Software module development for non-invasive blood glucose measurement using an ultra-wide band and machine learning
Diabetes is a chronic disease and in uprising trend worldwide. There is no remedy, hence, blood glucose management is essential by screening blood glucose concentration levels (BGCL) regularly to maintain a healthy life. However, the present way of measuring BGCL is invasive by using a glucometer an...
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| Format: | Conference or Workshop Item |
| Language: | English |
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IOP Publishing
2020
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/28823/ http://umpir.ump.edu.my/id/eprint/28823/1/Software%20module%20development%20for%20non-invasive%20blood%20glucose.pdf |
| _version_ | 1848823143995539456 |
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| author | Islam, Minarul Chai, Ly Min Shoumy, Nusrat Jahan Ali, Md Shawkat Sabira, Khatun Mohamad Shaiful, Abdul Karim Bari, Bifta Sama |
| author_facet | Islam, Minarul Chai, Ly Min Shoumy, Nusrat Jahan Ali, Md Shawkat Sabira, Khatun Mohamad Shaiful, Abdul Karim Bari, Bifta Sama |
| author_sort | Islam, Minarul |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Diabetes is a chronic disease and in uprising trend worldwide. There is no remedy, hence, blood glucose management is essential by screening blood glucose concentration levels (BGCL) regularly to maintain a healthy life. However, the present way of measuring BGCL is invasive by using a glucometer and drawing a blood sample directly from the human body. To overcome this discomfort-problem, a non-invasive device to measure BGCL is in demand. This paper presents an autonomous software module with a user-friendly graphical user interface (GUI) based on digital signal processing (DSP) and artificial neural network (ANN) to process, classify and recognize the BGL signature from captured ultra-wideband (UWB) signal through human blood medium. To capture the signal, a pair of UWB bio-antenna is placed in between the human earlobe. Received signals are captured and processed through GUI and undergo signal processing, ANN training, testing, and validation. An interface is developed to integrate the hardware (UWB transceiver, bio-antenna, etc.) and the developed software module to make a system. The initial system showed a consistent result with reliability and demonstrated 90.6% accuracy to detect the BGCL. The detection accuracy is 9.6% improved compared to existing work. Besides, this proposed system is cost-effective, user-friendly and suitable to be used by both doctors and home users. |
| first_indexed | 2025-11-15T02:52:27Z |
| format | Conference or Workshop Item |
| id | ump-28823 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:52:27Z |
| publishDate | 2020 |
| publisher | IOP Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-288232020-07-20T08:08:21Z http://umpir.ump.edu.my/id/eprint/28823/ Software module development for non-invasive blood glucose measurement using an ultra-wide band and machine learning Islam, Minarul Chai, Ly Min Shoumy, Nusrat Jahan Ali, Md Shawkat Sabira, Khatun Mohamad Shaiful, Abdul Karim Bari, Bifta Sama R Medicine (General) TK Electrical engineering. Electronics Nuclear engineering Diabetes is a chronic disease and in uprising trend worldwide. There is no remedy, hence, blood glucose management is essential by screening blood glucose concentration levels (BGCL) regularly to maintain a healthy life. However, the present way of measuring BGCL is invasive by using a glucometer and drawing a blood sample directly from the human body. To overcome this discomfort-problem, a non-invasive device to measure BGCL is in demand. This paper presents an autonomous software module with a user-friendly graphical user interface (GUI) based on digital signal processing (DSP) and artificial neural network (ANN) to process, classify and recognize the BGL signature from captured ultra-wideband (UWB) signal through human blood medium. To capture the signal, a pair of UWB bio-antenna is placed in between the human earlobe. Received signals are captured and processed through GUI and undergo signal processing, ANN training, testing, and validation. An interface is developed to integrate the hardware (UWB transceiver, bio-antenna, etc.) and the developed software module to make a system. The initial system showed a consistent result with reliability and demonstrated 90.6% accuracy to detect the BGCL. The detection accuracy is 9.6% improved compared to existing work. Besides, this proposed system is cost-effective, user-friendly and suitable to be used by both doctors and home users. IOP Publishing 2020 Conference or Workshop Item PeerReviewed pdf en cc_by http://umpir.ump.edu.my/id/eprint/28823/1/Software%20module%20development%20for%20non-invasive%20blood%20glucose.pdf Islam, Minarul and Chai, Ly Min and Shoumy, Nusrat Jahan and Ali, Md Shawkat and Sabira, Khatun and Mohamad Shaiful, Abdul Karim and Bari, Bifta Sama (2020) Software module development for non-invasive blood glucose measurement using an ultra-wide band and machine learning. In: Journal of Physics: Conference Series, The 2nd Joint International Conference on Emerging Computing Technology and Sports (JICETS 2019) , 25-27 November 2019 , Bandung, Indonesia. pp. 1-12., 1529 (052066). ISSN 1742-6596 (Published) https://doi.org/10.1088/1742-6596/1529/5/052066 |
| spellingShingle | R Medicine (General) TK Electrical engineering. Electronics Nuclear engineering Islam, Minarul Chai, Ly Min Shoumy, Nusrat Jahan Ali, Md Shawkat Sabira, Khatun Mohamad Shaiful, Abdul Karim Bari, Bifta Sama Software module development for non-invasive blood glucose measurement using an ultra-wide band and machine learning |
| title | Software module development for non-invasive blood glucose measurement using an ultra-wide band and machine learning |
| title_full | Software module development for non-invasive blood glucose measurement using an ultra-wide band and machine learning |
| title_fullStr | Software module development for non-invasive blood glucose measurement using an ultra-wide band and machine learning |
| title_full_unstemmed | Software module development for non-invasive blood glucose measurement using an ultra-wide band and machine learning |
| title_short | Software module development for non-invasive blood glucose measurement using an ultra-wide band and machine learning |
| title_sort | software module development for non-invasive blood glucose measurement using an ultra-wide band and machine learning |
| topic | R Medicine (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/28823/ http://umpir.ump.edu.my/id/eprint/28823/ http://umpir.ump.edu.my/id/eprint/28823/1/Software%20module%20development%20for%20non-invasive%20blood%20glucose.pdf |