Academic Journal
Semantic representation and processing of hypoglycemic events derived from wearable sensor data
| Τίτλος: | Semantic representation and processing of hypoglycemic events derived from wearable sensor data |
|---|---|
| Συγγραφείς: | Calbimonte, Jean-Paul, Ranvier, Jean-Eudes, Dubosson, Fabien, Aberer, Karl |
| Στοιχεία εκδότη: | Ios Press Amsterdam |
| Έτος έκδοσης: | 2017 |
| Συλλογή: | Ecole Polytechnique Fédérale Lausanne (EPFL): Infoscience |
| Θεματικοί όροι: | RDF streams, diabetes, sensors, personalized health, semantic sensor networks |
| Περιγραφή: | Diabetes Type 1 is a metabolic disease which results in a lack of insulin production, causing high glucose levels in the blood. It is crucial for diabetic patients to balance this glucose level, and they depend on external substances to do so. In order to keep this level under control, they usually need to resort to invasive glucose control methods, such as taking a sample drop of blood from their finger and have it analyzed. Recently, other directions emerged to offer alternative ways to estimate glucose level, using indirect sensor measurements including ECG monitoring and other physiological parameters. This paper showcases a framework for inferring semantically annotated glycemic events on the patient, which leverages data from mobile wearable sensors deployed on a sport-belt. This work is part of the D1namo project for non-invasive diabetes monitoring, and focuses on the representation and query processing of the data produced by the wearable sensors, using semantic technologies and vocabularies that extend existing Web standards. Furthermore, this work shows how different layers of data, from raw measurements to complex events can be represented and linked in this framework, and experimental evidence is provided of how these layers can be efficiently exploited using an RDF Stream Processing engine. ; LSIR |
| Τύπος εγγράφου: | article in journal/newspaper |
| Γλώσσα: | unknown |
| ISSN: | 1876-1364 |
| Relation: | Journal Of Ambient Intelligence And Smart Environments; https://infoscience.epfl.ch/handle/20.500.14299/136699; WOS:000394151700009 |
| DOI: | 10.3233/Ais-160420 |
| Διαθεσιμότητα: | https://doi.org/10.3233/Ais-160420 https://infoscience.epfl.ch/handle/20.500.14299/136699 https://hdl.handle.net/20.500.14299/136699 |
| Αριθμός Καταχώρησης: | edsbas.6F81C67C |
| Βάση Δεδομένων: | BASE |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://doi.org/10.3233/Ais-160420# Name: EDS - BASE (ns324271) Category: fullText Text: View record from BASE |
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| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Semantic representation and processing of hypoglycemic events derived from wearable sensor data – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Calbimonte%2C+Jean-Paul%22">Calbimonte, Jean-Paul</searchLink><br /><searchLink fieldCode="AR" term="%22Ranvier%2C+Jean-Eudes%22">Ranvier, Jean-Eudes</searchLink><br /><searchLink fieldCode="AR" term="%22Dubosson%2C+Fabien%22">Dubosson, Fabien</searchLink><br /><searchLink fieldCode="AR" term="%22Aberer%2C+Karl%22">Aberer, Karl</searchLink> – Name: Publisher Label: Publisher Information Group: PubInfo Data: Ios Press<br />Amsterdam – Name: DatePubCY Label: Publication Year Group: Date Data: 2017 – Name: Subset Label: Collection Group: HoldingsInfo Data: Ecole Polytechnique Fédérale Lausanne (EPFL): Infoscience – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22RDF+streams%22">RDF streams</searchLink><br /><searchLink fieldCode="DE" term="%22diabetes%22">diabetes</searchLink><br /><searchLink fieldCode="DE" term="%22sensors%22">sensors</searchLink><br /><searchLink fieldCode="DE" term="%22personalized+health%22">personalized health</searchLink><br /><searchLink fieldCode="DE" term="%22semantic+sensor+networks%22">semantic sensor networks</searchLink> – Name: Abstract Label: Description Group: Ab Data: Diabetes Type 1 is a metabolic disease which results in a lack of insulin production, causing high glucose levels in the blood. It is crucial for diabetic patients to balance this glucose level, and they depend on external substances to do so. In order to keep this level under control, they usually need to resort to invasive glucose control methods, such as taking a sample drop of blood from their finger and have it analyzed. Recently, other directions emerged to offer alternative ways to estimate glucose level, using indirect sensor measurements including ECG monitoring and other physiological parameters. This paper showcases a framework for inferring semantically annotated glycemic events on the patient, which leverages data from mobile wearable sensors deployed on a sport-belt. This work is part of the D1namo project for non-invasive diabetes monitoring, and focuses on the representation and query processing of the data produced by the wearable sensors, using semantic technologies and vocabularies that extend existing Web standards. Furthermore, this work shows how different layers of data, from raw measurements to complex events can be represented and linked in this framework, and experimental evidence is provided of how these layers can be efficiently exploited using an RDF Stream Processing engine. ; LSIR – Name: TypeDocument Label: Document Type Group: TypDoc Data: article in journal/newspaper – Name: Language Label: Language Group: Lang Data: unknown – Name: ISSN Label: ISSN Group: ISSN Data: 1876-1364 – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: Journal Of Ambient Intelligence And Smart Environments; https://infoscience.epfl.ch/handle/20.500.14299/136699; WOS:000394151700009 – Name: DOI Label: DOI Group: ID Data: 10.3233/Ais-160420 – Name: URL Label: Availability Group: URL Data: https://doi.org/10.3233/Ais-160420<br />https://infoscience.epfl.ch/handle/20.500.14299/136699<br />https://hdl.handle.net/20.500.14299/136699 – Name: AN Label: Accession Number Group: ID Data: edsbas.6F81C67C |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.6F81C67C |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3233/Ais-160420 Languages: – Text: unknown Subjects: – SubjectFull: RDF streams Type: general – SubjectFull: diabetes Type: general – SubjectFull: sensors Type: general – SubjectFull: personalized health Type: general – SubjectFull: semantic sensor networks Type: general Titles: – TitleFull: Semantic representation and processing of hypoglycemic events derived from wearable sensor data Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Calbimonte, Jean-Paul – PersonEntity: Name: NameFull: Ranvier, Jean-Eudes – PersonEntity: Name: NameFull: Dubosson, Fabien – PersonEntity: Name: NameFull: Aberer, Karl IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2017 Identifiers: – Type: issn-print Value: 18761364 – Type: issn-locals Value: edsbas |
| ResultId | 1 |