Please use this identifier to cite or link to this item:
http://repository.iiitd.edu.in/xmlui/handle/123456789/1442| Title: | Continuous glucose monitoring |
| Authors: | Sahu, Sahil Jalote, Pankaj (Advisor) Sethi, Tavpritesh (Advisor) |
| Keywords: | PPG GSR diabetes blood glucose signal processing machine learning |
| Issue Date: | 29-Nov-2023 |
| Publisher: | IIIT-Delhi |
| Abstract: | Diabetes is a chronic illness that affects millions of people worldwide and requires regular monitoring of a patient’s blood glucose level. Currently, blood glucose is monitored by a minimally invasive process where a small droplet of blood is extracted and passed to a glucometer—however, this process is uncomfortable for the patient. In this project, a noninvasive technique is investigated for the quantitative estimation of glucose levels in the blood based on Photoplethysmography (PPG) signal, Galvanic Skin Response, and other biosignals. |
| URI: | http://repository.iiitd.edu.in/xmlui/handle/123456789/1442 |
| Appears in Collections: | Year-2023 |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| BTP_Report - Sahil Sahu.pdf Restricted Access | 1.67 MB | Adobe PDF | View/Open Request a copy |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.