Detection of Risky Situations for Frail Adults With Hybrid Neural Networks on Multimodal Health Data
| dc.rights.license | open | en_US |
| hal.structure.identifier | Laboratoire Bordelais de Recherche en Informatique [LaBRI] | |
| dc.contributor.author | MALLICK, Rupayan | |
| hal.structure.identifier | Laboratoire Bordelais de Recherche en Informatique [LaBRI] | |
| dc.contributor.author | YEBDA, Thinhinane | |
| hal.structure.identifier | Laboratoire Bordelais de Recherche en Informatique [LaBRI] | |
| dc.contributor.author | BENOIS PINEAU, Jenny
IDREF: 074466992 | |
| hal.structure.identifier | Laboratoire Bordelais de Recherche en Informatique [LaBRI] | |
| dc.contributor.author | ZEMMARI, Akka
IDREF: 157264491 | |
| hal.structure.identifier | Bordeaux population health [BPH] | |
| dc.contributor.author | PECH, Marion | |
| hal.structure.identifier | Bordeaux population health [BPH] | |
| dc.contributor.author | AMIEVA, Helene | |
| dc.date.accessioned | 2022-07-15T09:20:54Z | |
| dc.date.available | 2022-07-15T09:20:54Z | |
| dc.date.issued | 2022-02-01 | |
| dc.identifier.issn | 1070-986X | en_US |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/140496 | |
| dc.description.abstractEn | In healthcare applications, the multimedia methodology is applied to multimodal signals and visual data. This article focuses on the detection of risk situations of frail people from lifelog multimodal signals and video recorded with wearable sensors. We propose a hybrid 3D convolutional neural network (3DCNN) and gated recurrent unit (GRU) (3DCNN-GRU) deep architecture with two branches. The first branch is a GRU network with a global attention block for classification of multisensory signal data. The second branch is a 3DCNN with windowing synchronized with multidimensional time-series signals. Two branches of the neural network are fused yielding promising results. The method produces 83.26% accuracy with dataset BIRDS. Benchmarking is also fulfilled on a publicly available dataset in action recognition. | |
| dc.language.iso | EN | en_US |
| dc.title.en | Detection of Risky Situations for Frail Adults With Hybrid Neural Networks on Multimodal Health Data | |
| dc.type | Article de revue | en_US |
| dc.identifier.doi | 10.1109/Mmul.2022.3147381 | en_US |
| dc.subject.hal | Sciences du Vivant [q-bio]/Santé publique et épidémiologie | en_US |
| bordeaux.journal | IEEE MultiMedia | en_US |
| bordeaux.page | 7-17 | en_US |
| bordeaux.volume | 29 | en_US |
| bordeaux.hal.laboratories | Bordeaux Population Health Research Center (BPH) - UMR 1219 | en_US |
| bordeaux.issue | 1 | en_US |
| bordeaux.institution | Université de Bordeaux | en_US |
| bordeaux.institution | INSERM | en_US |
| bordeaux.institution | Bordeaux INP | |
| bordeaux.institution | CNRS | |
| bordeaux.team | ACTIVE_BPH | en_US |
| bordeaux.peerReviewed | oui | en_US |
| bordeaux.inpress | non | en_US |
| hal.identifier | hal-03723815 | |
| hal.version | 1 | |
| hal.date.transferred | 2022-07-15T09:20:56Z | |
| hal.export | true | |
| dc.rights.cc | Pas de Licence CC | en_US |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=IEEE%20MultiMedia&rft.date=2022-02-01&rft.volume=29&rft.issue=1&rft.spage=7-17&rft.epage=7-17&rft.eissn=1070-986X&rft.issn=1070-986X&rft.au=MALLICK,%20Rupayan&YEBDA,%20Thinhinane&BENOIS%20PINEAU,%20Jenny&ZEMMARI,%20Akka&PECH,%20Marion&rft.genre=article |
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