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9 April 2007 Wellness engineering for better quality of life of aging baby boomer
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Current health care system serving 78M aging baby-boomers is no longer sustainable, as the cost about 1/5 GDP will reach 1/4 GDT when all is retired in decades, unless the system is changed. We design a high-tech safe net to enhance the timeliness of early correct treatment execution (otherwise, causing 1/4 mortality associated with an escalating legal fee waste). We follow the common sense that "a stitch in time saves nine," and adopt the military surveillance know-how in designing early warning health management system, comprising of smart sensor pairs for point-care surveillance. However, the grand plan of affordable smart sensors hardware for households requires an ODM & OEM teaming to conduct parallel designing and sequential marketing strategy. The military software strategy combating a treacherous adversary enemy match well with point cares surveillance overcoming real world microorganism variability. Moreover, such smart military software provides self-reference change detection, not by traditional cohort ensemble average, but by individual own higher order statistics (HOS) independent component analysis (ICA), which take the advantage of known initial condition for each individual and desirable over-sampling daily dynamics. The triggering of warning follows the military algorithms comprising of Receiver Operation Characteristics (ROC) and Automatic Target Recognition (ATR). To further reduce the unwanted false negative rate, a benchmarked is made against the traditional cohort-ensemble baseline average & the upper & lower bounds of variance as adopted by the gatekeepers - Medical Doctors (MD) and Nurses.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Harold Szu "Wellness engineering for better quality of life of aging baby boomer", Proc. SPIE 6576, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks V, 65760O (9 April 2007);


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