Related Experiment Video
Updated: Apr 19, 2026
Author Spotlight: Enhancing Vascular Function and Physical Capacity in Cardiovascular Disease Through Novel Interventions and NIRS Technology
Published on: March 22, 2024
Noninvasive blood glucose sensing using near infra-red spectroscopy and artificial neural networks based on inverse
Swathi Ramasahayam1, Sri Haindavi Koppuravuri, Lavanya Arora
1Center for VLSI and Embedded System Technology, International Institute of Information Technology, Hyderabad, AP, India, swathi.ramasahayam@research.iiit.ac.in.
Abstract:
In this paper, a non-invasive blood glucose sensing system is presented using near infra-red(NIR) spectroscopy. The signal from the NIR optodes is processed using artificial neural networks (ANN) to estimate the glucose level in blood. In order to obtain accurate values of the synaptic weights of the ANN, inverse delayed (ID) function model of neuron has been used. The ANN model has been implemented on field programmable gate array (FPGA). Error in estimating glucose levels using ANN based on ID function model of neuron implemented on FPGA, came out to be 1.02 mg/dl using 15 hidden neurons in the hidden layer as against 5.48 mg/dl using ANN based on conventional neuron model.
More Related Videos
07:27How to Administer Near-Infrared Spectroscopy in Critically ill Neonates, Infants, and Children
Published on: August 19, 2020
14:28Non-Invasive Monitoring of Microvascular Oxygenation and Reactive Hyperemia using Hybrid, Near-Infrared Diffuse Optical Spectroscopy for Critical Care
Published on: May 10, 2024