Publication Type : Journal Article
Publisher : Elsevier BV
Source : Sensors and Actuators A: Physical
Url : https://doi.org/10.1016/j.sna.2025.116391
Keywords : Photoacoustic spectroscopy, Non-invasive procedures, In vivo techniques, Pulsed lasers, Shallow dense neural network, Hybrid loss function, IoT
Campus : Amaravati
School : School of Computing
Department : Computer Science and Engineering
Year : 2025
Abstract : Diabetes management requires frequent blood glucose monitoring, yet invasive procedures impede testing. A noninvasive approach to detect random blood glucose (RBG) is crucial for early diagnosis and timely treatment. This work leverages Photoacoustic Spectroscopy for the detection of RBG due to its high sensitivity, specificity, and real-time monitoring capabilities. Therefore PAS has been implemented with a shallow dense neural network using a hybrid loss function (logcosh + huber loss) to estimate RBG. The augmentation of blood glucose is obtained by integrating biological parameters of a person like Body Mass Index, Age, and Spo2 with photoacoustic signal values. The intended hardware setup integrates with a Raspberry Pi 4 enabling real-time monitoring through the Thingspeak cloud platform. Testing with 105 in vivo samples demonstrated accuracies of 2.86 mg/dl (RMSE), 8.77 mg/dl (MAD), and 8.49% (MARD). Overall, an IoT-based PAS portable device is designed to provide smart healthcare services and quality care improvement.
Cite this Research Publication : P.N.S.B.S.V. Prasad V., Syed Ali Hussain, Amrit Kumar Singha, Biswabandhu Jana, Pranab Mandal, Pradyut Kumar Sanki, An advanced IoT-based non-invasive in vivo blood glucose estimation exploiting photoacoustic spectroscopy with SDNN architecture, Sensors and Actuators A: Physical, Elsevier BV, 2025, https://doi.org/10.1016/j.sna.2025.116391
