## 6 Conclusions  
Here, we have investigated a noninvasive blood glucose estimation technique using only fingertip video acquired through a commercially available smartphone camera. Commercially available smartphones have been used to acquire video data which have been converted into PPG waveform. A computational model comprised of signal processing techniques used for cleaning the data and extracting the features and regression models trained with the features for the quantitative estimation of blood glucose has also been presented. Smartphone-based data acquisition system and disease diagnosis bring significant challenges with motion artifacts and noises. This work, therefore, pays special attention to the data acquisition techniques, and the development of algorithms to clean these artifacts. Numerous experiments were conducted with different acquisition techniques to identify a suitable method and duration of data acquisition. The models have been optimized by applying four regression algorithms along with different features. A PLS-based model could predict glucose with a SEP as low as 17.71 mg/dL, which is quite comparable to the overall lowest SEP achieved by PLS, 17.02 mg/dL. The experimental results confirm the usefulness of the first development steps towards a smartphone-based novel noninvasive estimation of blood glucose. Additional experiments are planned for the testing of robustness, platform independency, and, finally, implementing it in user side app for instant measurement. We believe that the proposed noninvasive technique has importance to the community as the system uses a much more natural and comfortable method of data acquisition compared to the traditional methods, can predict glucose value with high accuracy, and has the potential to acquire and deliver the glucose level information through only a smartphone that is now available to most people. The following are available online at [[https://www.mdpi.com/2076-3417/11/2/618/s1](https://www.mdpi.com/2076-3417/11/2/618/s1)]([https://www.mdpi.com/2076-3417/11/2/618/s1](https://www.mdpi.com/2076-3417/11/2/618/s1)), Figure S1: (Left) proposed model flow diagram and (Right) data acquisition procedure using smartphone camera; Figure S2: (Left) Corrected baseline PPG Signal of 5 different subjects from SP3 (a) Sub-1 (b) Sub-2 (c) Sub-3 (d) Sub-4 (e) Sub-5 (Right) complete procedure from raw PPG signal to feature extraction. (a) raw PPG; (b) corrected baseline ALS and Savitzky-Golay filter; (c) Gaussian smoothing applied, and (d) feature analysis (Peak-Detection); Figure S3: Feature analysis (Peak-Detection) (a) SPI signal peaks; (b) SP2 signal peaks, and (c) SP3 signal peaks; Figure S4: Extracting features (peak-detection) after being preprocessed with 1 st and 2 nd order derivatives; Figure S5: (Top Left) Intentional finger movement at 20, 30 and 40 seconds, (Top Right) PPG signal acquired while flash on, (Bottom Left) PPG signal acquired while the flash is off, (Bottom Right) PPG signal acquired and extracted from Red Channel; Figure S6: (a) A sample Raw PPG signal with high-frequency noises and (b) its filtered version through Gaussian Filter, baseline issues are still evident; Figure S7: (a) A sample Raw PPG signal with high baseline variations and (b) its ALS corrected version no baseline problems, high-frequency noises are still evident, Table S1: 2 nd Derivative Features Extracted from PPG signals acquired One Plus 6T, Table S2: K-Fold CV of different approaches for PCR and PLS, Table S3: K-Fold CV of different approaches for SVR and RFR. All authors listed contributed equally to the conception, design, analysis, drafting, and revision of this work. All authors have read and agreed to the published version of the manuscript. This research was funded by ICT Ministry & NSU CTRG: 38. The research protocol was approved by the NSU Institutional Review Board/Ethical Review Committee (IRB/ERC). Ethics Research Review Code: #2019/OR=NSU/IRB-No.0905. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.