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dc.contributor.authorLogacjov, Aleksej
dc.contributor.authorBach, Kerstin
dc.contributor.authorKongsvold, Atle
dc.contributor.authorBårdstu, Hilde Bremseth
dc.contributor.authorMork, Paul Jarle
dc.date.accessioned2022-03-18T08:00:08Z
dc.date.available2022-03-18T08:00:08Z
dc.date.created2021-11-26T07:18:22Z
dc.date.issued2021
dc.identifier.issn1424-8220
dc.identifier.urihttps://hdl.handle.net/11250/2986024
dc.description.abstractExisting accelerometer-based human activity recognition (HAR) benchmark datasets that were recorded during free living suffer from non-fixed sensor placement, the usage of only one sensor, and unreliable annotations. We make two contributions in this work. First, we present the publicly available Human Activity Recognition Trondheim dataset (HARTH). Twenty-two participants were recorded for 90 to 120 min during their regular working hours using two three-axial accelerometers, attached to the thigh and lower back, and a chest-mounted camera. Experts annotated the data independently using the camera’s video signal and achieved high inter-rater agreement (Fleiss’ Kappa =0.96). They labeled twelve activities. The second contribution of this paper is the training of seven different baseline machine learning models for HAR on our dataset. We used a support vector machine, k-nearest neighbor, random forest, extreme gradient boost, convolutional neural network, bidirectional long short-term memory, and convolutional neural network with multi-resolution blocks. The support vector machine achieved the best results with an F1-score of 0.81 (standard deviation: ±0.18), recall of 0.85±0.13, and precision of 0.79±0.22 in a leave-one-subject-out cross-validation. Our highly professional recordings and annotations provide a promising benchmark dataset for researchers to develop innovative machine learning approaches for precise HAR in free living.en_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.relation.urihttps://www.mdpi.com/1424-8220/21/23/7853
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleHarth: A human activity recognition dataset for machine learningen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.volume21en_US
dc.source.journalSensorsen_US
dc.source.issue23en_US
dc.identifier.doi10.3390/s21237853
dc.identifier.cristin1959458
dc.relation.projectNorges teknisk-naturvitenskapelige universitet: Project No. 81148022en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal