Abstract
Within 2050, demographic changes, due to the significant increase of elderly, will represent one of the most important aspect for social assistance and healthcare institutions, particularly in European Union. Great attention is given to dementia diseases with over 35 million people worldwide who live in this condition, affected by cognitive impairment, frailty and social exclusion with considerable negative consequences for their independence. Preference will be given to intervention with high impact on the quality of life of the individual associated with a socio-economic burden, also for people who care for them. The main challenge comes from the social objective of assisting and keeping elderly people in their familiar home surrounding or to enable them to “aging in place”.
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References
Avgerinakis, K., Briassouli, A., Kompatsiaris, I.: Video monitoring for activities of daily living recognition. In: AAL Forum, Eidhoven, pp. 24–27 (September 2012)
Bharucha, A.J., London, A.J., Barnard, D., Wactlar, H., Dew, M.A., Reynolds, C.F.: Ethical considerations in the conduct of electronic surveillance research. The Journal of Law, Medicine & Ethics 34(3), 611–619 (2006)
Bobick, A., Davis, J.: The recognition of human movement using temporal templates. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(3), 257–267 (2001)
Bosch, A., Zisserman, A., Munoz, X.: Representing shape with a spatial pyramid kernel. In: Proceedings of the 6th ACM International Conference on Image and Video Retrieval, pp. 401–408. ACM (2007)
Dishman, E., Carrillo, M.C.: Perspective on everyday technologies for alzheimer’s care: Research findings, directions, and challenges. Alzheimer’s & Dementia 3(3), 227–234 (2007), http://www.sciencedirect.com/science/article/pii/S1552526007004827
EU: The benefits of early diagnosis and intervention (2011), http://ec.europa.eu/health-eu/my_health/elderly/index_en.htm
Jimison, J., McKanna, J., Ambert, K., Hagler, S., Hatt, W., Pavel, M.: Models of cognitive performance based on home monitoring data. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5234–5237 (August 2010)
Kenner, A.M.: Securing the elderly body: Dementia, surveillance, and the politics of “aging in place”. Surveillance & Society 5(3), 252–269 (2008)
Mégret, R., Dovgalecs, V., Wannous, H., Karaman, S., Benois-Pineau, J., El Khoury, E., Pinquier, J., Joly, P., André-Obrecht, R., Gaëstel, Y., Dartigues, J.: The immed project: wearable video monitoring of people with age dementia. In: Proc. of the Int. Conf. on Multimedia, MM 2010, pp. 1299–1302 (2010)
Romdhane, R., Mulin, E., Derreumeaux, A., Zouba, N., Piano, J., Lee, L., Leroi, I., Mallea, P., David, R., Thonnat, M., Bremond, F., Robert, P.: Automatic video monitoring system for assessment of alzheimers disease symptoms. The Journal of Nutrition, Health & Aging 16(3), 213–218 (2012)
Sacco, G., Joumier, V., Darmon, N., Dechamps, A., Derreumaux, A., Lee, J.H., Piano, J., Bordone, N., Konig, A., Teboul, B., et al.: Detection of activities of daily living impairment in alzheimer’s disease and mild cognitive impairment using information and communication technology. Clinical Interv. in Aging 7, 539 (2012)
Tani, J., Ito, M., Sugita, Y.: Self-organization of distributedly represented multiple behavior schemata in a mirror system: reviews of robot experiments using rnnpb. Neural Networks 17(8-9), 1273–1290 (2004)
Zanetti, O., Frisoni, G., Rozzini, L., Bianchetti, A., Trabucchi, M.: Validity of direct assessment of functional status as a tool for measuring alzheimer’s disease severity. Age and Ageing 27(5), 615–622 (1998)
Zhang, C., Tian, Y.: Rgb-d camera-based daily living activity recognition. Journal of Computer Vision and Image Processing 2(4) (2012)
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Iarlori, S., Ferracuti, F., Giantomassi, A., Longhi, S. (2014). RGB-D Video Monitoring System to Assess the Dementia Disease State Based on Recurrent Neural Networks with Parametric Bias Action Recognition and DAFS Index Evaluation. In: Miesenberger, K., Fels, D., Archambault, D., Peňáz, P., Zagler, W. (eds) Computers Helping People with Special Needs. ICCHP 2014. Lecture Notes in Computer Science, vol 8548. Springer, Cham. https://doi.org/10.1007/978-3-319-08599-9_25
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DOI: https://doi.org/10.1007/978-3-319-08599-9_25
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