  Over the last decade, smartphones have changed radically to support us with
mHealth technology, cloud computing, and machine learning algorithm. Having its
multifaceted facilities, we present a novel smartphone-based noninvasive
hemoglobin (Hb) level prediction model by analyzing hue, saturation and value
(HSV) of a fingertip video. Here, we collect 60 videos of 60 subjects from two
different locations: Blood Center of Wisconsin, USA and AmaderGram, Bangladesh.
We extract red, green, and blue (RGB) pixel intensities of selected images of
those videos captured by the smartphone camera with flash on. Then we convert
RGB values of selected video frames of a fingertip video into HSV color space
and we generate histogram values of these HSV pixel intensities. We average
these histogram values of a fingertip video and consider as an observation
against the gold standard Hb concentration. We generate two input feature
matrices based on observation of two different data sets. Partial Least Squares
(PLS) algorithm is applied on the input feature matrix. We observe R2=0.95 in
both data sets through our research. We analyze our data using Python OpenCV,
Matlab, and R statistics tool.
