Skip to main content
SpringerLink
Log in

Human home daily living activities recognition based on a LabVIEW implemented hidden Markov model

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The recognition of human daily living activities within a house represents an efficient tool to model its power consumption and is also a good indicator for monitoring the health status of the inhabitants. The problematic of activities recognition in smart homes has been extensively addressed in several studies. In this paper, we present an original interactive tool developed under LabVIEW environment with a graphical user interface allowing the modeling of the daily living activities, based on a machine learning Hidden Markov Model. After an overview of the advantage for the consideration of this model in current human activities, we examine how the associated scientific problematic can find an interest and a solution by the integration of machine learning tools. Thus, the application based on a Hidden Markov model approach, is presented and evaluated using two sets of experimental data from literature. Comparing with results obtained by other daily living activities recognition methods, we point out the very satisfactory recognition performance of the Hidden Markov Model and the likelihood of our development associated to a user-friendly graphical interface. This work opens the way to applications dedicated to the supervision of human daily living activities and / or to the management of the electrical consumption within a smart home equipped with non-intrusive sensors.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Alaa M, Zaidan AA, Zaidan BB, Talal M, Kiah MLM (2017) A review of smart home applications based on internet of things. J Netw Comput Appl 97:48–65. https://doi.org/10.1016/j.jnca.2017.08.017

    Article  Google Scholar 

  2. Aldrich FK (2006) Smart homes: past, present and future. Insid Smart Home:17–39. https://doi.org/10.1007/1-85233-854-7_2

  3. Bitter R, Mohiuddin T, Nawrocki M (2007) LabView advanced programming techniques. CRC Press

  4. Brank J, Mladenić D, Grobelnik M, Liu H, Mladenić D, Flach PA, Garriga GC, Toivonen H, Toivonen H (2011) F-measure. In: Sammut C, Webb GI (eds) Encyclopedia of machine learning. Springer, Boston, pp 416–416. https://doi.org/10.1007/978-0-387-30164-8_315

    Chapter  Google Scholar 

  5. Brecha RJ, Mitchell A, Hallinan K, Kissock K (2011) Prioritizing investment in residential energy efficiency and renewable energy — A case study for the U. S. Midwest. Energy Policy 39(5):2982–2992. https://doi.org/10.1016/j.enpol.2011.03.011

    Article  Google Scholar 

  6. Chernbumroong S, Cang S, Atkins A, Hongnian Y (2013) Elderly activities recognition and classification for applications in assisted living. Expert Syst Appl 40(5):1662–1674. https://doi.org/10.1016/j.eswa.2012.09.004

    Article  Google Scholar 

  7. Cornuéjols A, Miclet L (2002) Apprentissage Artificiel Concepts et Algorithmes. EYROLLES

  8. Ferrández-Pastor FJ, Mora-Mora H, Sánchez-Romero JL, Nieto-Hidalgo M, García-Chamizo JM (2017) Interpreting human activity from electrical consumption data using reconfigurable hardware and hidden Markov models. J Ambient Intell Humaniz Comput 8(4):469–483. https://doi.org/10.1007/s12652-016-0431-y

    Article  Google Scholar 

  9. Fleury A, Vacher M, Noury N (2010) SVM-based multimodal classification of activities of daily living in health smart homes: sensors, algorithms, and first experimental results. IEEE Trans Inf Technol Biomed 14:274–283. https://doi.org/10.1109/TITB.2009.2037317

    Article  Google Scholar 

  10. Geller H, Schaeffer R, Szklo A, Tolmasquim M (2004) Policies for advancing energy efficiency and renewable energy use in Brazil. Energy Policy 32(12):1437–1450. https://doi.org/10.1016/S0301-4215(03)00122-8

    Article  Google Scholar 

  11. Grewal JK, Krzywinski M, Altman N (2019) Markov Models — Hidden Markov Models. Nat Methods 16(9):795–796. https://doi.org/10.1038/s41592-019-0532-6

