Abstract
Behavior change indicates continuous decline in physical, cognitive and emotional status of elderly people. Early detection of behavior change is major enabler for service providers to adapt their services and improve the quality of life of elderly people. Nowadays, existing psychogeriatric scales and questionnaires are insufficient to observe all possible changes at a daily basis. Therefore, we propose a technological approach for behavior change detection, that employs unobtrusive ambient technologies to follow up elderly people over long periods. In fact, we study significant behavior change indicators (e.g., sleep impairments, visits and go out) and investigate statistical techniques that distinguish transient and continuous changes in monitored behavior. Furthermore, we present a preliminary validation of our approach through results based on correlations between our technological observations and medical observations of two-year nursing home deployment.
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References
Holsinger, T., et al.: Does this patient have dementia? JAMA 297(21), 2391–2404 (2007)
Cao, L.: In-depth behavior understanding and use: the behavior informatics approach. Inf. Sci. 180(17), 3067–3085 (2010)
Mathias, S., et al.: Balance in elderly patients: the “get-up and go” test. Arch. Phys. Med. Rehabil. 67(6), 387–389 (1986)
Cockrell, J.R., Folstein, M.F.: Mini-mental state examination. Principles and Practice of Geriatric Psychiatry, pp. 140–141 (2002)
Vellas, B., et al.: The mini nutritional assessment (mna) and its use in grading the nutritional state of elderly patients. Nutrition 15(2), 116–122 (1999)
Lafont, S., et al.: Relation entre performances cognitives globales et dépendance évaluée par la grille aggir. Revue d’épidémiologie et de santé publique 47(1), 7–17 (1999)
Barberger-Gateau, P., et al.: Instrumental activities of daily living as a screening tool for cognitive impairment and dementia in elderly community dwellers. J. Am. Geriatr. Soc. 40(11), 1129–1134 (1992)
Boockvar, K.S., Lachs, M.S.: Predictive value of nonspecific symptoms for acute illness in nursing home residents. J. Am. Geriatr. Soc. 51(8), 1111–1115 (2003)
Ridley, S.: The recognition and early management of critical illness. Ann. R. Coll. Surg. Engl. 87(5), 315 (2005)
Kaddachi, F., et al.: Technological approach for behavior change detection toward better adaptation of services for elderly people. BIOSTEC 2017, 96 (2017)
Reisberg, B., et al.: Behavioral pathology in alzheimer’s disease (behave-ad) rating scale. Int. Psychogeriatr. 8(S3), 301–308 (1997)
Bourke, A.K., et al.: Fall detection algorithms for real-world falls harvested from lumbar sensors in the elderly population: a machine learning approach. In: 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), pp. 3712–3715. IEEE (2016)
Aloulou, H., et al.: Deployment of assistive living technology in a nursing home environment: methods and lessons learned. BMC Med. Inform. Decis. Mak. 13(1), 42 (2013)
Singtel: Monitor and watch you elderly family members’ daily activities with singtel’s smart home solutions (2017). https://www.singtelshop.com/smarthome-yuhua
SeniorHome: Eva est une plateforme logicielle qui fonctione sur la base de capteurs repartis dans le domicile (2017). http://seniorhome.fr/
Lee, M.L., Dey, A.K.: Sensor-based observations of daily living for aging in place. Pers. Ubiquit. Comput. 19(1), 27–43 (2015)
Rantz, M., Skubic, M., Miller, S., Krampe, J.: Using technology to enhance aging in place. In: Helal, S., Mitra, S., Wong, J., Chang, C.K., Mokhtari, M. (eds.) ICOST 2008. LNCS, vol. 5120, pp. 169–176. Springer, Heidelberg (2008). doi:10.1007/978-3-540-69916-3_20
Hayes, T.L., et al.: Sleep habits in mild cognitive impairment. Alzheimer Dis. Assoc. Disord. 28(2), 145 (2014)
Kaye, J., et al.: Unobtrusive measurement of daily computer use to detect mild cognitive impairment. Alzheimer’s Dement. 10(1), 10–17 (2014)
Hayes, T.L., et al.: Medication adherence in healthy elders: small cognitive changes make a big difference. J. Aging Health (2009)
Hodges, M.R., Kirsch, N.L., Newman, M.W., Pollack, M.E.: Automatic assessment of cognitive impairment through electronic observation of object usage. In: Floréen, P., Krüger, A., Spasojevic, M. (eds.) Pervasive 2010. LNCS, vol. 6030, pp. 192–209. Springer, Heidelberg (2010). doi:10.1007/978-3-642-12654-3_12
Avvenuti, M., et al.: Non-intrusive patient monitoring of alzheimer’s disease subjects using wireless sensor networks. In: World Congress on Privacy, Security, Trust and the Management of e-Business. CONGRESS 2009, pp. 161–165. IEEE (2009)
Tolstikov, A., et al.: Eating activity primitives detection-a step towards ADL recognition. In: 10th International Conference on e-health Networking, Applications and Services. HealthCom 2008, pp. 35–41. IEEE (2008)
Allin, S., et al.: Toward the automatic assessment of behavioral distrubances of dementia (2003)
Magill, E., Blum, J.M.: Personalised ambient monitoring: supporting mental health at home. Advances in home care technologies: Results of the Match project, pp. 67–85 (2012)
Demiris, G., et al.: Older adults’ attitudes towards and perceptions of smart home technologies: a pilot study. Med. Inf. Internet Med. 29(2), 87–94 (2004)
Cummings, J.L., et al.: The neuropsychiatric inventory comprehensive assessment of psychopathology in dementia. Neurology 44(12), 2308–2308 (1994)
Parmelee, P.A., Katz, I.R.: Geriatric depression scale. J. Am. Geriatr. Soc. 38, 1379–1379 (1990)
Page, E.: Continuous inspection schemes. Biometrika 41(1/2), 100–115 (1954)
Mesnil, B., Petitgas, P.: Detection of changes in time-series of indicators using cusum control charts. Aquat. Living Resour. 22(2), 187–192 (2009)
Taylor, W.A.: Change-point analysis: a powerful new tool for detecting changes. preprint http://www.variation.com/cpa/tech/changepoint.html (2000)
Bland, J.M., Altman, D.G.: Comparing methods of measurement: why plotting difference against standard method is misleading. Lancet 346(8982), 1085–1087 (1995)
Krishef, C.H.: Fundamental Approaches to Sigle Subject Design and Analysis. Krieger, Malabar (1991)
City4Age: Elderly-friendly city services for active and healthy aging (2016). http://www.city4ageproject.eu/
Acknowledgement
We give our special thanks to Saint Vincent de Paul nursing home in Occagnes, France. Our deployment in this nursing home is also supported by VHP inter@ctive project and the Quality Of Life chair.
Our work is part of the European project City4Age that received funding from the Horizon 2020 research and innovation program under grant agreement number 689731.
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Kaddachi, F., Aloulou, H., Abdulrazak, B., Fraisse, P., Mokhtari, M. (2017). Unobtrusive Technological Approach for Continuous Behavior Change Detection Toward Better Adaptation of Clinical Assessments and Interventions for Elderly People. In: Mokhtari, M., Abdulrazak, B., Aloulou, H. (eds) Enhanced Quality of Life and Smart Living. ICOST 2017. Lecture Notes in Computer Science(), vol 10461. Springer, Cham. https://doi.org/10.1007/978-3-319-66188-9_3
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DOI: https://doi.org/10.1007/978-3-319-66188-9_3
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