| Autor: |
Hina, Aminah, Saadeh, Wala |
| Zdroj: |
IEEE Sensors Journal; 7/15/2022, Vol. 22 Issue 14, p14185-14195, 11p |
| Abstrakt: |
Recent trends in research and the market show high demand for frequent monitoring of glucose to estimate the blood sugar level non-invasively which can replace the conventional finger-prick glucometer for daily use. This paper presents a high precision near-infrared Photoplethysmography (PPG) based noninvasive glucose monitoring System on Chip (SoC). The proposed system implements a fully differential Analog Frontend (AFE) with nonlinear medium Gaussian support-vector-regression (NMG-SVR) for glucose estimation. The AFE design incorporates chopping which enables the reduction of the integrated input-referred current noise to 9.4pArms thus achieving a dynamic range of 115dB. The glucose prediction processor (GPP) removes noise from the PPG signal, extracts ten unique features, and estimates the blood glucose level using a trained customized NMG-SVR model that minimizes the hardware cost by 25%. The extracted features are carefully designed and implemented to ensure inter-feature dependency, which helps to reduce the overall area by more than 40%. Moreover, GPP is implemented using power and clock gating techniques to minimize both static and dynamic power consumption. The proposed SoC is realized with $0.18~\mu \text{m}$ CMOS technology and occupies an area of 6 mm2. It dissipates a power of $186~\mu \text{W}$ and achieves a mean absolute relative difference (mARD) of 6.9% verified on 200 subjects. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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