    Article  Google Scholar 

  12. Hellgren M (2015) Energy Use as a Consequence of Everyday Life. https://doi.org/10.3384/diss.diva-122253

  13. Hu Q, Li F (2013) Hardware Design of Smart Home Energy Management System with dynamic Price response. IEEE Trans Smart Grid 4(4):1878–1887. https://doi.org/10.1109/TSG.2013.2258181

    Article  Google Scholar 

  14. Japkowicz N (2006) “Why question machine learning evaluation methods? (An illustrative review of the shortcomings of current methods).” AAAI-2006 Workshop on Evaluation Methods for Machine Learning, 6

  15. Kakas AC, Cohn D, Dasgupta S, Barto AG, Carpenter GA, Grossberg S, Webb GI et al (2011) Accuracy. In: Sammut C, Webb GI (eds) Encyclopedia of machine learning. Springer, Boston, pp 9–10. https://doi.org/10.1007/978-0-387-30164-8_3

    Chapter  Google Scholar 

  16. Kang J, Kim M, Park JH (2016) A reliable TTP-based infrastructure with low sensor resource consumption for the smart home multi-platform. Sensors (Switzerland) 16(7):1–15. https://doi.org/10.3390/s16071036

    Article  Google Scholar 

  17. Karaman S, Benois-Pineau J, Dovgalecs V, Mégret R, Pinquier J, André-Obrecht R, Gaëstel Y, Dartigues JF (2014) Hierarchical hidden Markov model in detecting activities of daily living in wearable videos for studies of dementia. Multimed Tools Appl 69(3):743–771. https://doi.org/10.1007/s11042-012-1117-x

    Article  Google Scholar 

  18. Kim MJ, Myoung Won O, Cho ME, Lee H, Kim JT (2013) A critical review of user studies on healthy smart homes. Indoor Built Environ 22(1):260–270. https://doi.org/10.1177/1420326X12469733

    Article  Google Scholar 

  19. Li Y, Peng X, Zhou G, Zhao H (2020) SmartJump: a continuous jump detection framework on smartphones. IEEE Internet Comput 24(2):18–26. https://doi.org/10.1109/MIC.2020.2969610

    Article  Google Scholar 

  20. Liouane Z, Lemlouma T, Roose P, Weis F, Messaoud H (2016) A Markovian-based approach for daily living activities recognition. In: SENSORNETS 2016 - Proceedings of the 5th International Confererence on Sensor Networks, 214–19. https://doi.org/10.5220/0005809502140219.

  21. Ordóñez J, de Toledo FP, Sanchis A (2013) Activity recognition using hybrid generative/discriminative models on home environments using binary sensors. Sensors (Switzerland) 13(5):5460–5477. https://doi.org/10.3390/s130505460

    Article  Google Scholar 

  22. Rabiner LR, Juang BH (1986) An introduction to hidden Markov models. IEEE ASSP Mag 3(1):4–16. https://doi.org/10.1109/MASSP.1986.1165342

    Article  Google Scholar 

  23. Ricquebourg V, Menga D, Durand D, Marhic B, Delahoche L, Logé C (2006) The smart home concept: our immediate future. 2006 1st IEEE International Conference on E-Learning in Industrial Electronics, ICELIE, no. May 2014: 23–28. https://doi.org/10.1109/ICELIE.2006.347206

  24. Sammut C, Webb GI, eds. (2010) Recall. In: Encyclopedia of Machine Learning, 829. Boston: Springer. https://doi.org/10.1007/978-0-387-30164-8_702

  25. Shultz TR, Fahlman SE, Craw S, Andritsos P, Tsaparas P, Silva R, Drummond C, Ling CX, Sheng VS, Drummond C, Lanzi PL, Gama J, Wiegand RP, Sen P, Namata G, Bilgic M, Getoor L, He J, Jain S, Stephan F, Jain S, Stephan F, Sammut C, Harries M, Sammut C, Ting KM, Pfahringer B, Case J, Jain S, Wagstaff KL, Nijssen S, Wirth A, Ling CX, Sheng VS, Zhang X, Sammut C, Cancedda N, Renders J-M, Michelucci P, Oblinger D, Keogh E, Mueen A (2011) Confusion matrix. In: Sammut C, Webb GI (eds) Encyclopedia of machine learning. Springer, Boston, pp 209–209. https://doi.org/10.1007/978-0-387-30164-8_157

