dc.contributor.author | Waqar, Sahil | |
dc.contributor.author | Pätzold, Matthias Uwe | |
dc.date.accessioned | 2024-07-02T11:29:04Z | |
dc.date.available | 2024-07-02T11:29:04Z | |
dc.date.created | 2024-01-16T16:43:35Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | Waqar, S. & Pätzold, M. U. (2023). A Simulation-Based Framework for the Design of Human Activity Recognition Systems Using Radar Sensors. IEEE Internet of Things Journal, 11 (8), 14494-14507. | en_US |
dc.identifier.issn | 2327-4662 | |
dc.identifier.uri | https://hdl.handle.net/11250/3137316 | |
dc.description.abstract | Modern human activity recognition (HAR) systems are designed using large amounts of experimental data. So far, real-data-driven or experimental-based HAR systems using Wi-Fi or radar systems have shown considerable results. However, the acquisition of large, clean, and labeled training data sets remains a crucial impediment to the progress of experimental-based HAR systems. Therefore, in this article, a paradigm shift from the experimental to a fully simulation-based design of HAR systems is proposed in the context of radar sensing. An end-to-end simulation framework is proposed as a proof-of-concept that can simulate realistic millimeter-wave radar signatures for synthesized human motion. We designed a human motion synthesis tool that emulates different types of human activities and generates the spatial trajectories accordingly. These trajectories are processed by a geometric model with respect to user-defined antenna configurations. Considering the long- and short-time stationarity of wireless channels, we synthesize the raw in-phase and quadrature data and process the data to simulate the radar signatures for emulated human activities. Finally, a simulated and a real HAR data set were used to train and test a simulation-based HAR system, respectively, which gave an average (maximum) classification accuracy of 94% (98.4%). The main advantage of the proposed simulation framework is that the training effort for radar-based classifiers, e.g., gesture recognition systems, can be minimized drastically. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | IEEE | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | A Simulation-Based Framework for the Design of Human Activity Recognition Systems Using Radar Sensors | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.rights.holder | © 2023 The Author(s) | en_US |
dc.subject.nsi | VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550 | en_US |
dc.source.pagenumber | 14494-14507 | en_US |
dc.source.volume | 11 | en_US |
dc.source.journal | IEEE Internet of Things Journal | en_US |
dc.source.issue | 8 | en_US |
dc.identifier.doi | https://doi.org/10.1109/JIOT.2023.3344179 | |
dc.identifier.cristin | 2228055 | |
dc.relation.project | Norges forskningsråd: 300638 | en_US |
cristin.qualitycode | 2 | |