Abstract
Aging process is related to serious decline in physical and cognitive functions. Thus, early detection of these health changes is important to improve classical assessments that are mainly based on interviews, and are insufficient to early diagnose all possible health changes. Therefore, we propose a technological approach that analyzes elderly people behavior on a daily basis, employs unobtrusive monitoring technologies, and applies statistical techniques to identify continuous changes in monitored behavior. We detect significant long-term changes that are highly related to physical and cognitive problems. We also present a real validation through data collected from 3-year deployments in nursing-home rooms.
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References
Bureau, U.S.C.: United States Census Bureau: International Data Base (2018)
Ferri, C.P., et al.: Global prevalence of dementia: a Delphi consensus study. Lancet 366(9503), 2112–2117 (2005)
Cockrell, J.R., Folstein, M.F.: Mini-mental state examination. In: Principles and Practice of Geriatric Psychiatry, pp. 140–141 (2002)
Mathias, S., et al.: Balance in elderly patients: the “get-up and go” test. Arch. Phys. Med. Rehabil. 67(6), 387–389 (1986)
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)
Reisberg, B., et al.: Behavioral pathology in Alzheimer’s disease (BEHAVE-AD) rating scale. Int. Psychogeriatr. 8(S3), 301–308 (1997)
Wilson, D., et al.: In-home assessment of the activities of daily living of the elderly. In: Extended Abstracts of CHI 2005: Workshops-HCI Challenges in Health Assessment (2005)
Holsinger, T., et al.: Does this patient have dementia? JAMA 297(21), 2391–2404 (2007)
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)
Sloane, P.D., et al.: The public health impact of Alzheimer’s disease, 2000–2050: potential implication of treatment advances. Annu. Rev. Public Health 23, 213–231 (2002)
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). https://doi.org/10.1007/978-3-540-69916-3_20
Rantz, M.J., et al.: The future of long-term care for the chronically ill. Nurs. Adm. Q. 25(1), 51–58 (2000)
Demiris, G., et al.: Older adults’ attitudes towards and perceptions of ‘smart home’ technologies: a pilot study. Med. Inform. Internet Med. 29(2), 87–94 (2004)
Kaddachi, F., Aloulou, H., Abdulrazak, B., Fraisse, P., Mokhtari, M.: 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.) ICOST 2017. LNCS, vol. 10461, pp. 21–33. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66188-9_3
Kaddachi, F., et al.: Technological approach for behavior change detection toward better adaptation of services for elderly people. BIOSTEC 2017, 96 (2017)
Aloulou, H., et al.: An adaptable and flexible framework for assistive living of cognitively impaired people. IEEE J. Biomed. Health Inform. 18(1), 353–360 (2014)
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)
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)
Lally, P., et al.: How are habits formed: modelling habit formation in the real world. Eur. J. Soc. Psychol. 40(6), 998–1009 (2010)
Sprint, G., et al.: Unsupervised detection and analysis of changes in everyday physical activity data. J. Biomed. Inform. 63, 54–65 (2016)
Dodge, H.H., et al.: Web-enabled conversational interactions as a method to improve cognitive functions: results of a 6-week randomized controlled trial. Alzheimer’s Dement.: Transl. Res. Clin. Interv. 1(1), 1–12 (2015)
Petersen, J., et al.: Unobtrusive phone monitoring as a novel measure of cognitive function. Alzheimer’s Dement. J. Alzheimer’s Assoc. 10(4), P366–P367 (2014)
Reddy, V.R., et al.: PerDMCS: Weighted fusion of PPG signal features for robust and efficient diabetes mellitus classification. In: HEALTHINF, pp. 553–560 (2017)
Sashima, A., et al.: A telecare system on smart watches that communicate with wireless bio sensors. In: HEALTHINF, pp. 429–434 (2017)
Dalakleidi, K., et al.: A modified all-and-one classification algorithm combined with the bag-of-features model to address the food recognition task. In: HEALTHINF, pp. 284–290 (2017)
Magill, E., Blum, J.M.: Personalised ambient monitoring: supporting mental health at home. In: Advances in Home Care Technologies: Results of the Match Project, pp. 67–85 (2012)
Perera, C., et al.: Big data privacy in the Internet of Things era. IT Prof. 17(3), 32–39 (2015)
Basseville, M.: Statistical methods for change detection. In: Control Systems, Robotics and AutomatioN-Volume XVI: Fault Analysis and Control, p. 130 (2009)
Taylor, W.A.: Change-point analysis: a powerful new tool for detecting changes (2000). http://www.variation.com/cpa/tech/changepoint.html
Mesnil, B., Petitgas, P.: Detection of changes in time-series of indicators using CUSUM control charts. Aquat. Living Resour. 22(2), 187–192 (2009)
Bland, J.M., Altman, D.G.: Comparing methods of measurement: why plotting difference against standard method is misleading. Lancet 346(8982), 1085–1087 (1995)
Killick, R., et al.: Optimal detection of changepoints with a linear computational cost. J. Am. Stat. Assoc. 107(500), 1590–1598 (2012)
City4Age: Elderly-friendly city services for active and healthy aging (2018). 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. (2018). Technological Approach for Early and Unobtrusive Detection of Possible Health Changes Toward More Effective Treatment. In: Mokhtari, M., Abdulrazak, B., Aloulou, H. (eds) Smart Homes and Health Telematics, Designing a Better Future: Urban Assisted Living. ICOST 2018. Lecture Notes in Computer Science(), vol 10898. Springer, Cham. https://doi.org/10.1007/978-3-319-94523-1_5
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DOI: https://doi.org/10.1007/978-3-319-94523-1_5
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