References

Sun, 19 May 2013 14:21:26 -0700

Kaye, J., Mattek, N., Dodge, H., Buracchio, T., Austin, D., Hagler, S., Pavel, M., and Hates, T.: One Walk a Year to 1000 with a Year: Continuous In-home Unobtrusive Gait Assessment of Older Adults, In Gait Posture, 2012 February: 35(2): 197-202
Pavel M, Jimison HB, Wactlar HD, Hayes TL, Barkis W, Skapik J, Kaye J. The role of technology and engineering models in transforming healthcare. IEEE Rev Biomed Eng., 6:pp 156-77, 2013
Allègre, W., Burger, T., Berruet, P., and Yean-Yves, A.: A Non-intrusive Monitoring System for Ambient Assisted Living Service Delivery, In Donally, M. et al. (Eds.) ICOST 2012, LNCS 7251, pp 148-156, 2012
Skubic, M., Guevara R. D., and Rantz M.: Testing Classifiers for Embedded Health Assistant, In: Donally, M. et al. (Eds.) ICOST 2012, LNCS7251, pp 198-205, 2012
Mellone, S., Tacconi, C. L.Schwickert, J. Klenk, Becker, C., and Chiari, L.: Smartphone-based solutions for fall detection and prevention: the FARSEEING approach, In: Zeitschrift fur Gerontologie und Geriatre, Volume 45, Issue 8, pp 722-727, 2012
Bradford, D., Freyne, J. and Mohan Karunanithi, M.: Sensors on My Bed: The Ups and Downs of In-Home Monitoring, In J. Biswas et al. (Eds.): ICOST 2013, LNCS 7910, pp 10-18, 2013
Lin, Q., Zhang, D., Li, D., Ni, H., and Zhou, X.: Extracting Intra- and Inter-activity Association Patterns from Daily Routines of Elders, In J. Biswas et al. (Eds.): ICOST 2013, LNCS 7910, pp 36-44, 2013
Liu, C. T., Hsu, S. J., and Chan, C. T.: Activity Recognition and Activity Level Estimation for Context-Based Prompting System of Mild Cognitive Impairment Patients, In J. Biswas et al. (Eds.): ICOST 2013, LNCS 7910, pp 53-60, 2013
Albanese, A., Valtolina, E.: Measuring People Activity with Smart Homes, In J. Biswas et al. (Eds.): ICOST 2013, LNCS 7910, pp 198–204, 2013
Lee, D., and Helal, S.: From Activity Recognition to Situation Recognition, In In J. Biswas et al. (Eds.): ICOST 2013, LNCS 7910, pp 245–251, 2013
Kim, E. and Helal, S.: Fuzzy Logic Based Activity Life Cycle Tracking and Recognition. In J. Biswas et al. (Eds.): ICOST 2013, LNCS 7910, pp 252-258, 2013

Activity

Tue, 30 Apr 2013 21:49:05 -0700

Activity

Let's start from a simple principle: "life is motion", therefore, we should be able to correlate the motion of people as a measure of life or health. A daily pattern of motion displayed in hours, days and months shows differences between hours of rest (sleep) and activity (actions). In a smart home, life style and wellbeing may be assess by analyzing utility and sensor patterns. As we get older, we spend more time at home, and our home should be able to alert us about changes in our life/heath patterns, before they evolve into chronic problems. Our research effort evolves from automation, to activity monitoring of dwellers to collect data to facilitate the creation of mathematical models for the human body to correlate our activity to wellbeing.

Wellbeing | myHomeVillage.com

Research

Research

References

Activity

Activity