieved October 15, 2014 from http://web.mit. edu/workplacecenter/hndbk/.Google Scholar
- Abdelhamid Bouchachia and Charlie Vanaret. 2014. GT2FC: An online growing interval type-2 self-learning fuzzy classifier. IEEE Trans. Fuzzy Syst. 22, 4 (Aug. 2014), 999--1018. DOI:http://dx.doi.org/10.1109/TFUZZ.2013.2279554Google ScholarCross Ref
- Marie Chan, Eric Campo, Daniel Estève, and Jean-Yves Fourniols. 2009. Smart homes—Current features and future perspectives. Maturitas 64, 2 (Oct. 2009), 90--97. DOI:http://dx.doi.org/10.1016/j.maturitas.2009.07.014Google ScholarCross Ref
- Rong Chen and Yu Tong. 2014. A two-stage method for solving multi-resident activity recognition in smart environments. Entropy 16, 4 (April 2014), 2184--2203. DOI:http://dx.doi.org/10.3390/e16042184Google ScholarCross Ref
- Yi-ting Chiang, J. Y. J. Hsu, Ching-Hu Lu, and Li-Chen Fu. 2010. Interaction models for multiple-resident activity recognition in a smart home. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’10). 3753--3758. DOI:http://dx.doi.org/10.1109/IROS.2010.5650340Google Scholar
- Diane J. Cook. 2009. Multi-agent smart environments. J. Ambient Intell. Smart Env. 1, 1 (Jan. 2009), 51--55. DOI:http://dx.doi.org/10.3233/AIS-2009-0007 Google ScholarDigital Library
- Diane J. Cook, Aaron Crandall, Geetika Singla, and Brian Thomas. 2010. Detection of social interaction in smart spaces. Cybern. Syst. 41, 2 (Feb. 2010), 90--104. DOI:http://dx.doi.org/10.1080/01969720903584183 Google ScholarDigital Library
- Erin Y. Cornwell and Linda J. Waite. 2009. Social disconnectedness, perceived isolation, and health among older adults. J. Health Soc. Behav. 50, 1 (March 2009), 31--48. DOI:http://dx.doi.org/10.1177/002214650905000103Google ScholarCross Ref
- Aaron S. Crandall and Diane J. Cook. 2009. Coping with multiple residents in a smart environment. J. Ambient Intell. Smart Env. 1, 4 (Dec. 2009), 323--334. DOI:http://dx.doi.org/10.3233/AIS-2009-0041 Google ScholarDigital Library
- Aaron S. Crandall and Diane J. Cook. 2008a. Resident and caregiver: Handling multiple people in a smart care facility. In Proceedings of the AAAI Fall Symposium—AI in Eldercare: New solutions to Old Problems (2008). 39--47. DOI:http://dx.doi.org/10.1.1.329.4737Google Scholar
- Aaron S. Crandall and Diane J. Cook. 2008b. Attributing events to individuals in multi-inhabitant environments. In Proceedings of the IET 4th International Conference on Intelligent Environments (August 2008). 1--8. DOI:http://dx.doi.org/10.1049/cp:20081164Google Scholar
- Aaron S. Crandall and Diane J. Cook. 2010. Using a hidden Markov model for resident identification. In Proceedings of th 6th International Conference on Intelligent Environments (IE’10). 74--79. DOI:http://dx.doi.org/10.1109/IE.2010.21 Google ScholarDigital Library
- Scott Davidoff, Min Kyung Lee, John Zimmerman, and Anind Dey. 2006. Socially-aware requirements for a smart home. In Proceedings of the International Symposium on Intelligent Environments. 41--44. DOI:http://dx.doi.org/10.1.1.104.5771Google Scholar
- Youtian Du, Feng Chen, and Wenli Xu. 2007. Human interaction representation and recognition through motion decomposition. IEEE Signal Process. Lett. 14, 12 (Dec. 2007), 952--955. DOI:http://dx. doi.org/10.1109/LSP.2007.908035Google ScholarCross Ref
- Youtian Du, Feng Chen, Wenli Xu, and Yongbin Li. 2006. Recognizing interaction activities using dynamic Bayesian network. In Proceedings of the 18th International Conference on Pattern Recognition (ICPR’06). 618--621. DOI:http://dx.doi.org/10.1109/ICPR.2006.977 Google ScholarDigital Library
