dc.contributor.author | Sahu, Sahil | |
dc.contributor.author | Jalote, Pankaj (Advisor) | |
dc.contributor.author | Sethi, Tavpritesh (Advisor) | |
dc.date.accessioned | 2024-05-13T09:58:25Z | |
dc.date.available | 2024-05-13T09:58:25Z | |
dc.date.issued | 2023-11-29 | |
dc.identifier.uri | http://repository.iiitd.edu.in/xmlui/handle/123456789/1442 | |
dc.description.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. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | IIIT-Delhi | en_US |
dc.subject | PPG | en_US |
dc.subject | GSR | en_US |
dc.subject | diabetes | en_US |
dc.subject | blood glucose | en_US |
dc.subject | signal processing | en_US |
dc.subject | machine learning | en_US |
dc.title | Continuous glucose monitoring | en_US |
dc.type | Other | en_US |