We extend the 2nd Gen Discrete Wavelet Transform (DWT) of Swelden to the Next Generations (NG) Digital
Wavelet Transform (DWT) preserving the statistical salient features. The lossless NG DWT accomplishes the data
compression of "wellness baseline profiles (WBP)" of aging population at homes. For medical monitoring system at
home fronts we translate the military experience to dual usage of veterans & civilian alike with the following three
requirements: (i) Data Compression: The necessary down sampling reduces the immense amount of data of individual
WBP from hours to days and to weeks for primary caretakers in terms of moments, e.g. mean value, variance, etc.,
without the artifacts caused by FFT arbitrary windowing. (ii) Lossless: our new NG_DWT must preserve the original
data sets. (iii) Phase Transition: NG_DWT must capture the critical phase transition of the wellness toward the sickness
with simultaneous display of local statistical moments. According to the Nyquist sampling theory, assuming a band-limited
wellness physiology, we must sample the WBP at least twice per day since it is changing diurnally and
seasonally. Since NG_DWT, like the 2nd Gen, is lossless, we can reconstruct the original time series for the physicians'
second looks. This technique of NG_DWT can also help stock market day-traders monitoring the volatility of multiple
portfolios without artificial horizon artifacts.