Technological Approach for Behavior Change Detection toward Better Adaptation of Services for Elderly People

Firas Kaddachi, Hamdi Aloulou, Bessam Abdulrazak, Joaquim Bellmunt, Romain Endelin, Mounir Mokhtari, Philippe Fraisse

Abstract

Aging process is associated with behavior change and continuous decline in physical and cognitive abilities. Therefore, early detection of behavior change is major enabler for providing adapted services to elderly people. Today, different psychogeriatric methods target behavior change detection. However, these methods require presence of caregivers and manual analysis. In this paper, we present our technological approach for early behavior change detection. It consists in monitoring and analyzing individual activities using pervasive sensors, as well as detecting possible changes in early stages of their evolution. We also present a first validation of the approach with real data from nursing home deployment.

References

  1. Allin, S., Bharucha, A., Zimmerman, J., Wilson, D., Robinson, M., Stevens, S., Wactlar, H., and Atkeson, C. (2003). Toward the automatic assessment of behavioral distrubances of dementia.
  2. Aloulou, H. (2013). Framework for ambient assistive living: handling dynamism and uncertainty in real time semantic services provisioning. PhD thesis, Evry, Institut national des télécommunications.
  3. Aloulou, H., Mokhtari, M., Tiberghien, T., Biswas, J., Phua, C., Lin, J. H. K., and Yap, P. (2013). Deployment of assistive living technology in a nursing home environment: methods and lessons learned. BMC medical informatics and decision making, 13(1):1.
  4. Andersson, J. (2014). Locating multiple change-points using a combination of methods.
  5. Avvenuti, M., Baker, C., Light, J., Tulpan, D., and Vecchio, A. (2010). Non-intrusive patient monitoring of alzheimers disease subjects using wireless sensor networks. In 2009 World Congress on Privacy, Security, Trust and the Management of e-Business.
  6. Barberger-Gateau, P., Commenges, D., Gagnon, M., Letenneur, L., Sauvel, C., and Dartigues, J.-F. (1992). Instrumental activities of daily living as a screening tool for cognitive impairment and dementia in elderly community dwellers. Journal of the American Geriatrics Society, 40(11):1129-1134.
  7. Basseville, M., Nikiforov, I. V., et al. (1993). Detection of abrupt changes: theory and application, volume 104. Prentice Hall Englewood Cliffs.
  8. Bland, J. M. and Altman, D. G. (1995). Comparing methods of measurement: why plotting difference against standard method is misleading. The lancet, 346(8982):1085-1087.
  9. Cao, L. (2010). In-depth behavior understanding and use: the behavior informatics approach. Information Sciences, 180(17):3067-3085.
  10. Cho, H. (2015). Change-point detection in panel data via double cusum statistic.
  11. City4Age (2016). Elderly-friendly for active and healthy aging. http://www.city4ageproject.eu/.
  12. Cockrell, J. R. and Folstein, M. F. (2002). Mini-mental state examination. Principles and practice of geriatric psychiatry, pages 140-141.
  13. Cummings, J. L., Mega, M., Gray, K., RosenbergThompson, S., Carusi, D. A., and Gornbein, J. (1994). The neuropsychiatric inventory comprehensive assessment of psychopathology in dementia. Neurology, 44(12):2308-2308.
  14. Hayes, T. L., Abendroth, F., Adami, A., Pavel, M., Zitzelberger, T. A., and Kaye, J. A. (2008). Unobtrusive assessment of activity patterns associated with mild cognitive impairment. Alzheimer's & Dementia, 4(6):395-405.
  15. Hayes, T. L., Larimer, N., Adami, A., and Kaye, J. A. (2009). Medication adherence in healthy elders: small cognitive changes make a big difference. Journal of aging and health.
  16. Kaye, J., Mattek, N., Dodge, H. H., Campbell, I., Hayes, T., Austin, D., Hatt, W., Wild, K., Jimison, H., and Pavel, M. (2014). Unobtrusive measurement of daily computer use to detect mild cognitive impairment. Alzheimer's & Dementia, 10(1):10-17.
  17. Kibria, G. (2016). Cumulative sum and exponentially weighted moving average control charts. preprint, available as http://www2.fiu.edu/ kibriag /Stat5666/Handout/Chapter99.pdf.
  18. Lafont, S., Barberger-Gateau, P., Sourgen, C., and Dartigues, J. (1999). Relation entre performances cognitives globales et dépendance évaluée par la grille aggir. Revue d'épidémiologie et de santé publique, 47(1):7-17.
  19. Lavikainen, H. M., Lintonen, T., and Kosunen, E. (2009). Sexual behavior and drinking style among teenagers: a population-based study in finland.Health promotion international, 24(2):108-119.
  20. Ledolter, J. and Kardon, R. (2013). Detecting the progression of eye disease: Cusum charts for assessing the visual field and retinal nerve fiber layer thickness. Translational vision science & technology, 2(6):2-2.
