SiameseHAR: siamese-based model for human activity classification with FMCW radars

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buir.contributor.authorMorgül, Ömer
buir.contributor.orcidMorgül, Ömer|0000-0002-3158-3961
dc.citation.epage302en_US
dc.citation.spage291
dc.citation.volumeNumber716
dc.contributor.authorEge, Mert
dc.contributor.authorMorgül, Ömer
dc.date.accessioned2024-03-12T09:42:19Z
dc.date.available2024-03-12T09:42:19Z
dc.date.issued2023-06-03
dc.departmentDepartment of Electrical and Electronics Engineering
dc.description.abstractHuman Activity Recognition (HAR) is an attractive task in academic researchers. Furthermore, HAR is used in many areas such as security, sports activities, health, and entertainment. Frequency Modulated Continuous Wave (FMCW) radar data is a suitable option to classify human activities since it operates more robustly than a camera in difficult weather conditions such as fog and rain. Additionally, FMCW radars cost less than cameras. However, FMCW radars are less popular than camera-based HAR systems. This is mainly because the accuracy performance of FMCW radar data is lower than that of the camera when classifying human activation This article proposes the SiameseHAR model for the classification of human movement with FMCW radar data. In this model, we use LSTM and GRU blocks in parallel. In addition, we feed radar data operating at different frequencies (10 GHz, 24 GHz, 77 GHz) to the SiameseHAR model in parallel with the Siamese architecture. Therefore, the weights of the paths that use different radar data as inputs are tied. As far as we know, it is the first time that the multi-input Siamese architecture has been used for human activity classification. The SiameseHAR model we proposed is superior to most of the state-of-the-art models.
dc.description.provenanceMade available in DSpace on 2024-03-12T09:42:19Z (GMT). No. of bitstreams: 1 SiameseHAR_siamese-based_model_for_human_activity_classification_with_FMCW_radars.pdf: 630876 bytes, checksum: 3e350fd7839b52db754ef348a14b9cc6 (MD5) Previous issue date: 2023-06en
dc.identifier.doi10.1007/978-3-031-35501-1_29
dc.identifier.eisbn9783031355011
dc.identifier.eissn2367-3389
dc.identifier.isbn9783031355004
dc.identifier.issn2367-3370
dc.identifier.urihttps://hdl.handle.net/11693/114568
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofIntelligent Systems Design and Applications 22nd International Conference on Intelligent Systems Design and Applications (ISDA 2022) Held December 12–14, 2022 - Volume 3
dc.relation.ispartofseriesLecture Notes in Networks and Systems Volume 716 LNNS
dc.relation.ispartofseries22nd International Conference on Intelligent Systems Design and Applications (ISDA 2022)
dc.relation.isversionofhttps://dx.doi.org/10.1007/978-3-031-35501-1_29
dc.rightsCC BY 4.0 DEED (Attribution 4.0 International)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.source.titleLecture Notes in Networks and Systems
dc.subjectHuman activity recognition
dc.subjectSiamese network
dc.subjectFMCW radar data
dc.subjectmicro-doppler signature
dc.subjectdeep learning
dc.titleSiameseHAR: siamese-based model for human activity classification with FMCW radars
dc.typeConference Paper

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