Issue No. 01 - January-March (2007 vol. 6)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MPRV.2007.9
Tamara L. Hayes , Oregon Health and Science University
Misha Pavel , Oregon Health and Science University
Nicole Larimer , Oregon Health and Science University
Ishan A. Tsay , Oregon Health and Science University
John Nutt , Oregon Health and Science University
Andre Gustavo Adami , University of Caxias do Sul
As treatments for life-threatening illnesses improve, life expectancy increases along with the proportion of healthcare dollars supporting chronic care. Combine this with the growing number of aging baby boomers (who are most at risk for these chronic diseases), and we see a greater demand for healthcare alternatives. Most people approach healthcare by reacting to triggered problems: when we get sick, we typically wait until the symptoms start interfering with our daily life, and then we visit a clinic. At this point, particularly for populations at risk such as the elderly and the chronically ill, the treatment can often be riskier and much more expansive than if the problem had been dealt with earlier. Conversely, a proactive approach to healthcare would in many cases result in more effective and much less expensive treatments, by predicting or detecting conditions earlier. This article demonstrates an approach that estimates normalized walking times in a multiperson home to show how we can use pervasive computing with neurobehavioral measurement to provide a practical and economically feasible way to make frequent assessments. This article is part of a special issue on Healthcare.
algorithms, machine learning, healthcare, pervasive computing, sensors, wearable computing
J. Nutt, T. L. Hayes, N. Larimer, I. A. Tsay, M. Pavel and A. G. Adami, "Distributed Healthcare: Simultaneous Assessment of Multiple Individuals," in IEEE Pervasive Computing, vol. 6, no. , pp. 36-43, 2007.