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dc.rights.licenseopenen_US
hal.structure.identifierESTIA INSTITUTE OF TECHNOLOGY
dc.contributor.authorDELLA-LIBERA, Vincent
hal.structure.identifierESTIA INSTITUTE OF TECHNOLOGY
dc.contributor.authorLLARIA, Alvaro
ORCID: 0000-0002-0348-6419
IDREF: 259161004
hal.structure.identifierESTIA INSTITUTE OF TECHNOLOGY
dc.contributor.authorTERRASSON, Guillaume
ORCID: 0000-0002-3468-5883
IDREF: 136426158
hal.structure.identifierESTIA INSTITUTE OF TECHNOLOGY
dc.contributor.authorCUREA, Octavian
ORCID: 0000-0002-5030-2088
IDREF: 68259131
dc.date.accessioned2023-04-11T15:51:19Z
dc.date.available2023-04-11T15:51:19Z
dc.date.issued2020
dc.date.conference2020-06-10
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/172955
dc.description.abstractEnCyber-Physical Systems (CPS) are more and more present in many domains such as Industry 4.0, Smart Farming and Smart Healthcare Systems, among others, mainly due to the advantages offered by the combination of embedded electronics and data processing methods, inherent to CPS. An application field for which the characteristics of CPS are well adapted is the animal or human activity recognition, that can be useful to detect health troubles of the individuals under study. In this frame, this paper evaluates the adequation of different combinations of inertial sensors, together with an Artificial Neural Network based algorithm, to recognize basic human motion. The obtained experimental results are analyzed to corroborate if devices embedding different sensors, like Inertial Measurement Units, offer better performances than accelerometers when recognizing locomotion movements.
dc.language.isoENen_US
dc.subject.enCyber-Physical Systems
dc.subject.enWearable inertial sensors
dc.subject.enInertial measurement unit
dc.subject.enHuman activity recognition
dc.subject.enArtificial neural network
dc.subject.enSmart Healthcare
dc.title.enWearable inertial sensors to recognize basic human motion: What technology for what activity?
dc.typeCommunication dans un congrès avec actesen_US
dc.identifier.doi10.1109/ICPS48405.2020.9274718en_US
dc.subject.halSciences de l'ingénieur [physics]/Electroniqueen_US
dc.subject.halInformatique [cs]/Réseau de neurones [cs.NE]en_US
bordeaux.page344-349en_US
bordeaux.hal.laboratoriesESTIA - Rechercheen_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionBordeaux INPen_US
bordeaux.institutionBordeaux Sciences Agroen_US
bordeaux.conference.title3rd IEEE International Conference on Industrial Cyber-Physical Systems (ICPS)en_US
bordeaux.title.proceedingProceedings of the 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS)en_US
bordeaux.conference.cityTampereen_US
bordeaux.peerReviewedouien_US
bordeaux.import.sourcehal
hal.identifierhal-03038210
hal.version1
hal.exportfalse
workflow.import.sourcehal
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2020&rft.spage=344-349&rft.epage=344-349&rft.au=DELLA-LIBERA,%20Vincent&LLARIA,%20Alvaro&TERRASSON,%20Guillaume&CUREA,%20Octavian&rft.genre=proceeding


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