Sense and Learn: Recent Advances in Wearable Sensing and Machine Learning for Blood Glucose Monitoring and Trend-Detection
<p dir="ltr">Diabetes mellitus is characterized by elevated blood glucose levels, however patients with diabetes may also develop hypoglycemia due to treatment. There is an increasing demand for non-invasive blood glucose monitoring and trends detection amongst people with diabetes a...
محفوظ في:
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2022
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إضافة وسم
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| _version_ | 1864513512349368320 |
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| author | Ahmad Yaser Alhaddad (7017434) |
| author2 | Hussein Aly (18877555) Hoda Gad (6470522) Abdulaziz Al-Ali (16393288) Kishor Kumar Sadasivuni (8036039) John-John Cabibihan (352200) Rayaz A. Malik (7372649) |
| author2_role | author author author author author author |
| author_facet | Ahmad Yaser Alhaddad (7017434) Hussein Aly (18877555) Hoda Gad (6470522) Abdulaziz Al-Ali (16393288) Kishor Kumar Sadasivuni (8036039) John-John Cabibihan (352200) Rayaz A. Malik (7372649) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ahmad Yaser Alhaddad (7017434) Hussein Aly (18877555) Hoda Gad (6470522) Abdulaziz Al-Ali (16393288) Kishor Kumar Sadasivuni (8036039) John-John Cabibihan (352200) Rayaz A. Malik (7372649) |
| dc.date.none.fl_str_mv | 2022-05-12T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3389/fbioe.2022.876672 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Sense_and_Learn_Recent_Advances_in_Wearable_Sensing_and_Machine_Learning_for_Blood_Glucose_Monitoring_and_Trend-Detection/26095453 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Cardiovascular medicine and haematology Engineering Biomedical engineering Information and computing sciences Machine learning diabetes mellitus non-invasive wearables and sensors hypoglycemia machine learning blood glucose management deep learning bodily fluids glucose |
| dc.title.none.fl_str_mv | Sense and Learn: Recent Advances in Wearable Sensing and Machine Learning for Blood Glucose Monitoring and Trend-Detection |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Diabetes mellitus is characterized by elevated blood glucose levels, however patients with diabetes may also develop hypoglycemia due to treatment. There is an increasing demand for non-invasive blood glucose monitoring and trends detection amongst people with diabetes and healthy individuals, especially athletes. Wearable devices and non-invasive sensors for blood glucose monitoring have witnessed considerable advances. This review is an update on recent contributions utilizing novel sensing technologies over the past five years which include electrocardiogram, electromagnetic, bioimpedance, photoplethysmography, and acceleration measures as well as bodily fluid glucose sensors to monitor glucose and trend detection. We also review methods that use machine learning algorithms to predict blood glucose trends, especially for high risk events such as hypoglycemia. Convolutional and recurrent neural networks, support vector machines, and decision trees are examples of such machine learning algorithms. Finally, we address the key limitations and challenges of these studies and provide recommendations for future work.</p><h2>Other Information</h2><p dir="ltr">Published in: Frontiers in Bioengineering and Biotechnology<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3389/fbioe.2022.876672" target="_blank">https://dx.doi.org/10.3389/fbioe.2022.876672</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_8a3a4f95a66f6888d2875e7a9409eac6 |
| identifier_str_mv | 10.3389/fbioe.2022.876672 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26095453 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Sense and Learn: Recent Advances in Wearable Sensing and Machine Learning for Blood Glucose Monitoring and Trend-DetectionAhmad Yaser Alhaddad (7017434)Hussein Aly (18877555)Hoda Gad (6470522)Abdulaziz Al-Ali (16393288)Kishor Kumar Sadasivuni (8036039)John-John Cabibihan (352200)Rayaz A. Malik (7372649)Biomedical and clinical sciencesCardiovascular medicine and haematologyEngineeringBiomedical engineeringInformation and computing sciencesMachine learningdiabetes mellitusnon-invasive wearables and sensorshypoglycemiamachine learningblood glucose managementdeep learningbodily fluids glucose<p dir="ltr">Diabetes mellitus is characterized by elevated blood glucose levels, however patients with diabetes may also develop hypoglycemia due to treatment. There is an increasing demand for non-invasive blood glucose monitoring and trends detection amongst people with diabetes and healthy individuals, especially athletes. Wearable devices and non-invasive sensors for blood glucose monitoring have witnessed considerable advances. This review is an update on recent contributions utilizing novel sensing technologies over the past five years which include electrocardiogram, electromagnetic, bioimpedance, photoplethysmography, and acceleration measures as well as bodily fluid glucose sensors to monitor glucose and trend detection. We also review methods that use machine learning algorithms to predict blood glucose trends, especially for high risk events such as hypoglycemia. Convolutional and recurrent neural networks, support vector machines, and decision trees are examples of such machine learning algorithms. Finally, we address the key limitations and challenges of these studies and provide recommendations for future work.</p><h2>Other Information</h2><p dir="ltr">Published in: Frontiers in Bioengineering and Biotechnology<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3389/fbioe.2022.876672" target="_blank">https://dx.doi.org/10.3389/fbioe.2022.876672</a></p>2022-05-12T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3389/fbioe.2022.876672https://figshare.com/articles/journal_contribution/Sense_and_Learn_Recent_Advances_in_Wearable_Sensing_and_Machine_Learning_for_Blood_Glucose_Monitoring_and_Trend-Detection/26095453CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/260954532022-05-12T06:00:00Z |
| spellingShingle | Sense and Learn: Recent Advances in Wearable Sensing and Machine Learning for Blood Glucose Monitoring and Trend-Detection Ahmad Yaser Alhaddad (7017434) Biomedical and clinical sciences Cardiovascular medicine and haematology Engineering Biomedical engineering Information and computing sciences Machine learning diabetes mellitus non-invasive wearables and sensors hypoglycemia machine learning blood glucose management deep learning bodily fluids glucose |
| status_str | publishedVersion |
| title | Sense and Learn: Recent Advances in Wearable Sensing and Machine Learning for Blood Glucose Monitoring and Trend-Detection |
| title_full | Sense and Learn: Recent Advances in Wearable Sensing and Machine Learning for Blood Glucose Monitoring and Trend-Detection |
| title_fullStr | Sense and Learn: Recent Advances in Wearable Sensing and Machine Learning for Blood Glucose Monitoring and Trend-Detection |
| title_full_unstemmed | Sense and Learn: Recent Advances in Wearable Sensing and Machine Learning for Blood Glucose Monitoring and Trend-Detection |
| title_short | Sense and Learn: Recent Advances in Wearable Sensing and Machine Learning for Blood Glucose Monitoring and Trend-Detection |
| title_sort | Sense and Learn: Recent Advances in Wearable Sensing and Machine Learning for Blood Glucose Monitoring and Trend-Detection |
| topic | Biomedical and clinical sciences Cardiovascular medicine and haematology Engineering Biomedical engineering Information and computing sciences Machine learning diabetes mellitus non-invasive wearables and sensors hypoglycemia machine learning blood glucose management deep learning bodily fluids glucose |