Back to Search
Start Over
The open D1NAMO dataset:A multi-modal dataset for research on non-invasive type 1 diabetes management
- Source :
- Dubosson, F, Ranvier, J-E, Bromuri, S, Calbimonte, J-P, Ruiz, J & Schumacher, M 2018, ' The open D1NAMO dataset : A multi-modal dataset for research on non-invasive type 1 diabetes management ', Informatics in Medicine Unlocked, vol. 13, pp. 92-100 . https://doi.org/10.1016/j.imu.2018.09.003, Informatics in Medicine Unlocked, Vol 13, Iss, Pp 92-100 (2018), Informatics in Medicine Unlocked, 13, 92-100. Elsevier Ltd
- Publication Year :
- 2018
-
Abstract
- The usage of wearable devices has gained popularity in the latest years, especially for health-care and well being. Recently there has been an increasing interest in using these devices to improve the management of chronic diseases such as diabetes. The quality of data acquired through wearable sensors is generally lower than what medical-grade devices provide, and existing datasets have mainly been acquired in highly controlled clinical conditions. In the context of the D1NAMO project — aiming to detect glycemic events through non-invasive ECG pattern analysis — we elaborated a dataset that can be used to help developing health-care systems based on wearable devices in non-clinical conditions. This paper describes this dataset, which was acquired on 20 healthy subjects and 9 patients with type-1 diabetes. The acquisition has been made in real-life conditions with the Zephyr BioHarness 3 wearable device. The dataset consists of ECG, breathing, and accelerometer signals, as well as glucose measurements and annotated food pictures. We open this dataset to the scientific community in order to allow the development and evaluation of diabetes management algorithms. Keywords: Diabetes management, Wearable devices, Glucose, ECG, Accelerometers, Annotated food pictures
- Subjects :
- Annotated food pictures
Computer science
Wearable computer
030209 endocrinology & metabolism
Health Informatics
Context (language use)
030204 cardiovascular system & hematology
Accelerometer
Machine learning
computer.software_genre
lcsh:Computer applications to medicine. Medical informatics
03 medical and health sciences
Diabetes management, Wearable devices, Glucose, ECG, Accelerometers, Annotated food pictures
0302 clinical medicine
Diabetes management
Wearable technology
business.industry
ECG
Non invasive
Healthy subjects
Wearable devices
Modal
Glucose
ComputingMethodologies_PATTERNRECOGNITION
lcsh:R858-859.7
Artificial intelligence
Accelerometers
business
computer
Subjects
Details
- Language :
- Dutch; Flemish
- ISSN :
- 23529148
- Database :
- OpenAIRE
- Journal :
- Dubosson, F, Ranvier, J-E, Bromuri, S, Calbimonte, J-P, Ruiz, J & Schumacher, M 2018, ' The open D1NAMO dataset : A multi-modal dataset for research on non-invasive type 1 diabetes management ', Informatics in Medicine Unlocked, vol. 13, pp. 92-100 . https://doi.org/10.1016/j.imu.2018.09.003, Informatics in Medicine Unlocked, Vol 13, Iss, Pp 92-100 (2018), Informatics in Medicine Unlocked, 13, 92-100. Elsevier Ltd
- Accession number :
- edsair.doi.dedup.....c8856ca0774d8546c30cb2d58ade663f