dc.contributor.author | Bourke, Alan | |
dc.contributor.author | Ihlen, Espen Alexander F. | |
dc.contributor.author | Bergquist, Ronny | |
dc.contributor.author | Wik, Per Bendik | |
dc.contributor.author | Vereijken, Beatrix | |
dc.contributor.author | Helbostad, Jorunn L. | |
dc.date.accessioned | 2018-01-02T15:29:44Z | |
dc.date.available | 2018-01-02T15:29:44Z | |
dc.date.created | 2017-10-12T12:21:58Z | |
dc.date.issued | 2017 | |
dc.identifier.issn | 1424-8220 | |
dc.identifier.uri | http://hdl.handle.net/11250/2474163 | |
dc.description.abstract | Physical activity monitoring algorithms are often developed using conditions that do not represent real-life activities, not developed using the target population, or not labelled to a high enough resolution to capture the true detail of human movement. We have designed a semi-structured supervised laboratory-based activity protocol and an unsupervised free-living activity protocol and recorded 20 older adults performing both protocols while wearing up to 12 body-worn sensors. Subjects’ movements were recorded using synchronised cameras (≥25 fps), both deployed in a laboratory environment to capture the in-lab portion of the protocol and a body-worn camera for out-of-lab activities. Video labelling of the subjects’ movements was performed by five raters using 11 different category labels. The overall level of agreement was high (percentage of agreement >90.05%, and Cohen’s Kappa, corrected kappa, Krippendorff’s alpha and Fleiss’ kappa >0.86). A total of 43.92 h of activities were recorded, including 9.52 h of in-lab and 34.41 h of out-of-lab activities. A total of 88.37% and 152.01% of planned transitions were recorded during the in-lab and out-of-lab scenarios, respectively. This study has produced the most detailed dataset to date of inertial sensor data, synchronised with high frame-rate (≥25 fps) video labelled data recorded in a free-living environment from older adults living independently. This dataset is suitable for validation of existing activity classification systems and development of new activity classification algorithms. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | MDPI | nb_NO |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | A physical activity reference data-set recorded from older adults using body-worn inertial sensors and video technology?The ADAPT study data-set | nb_NO |
dc.type | Journal article | nb_NO |
dc.type | Peer reviewed | nb_NO |
dc.description.version | publishedVersion | nb_NO |
dc.source.volume | 17 | nb_NO |
dc.source.journal | Sensors | nb_NO |
dc.source.issue | 3 | nb_NO |
dc.identifier.doi | 10.3390/s17030559 | |
dc.identifier.cristin | 1504113 | |
dc.description.localcode | © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | nb_NO |
cristin.unitcode | 194,65,30,0 | |
cristin.unitname | Institutt for nevromedisin og bevegelsesvitenskap | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |