1. The open D1NAMO dataset:A multi-modal dataset for research on non-invasive type 1 diabetes management
- Author
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Jean-Paul Calbimonte, Fabien Dubosson, Michael Schumacher, Jean-Eudes Ranvier, Juan Ruiz, Stefano Bromuri, Department Information Science and Business Processes, and RS-Research Line Resilience (part of LIRS program)
- 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 - 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
- Published
- 2018