On multi-resident activity recognition in ambient smart-homes

  • Son N. TranEmail author
  • Dung Nguyen
  • Tung-Son Ngo
  • Xuan-Son Vu
  • Long Hoang
  • Qing Zhang
  • Mohan Karunanithi


Increasing attention to the research on activity monitoring in smart homes has motivated the employment of ambient intelligence to reduce the deployment cost and solve the privacy issue. Several approaches have been proposed for multi-resident activity recognition, however, there still lacks a comprehensive benchmark for future research and practical selection of models. In this paper, we study different methods for multi-resident activity recognition and evaluate them on the same sets of data. In particular, we explore the effectiveness and efficiency of temporal learning algorithms using sequential data and non-temporal learning algorithms using temporally-manipulated features. In the experiments we compare and analyse the results of the studied methods using datasets from three smart homes.


Multiresident activity Pervasive computing Smart homes 



  1. Alemdar H, Ertan H, Incel OD, Ersoy C (2013) Aras human activity datasets in multiple homes with multiple residents. In: PervasiveHealth’13. ICST, Brussels, Belgium, pp 232–235.
  2. Benmansour A, Bouchachia A, Feham M (2015) Multioccupant activity recognition in pervasive smart home environments. ACM Comput Surv 48(3):34:1–34:36CrossRefGoogle Scholar
  3. Brand M, Oliver N, Pentland A (1997) Coupled hidden Markov models for complex action recognition. In: Proceedings of the 1997 conference on computer vision and pattern recognition, CVPR’97. IEEE Computer Society, Washington, DC, USA, p 994Google Scholar
  4. Chen R, Tong Y (2014) A two-stage method for solving multi-resident activity recognition in smart environments. Entropy 16(4):2184CrossRefGoogle Scholar
  5. Chiang YT, Hsu KC, Lu CH, Fu LC, Hsu JYJ (2010) Interaction models for multiple-resident activity recognition in a smart home. In: IEEE/RSJ international conference on IROS, pp 3753–3758.
  6. Cho K, van Merrienboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder–decoder for statistical machine translation. In: Proceedings of the 2014 conference on empirical methods in natural language processing, pp 1724–1734Google Scholar
  7. Cook DJ (2012) Learning setting-generalized activity models for smart spaces. IEEE Intell Syst 27:32–38. CrossRefGoogle Scholar
  8. Cook DJ, Crandall A, Singla G, Thomas B (2010) Detection of social interaction in smart spaces. Cybern Syst 41(2):90–104. CrossRefzbMATHGoogle Scholar
  9. Crandall AS, Cook DJ (2008) Resident and caregiver: handling multiple people in a smart care facility. In: Proceedings of AAAI fall symposium—AI in eldercare—new solutions to old problems, AAAI technical report, vol FS-08-02. AAAI.
  10. Das SK, Cook DJ (2004) Health monitoring in an agent-based smart home. In: Proceedings of the international conference on smart homes and health telematics. ICOST, IOS Press, pp 3–14Google Scholar
  11. Ghahramani Z, Jordan MI (1997) Factorial hidden Markov models. Mach Learn 29(2–3):245–273. CrossRefzbMATHGoogle Scholar
  12. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780. CrossRefGoogle Scholar
  13. Hsu KC, Chiang YT, Lin GY, Lu CH, Hsu JYJ, Fu LC (2010) Strategies for inference mechanism of conditional random fields for multiple-resident activity recognition in a smart home. Springer, Berlin, pp 417–426. CrossRefGoogle Scholar
  14. Plötz T, Hammerla NY, Olivier P (2011) Feature learning for activity recognition in ubiquitous computing. In: Proceedings of the twenty-second international joint conference on artificial intelligence—volume two, IJCAI’11. AAAI Press, pp 1729–1734Google Scholar
  15. Poppe R (2010) A survey on vision-based human action recognition. Image Vis Comput 28(6):976–990CrossRefGoogle Scholar
  16. Prossegger M, Bouchachia A (2014) Multi-resident activity recognition using incremental decision trees. In: Adaptive and intelligent systems—third international conference, ICAIS 2014, Bournemouth, UK, September 8–10, 2014. Proceedings, pp 182–191Google Scholar
  17. Rabiner LR (1990) Readings in speech recognition. A tutorial on hidden markov models and selected applications in speech recognition. Morgan Kaufmann Publishers Inc., San Francisco, pp 267–296Google Scholar
  18. Saini R, Kumar P, PratimRoy P, Dogra DP (2018) A novel framework of continuous human-activity recognition using kinect. Neurocomputing 311:99–111CrossRefGoogle Scholar
  19. Singla G, Cook DJ, Schmitter-Edgecombe M (2010) Recognizing independent and joint activities among multiple residents in smart environments. J Ambient Intell Humaniz Comput 1(1):57–63. CrossRefGoogle Scholar
  20. Son NT, Qing Z, Mohan K (2017) Improving multi-resident activity recognition for smarter home. In: IJCAI 2017 WS on AI for IoT, IJCAI’17Google Scholar
  21. Sutton C, McCallum A, Rohanimanesh K (2007) Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data. J Mach Learn Res 8:693–723zbMATHGoogle Scholar
  22. Tunca C, Alemdar H, Ertan H, Incel OD, Ersoy C (2014) Multimodal wireless sensor network-based ambient assisted living in real homes with multiple residents. Sensors 14:9692–9719CrossRefGoogle Scholar
  23. van Kasteren T, Noulas A, Englebienne G, Kröse B (2008) Accurate activity recognition in a home setting. In: Proceedings of the 10th international conference on ubiquitous computing, UbiComp’08. ACM, New York, NY, USA, pp 1–9.
  24. Wang L, Gu T, Tao X, Chen H, Lu J (2011) Recognizing multi-user activities using wearable sensors in a smart home. Pervasive Mob Comput 7(3):287–298. CrossRefGoogle Scholar
  25. Wilson DH, Atkeson C (2005) Simultaneous tracking and activity recognition (star) using many anonymous, binary sensors. In: Proceedings of the third international conference on pervasive computing, PERVASIVE’05. Springer, Berlin, pp 62–79. Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.University of TasmaniaLauncestonAustralia
  2. 2.Duy Tan UniversityDa NangVietnam
  3. 3.Department of Computer ScienceFPT UniversityHanoiVietnam
  4. 4.Department of Computing ScienceUmeå UniversityUmeåSweden
  5. 5.Posts and Telecommunications Institute of TechnologyHo Chi Minh CityVietnam
  6. 6.The Australian E-Health Research CentreCSIROBrisbaneAustralia

Personalised recommendations