Non-invasive blood glucose concentration level estimation accuracy using ultra-wide band and artificial intelligence
Diabetes becomes a rapidly increasing global epidemic and getting serious health concern worldwide. There is no remedy except systematic management to keep blood glucose level under control. To achieve that regular glucose level monitoring is a routine task for a patient. This involves collection of...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Springer Nature
2020
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| Online Access: | http://umpir.ump.edu.my/id/eprint/28825/ http://umpir.ump.edu.my/id/eprint/28825/1/Non-invasive%20blood%20glucose%20concentration.pdf |
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| author | Islam, Minarul Ali, Md Shawkat Shoumy, Nusrat Jahan Sabira, Khatun Mohamad Shaiful, Abdul Karim Bari, Bifta Sama |
| author_facet | Islam, Minarul Ali, Md Shawkat Shoumy, Nusrat Jahan Sabira, Khatun Mohamad Shaiful, Abdul Karim Bari, Bifta Sama |
| author_sort | Islam, Minarul |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Diabetes becomes a rapidly increasing global epidemic and getting serious health concern worldwide. There is no remedy except systematic management to keep blood glucose level under control. To achieve that regular glucose level monitoring is a routine task for a patient. This involves collection of blood physically from body with some discomfort and measuring using some device. To overcome this disadvantages and distress, non-invasive blood glucose measurement system is in demand. This article presents an ultra-wide band (UWB) microwave imaging and artificial intelligence based prospective solution to detect blood glucose concentration level non-invasively (without physical blood). The system consists of a pair of small UWB biomedical planar antenna, UWB transceiver as hardware and an artificial neural network with signal acquisition and processing interface as software module. The UWB signal with center frequency of 4.7 GHz was transmitted through ear lobe and forward scattering signals were received from other side. Characteristics features of received signal were extracted for pattern recognition and detection through deep artificial neural network. The system exhibits around 88% accuracy to detect glucose concentration in blood plasma. Besides, it is affordable, safe, user friendly and can be used with comfort in near future. |
| first_indexed | 2025-11-15T02:52:28Z |
| format | Article |
| id | ump-28825 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:52:28Z |
| publishDate | 2020 |
| publisher | Springer Nature |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-288252020-07-21T01:23:46Z http://umpir.ump.edu.my/id/eprint/28825/ Non-invasive blood glucose concentration level estimation accuracy using ultra-wide band and artificial intelligence Islam, Minarul Ali, Md Shawkat Shoumy, Nusrat Jahan Sabira, Khatun Mohamad Shaiful, Abdul Karim Bari, Bifta Sama TK Electrical engineering. Electronics Nuclear engineering Diabetes becomes a rapidly increasing global epidemic and getting serious health concern worldwide. There is no remedy except systematic management to keep blood glucose level under control. To achieve that regular glucose level monitoring is a routine task for a patient. This involves collection of blood physically from body with some discomfort and measuring using some device. To overcome this disadvantages and distress, non-invasive blood glucose measurement system is in demand. This article presents an ultra-wide band (UWB) microwave imaging and artificial intelligence based prospective solution to detect blood glucose concentration level non-invasively (without physical blood). The system consists of a pair of small UWB biomedical planar antenna, UWB transceiver as hardware and an artificial neural network with signal acquisition and processing interface as software module. The UWB signal with center frequency of 4.7 GHz was transmitted through ear lobe and forward scattering signals were received from other side. Characteristics features of received signal were extracted for pattern recognition and detection through deep artificial neural network. The system exhibits around 88% accuracy to detect glucose concentration in blood plasma. Besides, it is affordable, safe, user friendly and can be used with comfort in near future. Springer Nature 2020 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/28825/1/Non-invasive%20blood%20glucose%20concentration.pdf Islam, Minarul and Ali, Md Shawkat and Shoumy, Nusrat Jahan and Sabira, Khatun and Mohamad Shaiful, Abdul Karim and Bari, Bifta Sama (2020) Non-invasive blood glucose concentration level estimation accuracy using ultra-wide band and artificial intelligence. SN Applied Sciences, 2 (278). pp. 1-9. ISSN 2523-3963 (Print); 2523-3971 (Online). (Published) https://doi.org/10.1007/s42452-019-1884-3 https://doi.org/10.1007/s42452-019-1884-3 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Islam, Minarul Ali, Md Shawkat Shoumy, Nusrat Jahan Sabira, Khatun Mohamad Shaiful, Abdul Karim Bari, Bifta Sama Non-invasive blood glucose concentration level estimation accuracy using ultra-wide band and artificial intelligence |
| title | Non-invasive blood glucose concentration level estimation accuracy using ultra-wide band and artificial intelligence |
| title_full | Non-invasive blood glucose concentration level estimation accuracy using ultra-wide band and artificial intelligence |
| title_fullStr | Non-invasive blood glucose concentration level estimation accuracy using ultra-wide band and artificial intelligence |
| title_full_unstemmed | Non-invasive blood glucose concentration level estimation accuracy using ultra-wide band and artificial intelligence |
| title_short | Non-invasive blood glucose concentration level estimation accuracy using ultra-wide band and artificial intelligence |
| title_sort | non-invasive blood glucose concentration level estimation accuracy using ultra-wide band and artificial intelligence |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/28825/ http://umpir.ump.edu.my/id/eprint/28825/ http://umpir.ump.edu.my/id/eprint/28825/ http://umpir.ump.edu.my/id/eprint/28825/1/Non-invasive%20blood%20glucose%20concentration.pdf |