dc.contributor.author | Garcia-Ceja, Enrique | |
dc.contributor.author | Thambawita, Vajira L B | |
dc.contributor.author | Hicks, Steven | |
dc.contributor.author | Jha, Debesh | |
dc.contributor.author | Jakobsen, Petter | |
dc.contributor.author | Hammer, Hugo Lewi | |
dc.contributor.author | Halvorsen, Pål | |
dc.contributor.author | Riegler, Michael | |
dc.date.accessioned | 2022-03-11T09:58:31Z | |
dc.date.available | 2022-03-11T09:58:31Z | |
dc.date.issued | 2021-01-21 | |
dc.description.abstract | In this paper, we present HTAD: A Home Tasks Activities
Dataset. The dataset contains wrist-accelerometer and audio data from
people performing at-home tasks such as sweeping, brushing teeth, washing hands, or watching TV. These activities represent a subset of activities that are needed to be able to live independently. Being able to detect
activities with wearable devices in real-time is important for the realization of assistive technologies with applications in different domains such
as elderly care and mental health monitoring. Preliminary results show
that using machine learning with the presented dataset leads to promising results, but also there is still improvement potential. By making this
dataset public, researchers can test different machine learning algorithms
for activity recognition, especially, sensor data fusion methods | en_US |
dc.description | This version of the article has been accepted for publication, after peer review and is subject to <a href=https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms>Springer Nature’s AM terms of use</a>, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at <a href=https://doi.org/10.1007/978-3-030-67835-7_17>https://doi.org/10.1007/978-3-030-67835-7_17</a>. | en_US |
dc.identifier.citation | Garcia-Ceja E, Thambawita VLB, Hicks S, Jha D, Jakobsen P, Hammer HL, Halvorsen P, Riegler M. HTAD: A Home-Tasks Activities Dataset with Wrist-Accelerometer and Audio Features. Lecture Notes in Computer Science (LNCS). 2021;12573:196-205 | en_US |
dc.identifier.cristinID | FRIDAID 1975854 | |
dc.identifier.doi | 10.1007/978-3-030-67835-7_17 | |
dc.identifier.issn | 0302-9743 | |
dc.identifier.issn | 1611-3349 | |
dc.identifier.uri | https://hdl.handle.net/10037/24384 | |
dc.language.iso | eng | en_US |
dc.publisher | Springer Nature Switzerland AG | en_US |
dc.relation.journal | Lecture Notes in Computer Science (LNCS) | |
dc.rights.accessRights | openAccess | en_US |
dc.rights.holder | Copyright 2021 Springer Nature | en_US |
dc.title | HTAD: A Home-Tasks Activities Dataset with Wrist-Accelerometer and Audio Features | en_US |
dc.type.version | acceptedVersion | en_US |
dc.type | Journal article | en_US |
dc.type | Tidsskriftartikkel | en_US |
dc.type | Peer reviewed | en_US |