  21. Liu, S., Yamada, M., Collier, N., and Sugiyama, M. (2013). Change-point detection in time-series data by relative density-ratio estimation. Neural Networks, 43:72-83.
  22. Magill, E. and Blum, J. M. (2012). Personalised ambient monitoring: supporting mental health at home. Advances in home care technologies: Results of the Match project, pages 67-85.
  23. Mathias, S., Nayak, U., and Isaacs, B. (1986). Balance in elderly patients: the” get-up and go” test. Archives of physical medicine and rehabilitation, 67(6):387-389.
  24. Mesnil, B. and Petitgas, P. (2009). Detection of changes in time-series of indicators using cusum control charts. Aquatic Living Resources, 22(2):187-192.
  25. Miller, J. W., Naimi, T. S., Brewer, R. D., and Jones, S. E. (2007). Binge drinking and associated health risk behaviors among high school students. Pediatrics, 119(1):76-85.
  26. Moskvina, V. and Zhigljavsky, A. (2003). An algorithm based on singular spectrum analysis for change-point detection. Communications in Statistics-Simulation and Computation, 32(2):319-352.
  27. Neuropsy, R. (2016). Four Tests. http://fmc31200.free.fr/MGliens/Neurologie/quatre tests.pdf. [Online; accessed September-2016].
  28. Ormrod, J. E. (2013). Educational Psychology: Pearson New International Edition: Developing Learners. Pearson Higher Ed.
  29. Page, E. (1954). Continuous inspection schemes. Biometrika, 41(1/2):100-115.
  30. Parmelee, P. A. and Katz, I. R. (1990). Geriatric depression scale. Journal of the American Geriatrics Society.
  31. Perner, L. (2008). Consumer behaviour and marketing strategy.
  32. Petersen, J., Austin, D., Yeargers, J., and Kaye, J. (2014). Unobtrusive phone monitoring as a novel measure of cognitive function. Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 10(4):P366- P367.
  33. Prochaska, J. O. and DiClemente, C. C. (2005). The transtheoretical approach. Handbook of psychotherapy integration, 2:147-171.
  34. Reisberg, B., Auer, S. R., and Monteiro, I. M. (1997). Behavioral pathology in alzheimer's disease (behavead) rating scale. International Psychogeriatrics, 8(S3):301-308.
  35. Rosenstock, I. M. (1974). Historical origins of the health belief model. Health Education & Behavior, 2(4):328-335.
  36. Schwarzer, R. (2008). Modeling health behavior change: How to predict and modify the adoption and maintenance of health behaviors. Applied Psychology, 57(1):1-29.
  37. Szwacka-Mokrzycka, J. (2015). Trends in consumer behaviour changes. overview of concepts. Acta Scientiarum Polonorum. Oeconomia, 14(3).
  38. Takeuchi, J.-i. and Yamanishi, K. (2006). A unifying framework for detecting outliers and change points from time series. IEEE transactions on Knowledge and Data Engineering, 18(4):482-492.
  39. Tardieu, Ó., Mahmoudi, R., Novella, J.-L., Oubaya, N., Blanchard, F., Jolly, D., and Drame, M. (2016). External validation of the short emergency geriatric assessment (sega) instrument on the safes cohort. Geriatrie et psychologie neuropsychiatrie du vieillissement, 14(1):49-55.
  40. Taylor, W. A. (2000). Change-point analysis: a powerful new tool for detecting changes. preprint, available as http://www. variation. com/cpa/tech/changepoint. html.
  41. Vellas, B., Guigoz, Y., Garry, P. J., Nourhashemi, F., Bennahum, D., Lauque, S., and Albarede, J.-L. (1999). The mini nutritional assessment (mna) and its use in grading the nutritional state of elderly patients. Nutrition, 15(2):116-122.
  42. Wilson, E. O. (2000). Sociobiology. Harvard University Press.
Download


Paper Citation


in Harvard Style

Kaddachi F., Aloulou H., Abdulrazak B., Bellmunt J., Endelin R., Mokhtari M. and Fraisse P. (2017). Technological Approach for Behavior Change Detection toward Better Adaptation of Services for Elderly People . In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2017) ISBN 978-989-758-213-4, pages 96-105. DOI: 10.5220/0006145100960105


in Bibtex Style

@conference{healthinf17,
author={Firas Kaddachi and Hamdi Aloulou and Bessam Abdulrazak and Joaquim Bellmunt and Romain Endelin and Mounir Mokhtari and Philippe Fraisse},
title={Technological Approach for Behavior Change Detection toward Better Adaptation of Services for Elderly People},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2017)},
year={2017},
pages={96-105},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006145100960105},
isbn={978-989-758-213-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2017)
TI - Technological Approach for Behavior Change Detection toward Better Adaptation of Services for Elderly People
SN - 978-989-758-213-4
AU - Kaddachi F.
AU - Aloulou H.
AU - Abdulrazak B.
AU - Bellmunt J.
AU - Endelin R.
AU - Mokhtari M.
AU - Fraisse P.
PY - 2017
SP - 96
EP - 105
DO - 10.5220/0006145100960105