- Nathan Eagle. 2008. Behavioral inference across cultures: Using telephones as a cultural lens. IEEE Intell. Syst. 23, 4 (July 2008), 62--64. DOI:http://dx.doi.org/10.1109/MIS.2008.58 Google ScholarDigital Library
- Laura Fratiglioni, Hui-Xin Wang, Kjerstin Ericsson, Margaret Maytan, and Bengt Winblad. 2000. Influence of social network on occurrence of dementia: A community-based longitudinal study. Lancet 355, 9212 (April 2000), 1315--1319. DOI:http://dx.doi.org/10.1016/S0140-6736(00)02113-9Google ScholarCross Ref
- Jane Gross. 2007. A Grass-Roots Effort to Grow Old at Home. N. Y. Times (August 2007).Google Scholar
- Tao Gu, Zhanqing Wu, Xianping Tao, H. K. Pung, and Jian Lu. 2009a. epSICAR: An emerging patterns based approach to sequential, interleaved and concurrent activity recognition. In Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom’09). 1--9. DOI:http://dx.doi.org/10.1109/PERCOM.2009.4912776 Google ScholarDigital Library
- Tao Gu, Zhanqing Wu, Liang Wang, Xianping Tao, and Jian Lu. 2009b. Mining emerging patterns for recognizing activities of multiple users in pervasive computing. In Proceedings of the 6th Annual International Conference on Mobile and Ubiquitous Systems: Networking Services (MobiQuitous’09). 1--10. DOI:http://dx.doi.org/10.4108/ICST.MOBIQUITOUS2009.6818Google ScholarCross Ref
- Chien-Chang Hsu and Liang-Zong Wang. 2008. A smart home resource management system for multiple inhabitants by agent conceding negotiation. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC’08). 607--612. DOI:http://dx.doi.org/10.1109/ICSMC. 2008.48 11344Google Scholar
- Kuo-Chung Hsu, Yi-Ting Chiang, Gu-Yang Lin, Ching-Hu Lu, Jane Yung-Jen Hsu, and Li-Chen Fu. 2010. Strategies for inference mechanism of conditional random fields for multiple-resident activity recognition in a smart home. In Trends in Applied Intelligent Systems, Lecture Notes in Computer Science. Springer, Berlin, 417--426. Google ScholarDigital Library
- Derek Hao Hu, Xian-Xing Zhang, Jie Yin, Vincent Wenchen Zheng, and Qiang Yang. 2009. Abnormal activity recognition based on HDP-HMM models. In Proceedings of the 21st International Joint Conference on Artifical Intelligence (IJCAI’09). Morgan Kaufmann Publishers Inc., 1715--1720. Google ScholarDigital Library
- Shehroz S. Khan, Michelle E. Karg, Jesse Hoey, and Dana Kulic. 2012. Towards the detection of unusual temporal events during activities using HMMs. In Proceedings of the Conference on Ubiquitous Computing (UbiComp’12). ACM, New York, 1075--1084. DOI:http://dx.doi.org/10.1145/2370216.2370444 Google ScholarDigital Library
- Eunju Kim, S. Helal, and D. Cook. 2010. Human activity recognition and pattern discovery. IEEE Pervasive Comput. 9, 1 (Jan. 2010), 48--53. DOI:http://dx.doi.org/10.1109/MPRV.2010.7 Google ScholarDigital Library
- Jinyan Li, Guimei Liu, and Limsoon Wong. 2007. Mining statistically important equivalence classes and delta-discriminative emerging patterns. In Proceedings of the 13th International Conference on Knowledge Discovery and Data Mining (SIGKDD’07). ACM, New York, 430--439. DOI:http://dx.doi.org/10.1145/1281192.1281240 Google ScholarDigital Library
- Wenhui Liao and Qiang Ji. 2009. Learning Bayesian network parameters under incomplete data with domain knowledge. Pattern Recognit. 42 (2009), 3046--3056. Google ScholarDigital Library
- Zhi-Hao Lin and Li-Chen Fu. 2007. Multi-user preference model and service provision in a smart home environment. In Proceedings of the IEEE International Conference on Automation Science and Engineering (CASE’07). 759--764. DOI:http://dx.doi.org/10.1109/COASE.2007.4341737Google ScholarCross Ref
