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dc.contributor.authorCetin, Gokhan
dc.contributor.authorKucuk, Mehmet Ali
dc.date.accessioned2023-09-04T12:15:41Z
dc.date.available2023-09-04T12:15:41Z
dc.date.issued2023en_US
dc.identifier.citationCetin, Gokhan Send mail to Cetin G.; Kucuk, Mehmet Ali end mail to Kucuk M.A.; Koroglu, Muhammed Taha Send mail to Koroglu M.T Save all to author list a Gumushane University, Electrical & Electronics Engineering, Gumushane, Turkeyen_US
dc.identifier.urihttps://s3-us-west-2.amazonaws.com/ieeeshutpages/xplore/xplore-shut-page.html
dc.identifier.urihttps://hdl.handle.net/20.500.12440/6000
dc.description.abstractTraditional pedestrian dead-reckoning (DR) methods utilizing inertial measurement units (IMU) have been successfully employed in GPS-denied environments over the past two decades. However, despite continuous advancements in wearable IMU technology, the long-term solution still suffers from the issue of drift. In light of this, Deep Learning (DL) has emerged as a potential candidate for inertial navigation solutions, leveraging its powerful non-linear regression capabilities. Recent research has demonstrated that DL models trained on public datasets can effectively serve as end-to-end solutions. Most studies focus on smartphones, where the IMU is typically situated at the torso, and generally work with transformed data represented in the navigation coordinate frame to estimate pedestrian velocity and heading change at rates lower than the IMU sampling rate. In contrast, this study uses raw foot-mounted IMU data as input to a multi-headed structure consisting of independent convolutional neural networks (CNNs) and targets displacement directly and heading change. The proposed model is trained on a publicly available foot-mounted IMU dataset where pseudo ground-truth (GT) data is generated heuristically to provide supervision for the learning process. The results indicate that the Multi-Head CNN (MHCNN) model is capable of producing pedestrian trajectories at the IMU sampling rate, with positioning errors of less than 2m throughout the duration of the walks. The code for the trained network can be accessed in the associated repository, i.e., github.com/mtkoroglu/PIN-MHCNN. © 2023 IEEE.en_US
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2023 IEEE International Workshop on Metrology for Industry 4.0 and IoT, MetroInd4.0 and IoT 2023 - Proceedingsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectconvolutional neural networken_US
dc.subjectdeep learningen_US
dc.subjectIMUen_US
dc.subjectKalman filteren_US
dc.subjectpedestrian INSen_US
dc.subjecttime-series predictionen_US
dc.titlePedestrian Inertial Navigation with Multi-Head CNNen_US
dc.typearticleen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik - Elektronik Mühendisliği Bölümüen_US
dc.authorid0000-0002-7960-1217en_US
dc.identifier.startpage275en_US
dc.contributor.institutionauthorCetin, Gokhan
dc.identifier.doi10.1109/MetroInd4.0IoT57462.2023.10180130en_US
dc.identifier.endpage280en_US
dc.authorwosidGEF-8940-2022en_US
dc.authorscopusid57193714537en_US


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