  This work proposes a smartphone video-based approach for the estimation of
blood glucose in a non-invasive way. Videos using smartphone camera are
collected from the tip of the subjects finger and the frames are subsequently
converted into Photoplethysmography (PPG) waveform. Gaussian filter along with
Asymmetric Least Square methods have been applied on the PPG signals to remove
the high-frequency noise, optical and motion interferences. Different signal
features such as Systolic and Diastolic Peaks, the time difference between
consecutive peaks (DelT), First Derivative peaks, and Second derivative peaks
etc have been extracted from the processed signal. Finally, Principal Component
Regression (PCR) has been applied for the prediction of glucose level from the
extracted features. The proposed model, while applied to an unbiased dataset,
could predict the glucose level with a Standard Error of Prediction (SEP) of
around 18.31 mg/dL.
