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

Estimation of Blood Glucose Level of Type-2 Diabetes Patients using Smartphone Video