- Ching-Hu Lu, Yung-Ching Lin, and Li-Chen Fu. 2008. Hide and not easy to seek: A hybrid weaving strategy for context-aware service provision in a smart home. In IEEE Asia-Pacific Services Computing Conference (APSCC’08). 595--600. DOI:http://dx.doi.org/10.1109/APSCC.2008.77 Google ScholarDigital Library
- Iain McCowan, Daniel Gatica-Perez, Samy Bengio, Guillaume Lathoud, Mark Barnard, and Dong Zhang. 2005. Automatic analysis of multimodal group actions in meetings. IEEE Trans. Pattern Anal. Mach. Intell. 27, 3 (March 2005), 305--317. DOI:http://dx.doi.org/10.1109/TPAMI.2005.49 Google ScholarDigital Library
- Ahmed Nait Aicha, Gwenn Englebienne, and Ben Kröse. 2013. How lonely is your grandma?: Detecting the visits to assisted living elderly from wireless sensor network data. In Proceedings of the Conference on Pervasive and Ubiquitous Computing Adjunct Publication (UbiComp’13). ACM, New York, 1285--1294. DOI:http://dx.doi.org/10.1145/2494091.2497283 Google ScholarDigital Library
- Pradeep Natarajan and Ramakant Nevatia. 2007. Coupled hidden semi Markov models for activity recognition. In Proceedings of the Workshop on Motion and Video Computing (WMVC’07). IEEE, 10. DOI:http://dx.doi.org/10.1109/WMVC.2007.12 Google ScholarDigital Library
- Nam Nguyen, Svetha Venkatesh, and Hung Bui. 2006. Recognising behaviours of multiple people with hierarchical probabilistic model and statistical data association. In Proceedings of the 17th British Machine Vision Conference (BMVC’06). The British Machine Vision Association and Society for Pattern Recognition, 1239--1248.Google ScholarCross Ref
- Daniel O. Olguin, Benjamin N. Waber, Taemie Kim, Akshay Mohan, Koji Ara, and Alex Pentland. 2009. Sensible organizations: Technology and methodology for automatically measuring organizational behavior. IEEE Trans. Syst. Man Cybern. Part B Cybern. 39, 1 (Feb. 2009), 43--55. DOI:http://dx.doi.org/10.1109/TSMCB.2008.2006638 Google ScholarDigital Library
- Markus Prossegger and Abdelhamid Bouchachia. 2014. Multi-resident activity recognition using incremental decision trees. In Adaptive and Intelligent Systems, Abdelhamid Bouchachia (Ed.). Lecture Notes in Computer Science. Springer International Publishing, 182--191.Google Scholar
- Iulian Pruteanu-Malinici and Lawrence Carin. 2008. Infinite hidden Markov models for unusual-event detection in video. IEEE Trans. Image Process. 17, 5 (May 2008), 811--822. DOI:http://dx.doi. org/10.1109/TIP.2008.919359 Google ScholarDigital Library
- Barry Reisberg, Sanford I. Finkel, John Overall, and Hellmut Erzigkeit. 2001. The Alzheimer's disease activities of daily living international scale (ADL-IS). Int. Psychogeriatr. IPA 13, 2 (June 2001), 163--181.Google Scholar
- Daniele Riboni, Linda Pareschi, Laura Radaelli, and Claudio Bettini. 2011. Is ontology-based activity recognition really effective? In Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops’11). 427--431. DOI:http://dx.doi.org/10.1109/PERCOMW.2011.5766927Google ScholarCross Ref
- Susanne Smith Roley et al. 2008. Occupational therapy practice framework: Domain and practice 2nd ed. Am. J. Occup. Ther. Off. Publ. Am. Occup. Ther. Assoc. 62, 6 (Dec. 2008), 625--683.Google Scholar
- Fariba Sadri. 2011. Ambient intelligence: A survey. ACM Comput. Surv. 43, 4 (Oct. 2011), 36:1--36:66. DOI:http://dx.doi.org/10.1145/1978802.1978815 Google ScholarDigital Library
- Sumit Sanghai, Pedro Domingos, and Daniel Weld. 2005. Relational dynamic Bayesian networks. J. Artif. Intell. Res. 24, 2005 (2005), 759--797.Google ScholarCross Ref
