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J Appl Physiol (1985). 2014 Oct 1;117(7):738-44. doi: 10.1152/japplphysiol.00421.2014. Epub 2014 Aug 7.

Autocalibration of accelerometer data for free-living physical activity assessment using local gravity and temperature: an evaluation on four continents.

Author information

1
MoveLab, Institute of Cellular Medicine, Newcastle University, Newcastle, United Kingdom; Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom.
2
Department of Statistics, University of Oxford, Oxford, United Kingdom; Activinsight, Limited, Kimbolton, United Kingdom;
3
Activinsight, Limited, Kimbolton, United Kingdom;
4
University of Yaoundé, Yaoundé, Cameroon;
5
Dasman Diabetes Institute, Kuwait City, Kuwait;
6
Federal University of Pelotas-Postgraduate Program in Epidemiology, Pelotas, Brazil; and Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom.
7
MoveLab, Institute of Cellular Medicine, Newcastle University, Newcastle, United Kingdom;
8
Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom.
9
Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom soren.brage@mrc-epid.cam.ac.uk.

Abstract

Wearable 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.

KEYWORDS:

GENEActiv; accelerometry; calibration; epidemiology; physical activity

PMID:
25103964
PMCID:
PMC4187052
DOI:
10.1152/japplphysiol.00421.2014
[Indexed for MEDLINE]
Free PMC Article

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