    Chapter  Google Scholar 

  26. Tahir SF, Fahad LG, Kifayat K (2019) Key feature identification for recognition of activities performed by a smart-home resident. J Ambient Intell Humaniz Comput 11:2105–2115. https://doi.org/10.1007/s12652-019-01236-y

    Article  Google Scholar 

  27. Tapia EM, Intille SS, Larson K (2004) Activity recognition in the home using simple and ubiquitous sensors. Lect Notes Comput Sci 3001:158–175. https://doi.org/10.1007/978-3-540-24646-6_10

    Article  Google Scholar 

  28. Ting KM (2010) Precision. In: Sammut C, Webb GI (eds) Encyclopedia of Machine Learning, vol 780. Springer, Boston. https://doi.org/10.1007/978-0-387-30164-8_651

    Chapter  Google Scholar 

  29. van Kasteren T, Noulas A, Englebienne G, Kröse v (2008) “Accurate activity recognition in a home setting.” UbiComp 2008 - proceedings of the 10th international conference on ubiquitous computing, 2008. https://doi.org/10.1145/1409635.1409637.

  30. van Kasteren TML, Englebienne G, Kröse B (2010) Activity recognition using semi-Markov models on real world smart home data sets. J Ambient Intell Smart Environ 2:311–325

    Article  Google Scholar 

  31. Van Kasteren TLM, Englebienne G, Kröse BJA (2011) Human activity recognition from wireless sensor network data: benchmark and software. Act Recognit Pervasive Intell Environ 4:165–186. https://doi.org/10.2991/978-94-91216-05-3_8

    Article  Google Scholar 

  32. Weber S, Puddu S, Pacheco D (2017) Move it! How an electric contest motivates households to shift their load profile. Energy Econ 68:255–270. https://doi.org/10.1016/j.eneco.2017.10.010

    Article  Google Scholar 

  33. Zhou B, Li W, Chan KW, Cao Y, Kuang Y, Liu X, Wang X (2016) Smart home energy management systems: concept, configurations, and scheduling strategies. Renew Sust Energ Rev 61:30–40. https://doi.org/10.1016/j.rser.2016.03.047

    Article  Google Scholar 

  34. Zipperer A, Aloise-Young PA, Suryanarayanan S, Roche R, Earle L, Christensen D, Bauleo P, Zimmerle D (2013) Electric energy Management in the Smart Home: perspectives on enabling technologies and consumer behavior. Proc IEEE 101(11):2397–2408. https://doi.org/10.1109/JPROC.2013.2270172

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Université de BLIDA 1, Faculté de Technologie, Département des Energies Renouvelables, 09000, Blida, Algeria

    Abderrezak Guenounou & Moustafa Bouzaki

  2. Laboratoire Matériaux Optiques, Photonique et Systèmes, LMOPS, EA 4423, Université de Lorraine, F-57070, Metz, France

    Abderrezak Guenounou, Michel Aillerie, Moustafa Bouzaki & Jean-Pierre Charles

  3. Laboratoire Matériaux Optiques, Photonique et Systèmes, LMOPS, CentraleSupélec, Université Paris-Saclay, F-57070, Metz, France

    Abderrezak Guenounou, Michel Aillerie, Moustafa Bouzaki & Jean-Pierre Charles

  4. Unité de Développement des Equipements Solaires (UDES)/ EPST Centre de Développement des Energies Renouvelables (CDER), 42415, Bou Ismail, Tipaza, Algeria

    Achour Mahrane & Sabri Boulouma

Authors
  1. Abderrezak Guenounou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michel Aillerie
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Achour Mahrane
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Moustafa Bouzaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sabri Boulouma
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jean-Pierre Charles
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abderrezak Guenounou.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guenounou, A., Aillerie, M., Mahrane, A. et al. Human home daily living activities recognition based on a LabVIEW implemented hidden Markov model. Multimed Tools Appl 80, 24419–24435 (2021). https://doi.org/10.1007/s11042-021-10814-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-021-10814-2

Keywords

Search

Navigation