- Jehad A. M. Sarkar, Young-Koo Lee, and Sungyoung Lee. 2010. ARHMAM: An activity recognition system based on hidden Markov minded activity model. In Proceedings of the 4th International Conference on Uniquitous Information Management and Communication (ICUIMC’10). ACM, New York, 71:1--71:9. DOI:http://dx.doi.org/10.1145/2108616.2108702 Google ScholarDigital Library
- Geetika Singla, Diane J. Cook, and Maureen Schmitter-Edgecombe. 2010. Recognizing independent and joint activities among multiple residents in smart environments. J. Ambient Intell. Humaniz. Comput. 1, 1 (March 2010), 57--63. DOI:http://dx.doi.org/10.1007/s12652-009-0007-1Google ScholarCross Ref
- Eliot R. Smith and Diane M. Mackie. 1999. Social Psychology. Routledge, London.Google Scholar
- Charles Sutton and Andrew McCallum. 2006. An introduction to conditional random fields for relational learning. In Introduction to Statistical Relational Learning. MIT Press, 93--128.Google Scholar
- Charles Sutton, Andrew McCallum, and Khashayar Rohanimanesh. 2007. Dynamic conditional random fields: Factorized probabilistic models for labeling and segmenting sequence data. J. Mach. Learn. Res. 8 (May 2007), 693--723. Google ScholarDigital Library
- Tanzeem Choudhury and Alex Pentland. 2004. Characterizing social networks using the sociometer. In Proceedings of NAACOS 2004.Google Scholar
- Tim L. M. van Kasteren. 2011. Activity Recognition for Health Monitoring Elderly Using Temporal Probabilistic Models. Ph.D. dissertation. University of Amsterdam.Google Scholar
- Tim L. M. van Kasteren, Gwenn Englebienne, and Ben J. A. Kröse. 2011. Hierarchical activity recognition using automatically clustered actions. In Proceedings of the 2nd International Conference on Ambient Intelligence (AmI’11). 82--91. Google ScholarDigital Library
- Tim L. M. van Kasteren, Gwenn Englebienne, and Ben J. A. Kröse. 2010. Human activity recognition from wireless sensor network data: Benchmark and software. In Activity Recognition in Pervasive Intelligent Environments, Liming Chen, Chris D. Nugent, Jit Biswas, and Jesse Hoey (Eds.), Atlantis Ambient and Pervasive Intelligence. Atlantis Press, 165--186.Google Scholar
- Tim L. M. van Kasteren, Athanasios Noulas, Gwenn Englebienne, and Ben Kröse. 2008. Accurate activity recognition in a home setting. In Proceedings of the 10th International Conference on Ubiquitous Computing (UbiComp’08). ACM, 1--9. DOI:http://dx.doi.org/10.1145/1409635.1409637 Google ScholarDigital Library
- Kristof Van Laerhoven, Nicky Kern, Hans W. Gellersen, and Bernt Schiele. 2003. Towards a wearable inertial sensor network. In Proceedings of the IEE Eurowearable Conference (2003). 125--130. DOI:http://dx.doi.org/10.1049/ic:20030158Google ScholarCross Ref
- Liang Wang, Tao Gu, Xianping Tao, Hanhua Chen, and Jian Lu. 2011. Recognizing multi-user activities using wearable sensors in a smart home. Pervasive Mob. Comput. 7, 3 (June 2011), 287--298. DOI:http://dx.doi.org/10.1016/j.pmcj.2010.11.008 Google ScholarDigital Library
- Liang Wang, Tao Gu, Xianping Tao, and Jian Lu. 2009. Sensor-based human activity recognition in a multi-user scenario. In Ambient Intelligence, Manfred Tscheligi et al. (Eds.). Lecture Notes in Computer Science. Springer, Berlin, 78--87. Google ScholarDigital Library
- Daniel H. Wilson and Chris Atkeson. 2005. Simultaneous tracking and activity recognition (STAR) using many anonymous, binary sensors. In Pervasive Computing, Hans-W. Gellersen, Roy Want, and Albrecht Schmidt (Eds.). Lecture Notes in Computer Science. Springer, Berlin, 62--79. Google ScholarDigital Library
Index Terms
- Multioccupant Activity Recognition in Pervasive Smart Home Environments
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