Advertisement

Towards Habit Recognition in Smart Homes for People with Dementia

  • Gibson ChimamiwaEmail author
  • Marjan Alirezaie
  • Hadi Banaee
  • Uwe Köckemann
  • Amy Loutfi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11912)

Abstract

The demand for smart home technologies that enable ageing in place is rising. Through activity recognition, users’ activities can be monitored. However, for dementia patients, activity recognition alone cannot address the challenges associated with changes in the user’s habits along the disease’s stage transitions. Extending activity recognition to habit recognition enables the capturing of patients’ habits and changes in habits in order to detect anomalies. This paper aims to introduce relevant features for habit recognition solutions, extracted from data, in order to enrich the representation of the user’s habits. This solution is personalisable to meet the specific needs of the patients and generalizable for use in different scenarios. In this way caregivers are better informed on the expected changes of the patient’s habits, which can help to mitigate further deterioration through early treatment and intervention.

Keywords

Habit recognition Dementia Smart homes 

Notes

Acknowledgments

This work has been supported by both the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 754285, and the distributed research environment E-care@home funded by the Swedish Knowledge Foundation (KKS), 2015–2019.

References

  1. 1.
    Alirezaie, M., Hammar, K., Blomqvist, E.: Smartenv as a network of ontology patterns. Semant. Web 9(6), 903–918 (2018)CrossRefGoogle Scholar
  2. 2.
    Alirezaie, M., et al.: An ontology-based context-aware system for smart homes: E-care@ home. Sensors 17(7), 1586 (2017)CrossRefGoogle Scholar
  3. 3.
    Amiribesheli, M., Bouchachia, H.: A tailored smart home for dementia care. J. Ambient Intell. Hum. Comput. 9(6), 1755–1782 (2018)CrossRefGoogle Scholar
  4. 4.
    Arifoglu, D., Bouchachia, A.: Detection of abnormal behaviour for dementia sufferers using convolutional neural networks. A.I. Med. 94, 88–95 (2019)Google Scholar
  5. 5.
    Enshaeifar, S., et al.: Machine learning methods for detecting urinary tract infection and analysing daily living activities in people with dementia. PLoS ONE 14(1), e0209909 (2019)CrossRefGoogle Scholar
  6. 6.
    Lin, Q., Zhao, W., Wang, W.: Detecting dementia-related wandering locomotion of elders by leveraging active infrared sensors. J. Comput. Commun. 6(05), 94 (2018)CrossRefGoogle Scholar
  7. 7.
    Prince, M., Wimo, A., Guerchet, M., Ali, G., Wu, Y., Prina, M.: World Alzheimer report 2015-the global impact of dementia, an analysis of prevalence, incidence, cost and trends. Alzheimer’s Dis. Int. 17, 2016 (2015)Google Scholar
  8. 8.
    Ranasinghe, S., Al Machot, F., Mayr, H.C.: A review on applications of activity recognition systems with regard to performance and evaluation. Int. J. Distrib. Sens. Netw. 12(8) (2016).  https://doi.org/10.1177/1550147716665520CrossRefGoogle Scholar
  9. 9.
    Reisberg, B., Ferris, S.H., de Leon, M.J., Crook, T.: The global deterioration scale for assessment of primary degenerative dementia. Psychiatry J. 139(9), 1136–1139 (1982)Google Scholar
  10. 10.
    Stavropoulos, T.G., Meditskos, G., Andreadis, S., Avgerinakis, K., Adam, K., Kompatsiaris, I.: Semantic event fusion of computer vision and ambient sensor data for activity recognition to support dementia care. J. Ambient. Intell. Hum. Comput. 1–16 (2016).  https://doi.org/10.1007/s12652-016-0437-5
  11. 11.
    Su, C.F., Fu, L.C., Chien, Y.W., Li, T.Y.: Activity recognition system for dementia in smart homes based on wearable sensor data. In: 2018 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 463–469. IEEE (2018)Google Scholar
  12. 12.
    Thompson, M.: Occupations, habits, and routines: perspectives from persons with diabetes. Scand. J. Occup. Ther. 21(2), 153–160 (2014)CrossRefGoogle Scholar
  13. 13.
    Tiberghien, T., Mokhtari, M., Aloulou, H., Biswas, J.: Semantic reasoning in context-aware assistive environments to support ageing with dementia. In: Cudré-Mauroux, P., et al. (eds.) ISWC 2012. LNCS, vol. 7650, pp. 212–227. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-35173-0_14CrossRefGoogle Scholar
  14. 14.
    Woods, R.T., et al.: Dementia: issues in early recognition and intervention in primary care. J. R. Soc. Med. 96, 320–324 (2003)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Gibson Chimamiwa
    • 1
    Email author
  • Marjan Alirezaie
    • 1
  • Hadi Banaee
    • 1
  • Uwe Köckemann
    • 1
  • Amy Loutfi
    • 1
  1. 1.Centre for Applied Autonomous Sensor Systems (AASS)ÖrebroSweden

Personalised recommendations