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dc.contributor.authorvan Hees, Vincent T
dc.contributor.authorFang, Zhou
dc.contributor.authorLangford, Joss
dc.contributor.authorAssah, Felix
dc.contributor.authorMohammad, Anwar
dc.contributor.authorda Silva, Inacio CM
dc.contributor.authorTrenell, Michael I
dc.contributor.authorWhite, Thomas
dc.contributor.authorWareham, Nicholas
dc.contributor.authorBrage, Soren
dc.date.accessioned2014-08-22T13:46:20Z
dc.date.available2014-08-22T13:46:20Z
dc.date.issued2014-10-01
dc.identifier.citationHees et al. Journal of Applied Physiology (2014) Vol. 117 no. 7, pp. 738-744. DOI: 10.1152/japplphysiol.00421.2014.
dc.identifier.issn8750-7587
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/245783
dc.description.abstractWearable acceleration sensors are increasingly used for the assessment of free-living physical activity. Acceleration sensor calibration is a potential source of error. This study aims to describe and evaluate an autocalibration method to minimize calibration error using segments within the free-living records (no extra experiments needed). The autocalibration method entailed the extraction of nonmovement periods in the data, for which the measured vector magnitude should ideally be the gravitational acceleration (1 g); this property was used to derive calibration correction factors using an iterative closest-point fitting process. The reduction in calibration error was evaluated in data from four cohorts: UK (n = 921), Kuwait (n = 120), Cameroon (n = 311), and Brazil (n = 200). Our method significantly reduced calibration error in all cohorts (P < 0.01), ranging from 16.6 to 3.0 mg in the Kuwaiti cohort to 76.7 to 8.0 mg error in the Brazil cohort. Utilizing temperature sensor data resulted in a small nonsignificant additional improvement (P > 0.05). Temperature correction coefficients were highest for the z-axis, e.g., 19.6-mg offset per 5°C. Further, application of the autocalibration method had a significant impact on typical metrics used for describing human physical activity, e.g., in Brazil average wrist acceleration was 0.2 to 51% lower than uncalibrated values depending on metric selection (P < 0.01). The autocalibration method as presented helps reduce the calibration error in wearable acceleration sensor data and improves comparability of physical activity measures across study locations. Temperature ultization seems essential when temperature deviates substantially from the average temperature in the record but not for multiday summary measures.
dc.languageEnglish
dc.language.isoen
dc.publisherAmerican Physiological Society
dc.rights.urihttp://creativecommons.org/licenses/by/2.0/uk/
dc.rights.uriAttribution 2.0 UK: England & Wales
dc.titleAutocalibration of accelerometer data for free-living physical activity assessment using local gravity and temperature: an evaluation on four continents.
dc.typeArticle
dc.description.versionThis is the final version of the article. It first appeared from the American Physiological Society via http://dx.doi.org/10.1152/japplphysiol.00421.2014
prism.endingPage744
prism.publicationDate2014
prism.publicationNameJ Appl Physiol (1985)
prism.startingPage738
prism.volume117
rioxxterms.versionofrecord10.1152/japplphysiol.00421.2014
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2014-08-07
dc.contributor.orcidWhite, Thomas [0000-0001-8456-0803]
dc.contributor.orcidWareham, Nicholas [0000-0003-1422-2993]
dc.contributor.orcidBrage, Soren [0000-0002-1265-7355]
dc.identifier.eissn1522-1601
rioxxterms.typeJournal Article/Review
pubs.funder-project-idMedical Research Council (MC_UU_12015/1)
pubs.funder-project-idMedical Research Council (MC_UU_12015/3)
pubs.funder-project-idMedical Research Council (MC_U106179471)


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