Prediabetes detection in unconstrained conditions using wearable sensors
Summary: Prediabetes is a common health condition that often goes undetected until it progresses to type 2 diabetes. Early identification of prediabetes is essential for timely intervention and prevention of complications. This research explores the feasibility of using wearable continuous glucose m...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Clinical Nutrition Open Science |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667268524000950 |
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| author | Dimitra Tatli Vasileios Papapanagiotou Aris Liakos Apostolos Tsapas Anastasios Delopoulos |
| author_facet | Dimitra Tatli Vasileios Papapanagiotou Aris Liakos Apostolos Tsapas Anastasios Delopoulos |
| author_sort | Dimitra Tatli |
| collection | DOAJ |
| description | Summary: Prediabetes is a common health condition that often goes undetected until it progresses to type 2 diabetes. Early identification of prediabetes is essential for timely intervention and prevention of complications. This research explores the feasibility of using wearable continuous glucose monitoring along with smartwatches with embedded inertial sensors to collect glucose measurements and acceleration signals respectively, for the early detection of prediabetes. We propose a methodology based on signal processing and machine learning techniques. Two feature sets are extracted from the collected signals, based both on a dynamic modeling of the human glucose-homeostasis system and on the Glucose curve, inspired by three major glucose related blood tests. Features are aggregated per individual using bootstrap. Support Vector Machines are used to classify normoglycemic vs. prediabetic individuals. We collected data from 22 participants for evaluation. The results are highly encouraging, demonstrating high sensitivity and precision. This work is a proof of concept, highlighting the potential of wearable devices in prediabetes assessment. Future directions involve expanding the study to a larger, more diverse population and exploring the integration of CGM and smartwatch functionalities into a unified device. Automated eating detecting algorithms can also be used. |
| format | Article |
| id | doaj-art-46f4fecb0d134204aab279a072b10793 |
| institution | OA Journals |
| issn | 2667-2685 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Clinical Nutrition Open Science |
| spelling | doaj-art-46f4fecb0d134204aab279a072b107932025-08-20T01:59:34ZengElsevierClinical Nutrition Open Science2667-26852024-12-015816317410.1016/j.nutos.2024.09.013Prediabetes detection in unconstrained conditions using wearable sensorsDimitra Tatli0Vasileios Papapanagiotou1Aris Liakos2Apostolos Tsapas3Anastasios Delopoulos4Multimedia Understanding Group, Dpt. of Electrical and Computer Engineering, Faculty of Engineering, AUTH, Greece; Embedded Systems Laboratory, Institute of Electrical and Micro Engineering, EPFL, Lausanne, Switzerland; Corresponding author. Embedded Systems Laboratory, Institute of Electrical and Micro Engineering, EPFL, Lausanne, Switzerland.Multimedia Understanding Group, Dpt. of Electrical and Computer Engineering, Faculty of Engineering, AUTH, Greece; IMPACT Research Group, Dpt. of Medicine, Karolinska Institutet, Huddinge, SwedenDiabetes Centre, Second Medical Department at Ippokratio General Hospital, Thessaloniki, GreeceDiabetes Centre, Second Medical Department at Ippokratio General Hospital, Thessaloniki, GreeceMultimedia Understanding Group, Dpt. of Electrical and Computer Engineering, Faculty of Engineering, AUTH, GreeceSummary: Prediabetes is a common health condition that often goes undetected until it progresses to type 2 diabetes. Early identification of prediabetes is essential for timely intervention and prevention of complications. This research explores the feasibility of using wearable continuous glucose monitoring along with smartwatches with embedded inertial sensors to collect glucose measurements and acceleration signals respectively, for the early detection of prediabetes. We propose a methodology based on signal processing and machine learning techniques. Two feature sets are extracted from the collected signals, based both on a dynamic modeling of the human glucose-homeostasis system and on the Glucose curve, inspired by three major glucose related blood tests. Features are aggregated per individual using bootstrap. Support Vector Machines are used to classify normoglycemic vs. prediabetic individuals. We collected data from 22 participants for evaluation. The results are highly encouraging, demonstrating high sensitivity and precision. This work is a proof of concept, highlighting the potential of wearable devices in prediabetes assessment. Future directions involve expanding the study to a larger, more diverse population and exploring the integration of CGM and smartwatch functionalities into a unified device. Automated eating detecting algorithms can also be used.http://www.sciencedirect.com/science/article/pii/S2667268524000950PrediabetesContinuous glucose monitorsSignal processingMachine learning |
| spellingShingle | Dimitra Tatli Vasileios Papapanagiotou Aris Liakos Apostolos Tsapas Anastasios Delopoulos Prediabetes detection in unconstrained conditions using wearable sensors Clinical Nutrition Open Science Prediabetes Continuous glucose monitors Signal processing Machine learning |
| title | Prediabetes detection in unconstrained conditions using wearable sensors |
| title_full | Prediabetes detection in unconstrained conditions using wearable sensors |
| title_fullStr | Prediabetes detection in unconstrained conditions using wearable sensors |
| title_full_unstemmed | Prediabetes detection in unconstrained conditions using wearable sensors |
| title_short | Prediabetes detection in unconstrained conditions using wearable sensors |
| title_sort | prediabetes detection in unconstrained conditions using wearable sensors |
| topic | Prediabetes Continuous glucose monitors Signal processing Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2667268524000950 |
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