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      Separating Movement and Gravity Components in an Acceleration Signal and Implications for the Assessment of Human Daily Physical Activity


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          Human body acceleration is often used as an indicator of daily physical activity in epidemiological research. Raw acceleration signals contain three basic components: movement, gravity, and noise. Separation of these becomes increasingly difficult during rotational movements. We aimed to evaluate five different methods (metrics) of processing acceleration signals on their ability to remove the gravitational component of acceleration during standardised mechanical movements and the implications for human daily physical activity assessment.


          An industrial robot rotated accelerometers in the vertical plane. Radius, frequency, and angular range of motion were systematically varied. Three metrics (Euclidian norm minus one [ENMO], Euclidian norm of the high-pass filtered signals [HFEN], and HFEN plus Euclidean norm of low-pass filtered signals minus 1 g [HFEN +]) were derived for each experimental condition and compared against the reference acceleration (forward kinematics) of the robot arm. We then compared metrics derived from human acceleration signals from the wrist and hip in 97 adults (22–65 yr), and wrist in 63 women (20–35 yr) in whom daily activity-related energy expenditure (PAEE) was available.


          In the robot experiment, HFEN + had lowest error during (vertical plane) rotations at an oscillating frequency higher than the filter cut-off frequency while for lower frequencies ENMO performed better. In the human experiments, metrics HFEN and ENMO on hip were most discrepant (within- and between-individual explained variance of 0.90 and 0.46, respectively). ENMO, HFEN and HFEN + explained 34%, 30% and 36% of the variance in daily PAEE, respectively, compared to 26% for a metric which did not attempt to remove the gravitational component (metric EN).


          In conclusion, none of the metrics as evaluated systematically outperformed all other metrics across a wide range of standardised kinematic conditions. However, choice of metric explains different degrees of variance in daily human physical activity.

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          Most cited references15

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          Physical activity of Canadian children and youth: accelerometer results from the 2007 to 2009 Canadian Health Measures Survey.

          Physical activity is an important determinant of health and fitness. This study provides contemporary estimates of the physical activity levels of Canadians aged 6 to 19 years. Data are from the 2007 to 2009 Canadian Health Measures Survey. The physical activity of a nationally representative sample was measured using accelerometers. Data are presented as time spent in sedentary, light, moderate and vigorous intensity movement, and in steps accumulated per day. An estimated 9% of boys and 4% of girls accumulate 60 minutes of moderate-to-vigorous physical activity on at least 6 days a week. Regardless of age group, boys are more active than girls. Canadian children and youth spend 8.6 hours per day-62% of their waking hours-in sedentary pursuits. Daily step counts average 12,100 for boys and 10,300 for girls. Based on objective and robust measures, physical activity levels of Canadian children and youth are low.
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            Quaternion-based extended Kalman filter for determining orientation by inertial and magnetic sensing.

            R Sabatini (2006)
            In this paper, a quaternion based extended Kalman filter (EKF) is developed for determining the orientation of a rigid body from the outputs of a sensor which is configured as the integration of a tri-axis gyro and an aiding system mechanized using a tri-axis accelerometer and a tri-axis magnetometer. The suggested applications are for studies in the field of human movement. In the proposed EKF, the quaternion associated with the body rotation is included in the state vector together with the bias of the aiding system sensors. Moreover, in addition to the in-line procedure of sensor bias compensation, the measurement noise covariance matrix is adapted, to guard against the effects which body motion and temporary magnetic disturbance may have on the reliability of measurements of gravity and earth's magnetic field, respectively. By computer simulations and experimental validation with human hand orientation motion signals, improvements in the accuracy of orientation estimates are demonstrated for the proposed EKF, as compared with filter implementations where either the in-line calibration procedure, the adaptive mechanism for weighting the measurements of the aiding system sensors, or both are not implemented.
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              Estimation of gait cycle characteristics by trunk accelerometry.

              This study reports on the novel use of a portable system to measure gait cycle parameters. Measurements were made by a triaxial accelerometer over the lower trunk during timed walking over a range of self-administered speeds. Signals from each trial were transformed to a horizontal-vertical coordinate system and analyzed by an unbiased autocorrelation procedure to obtain cadence, step length, and measures of gait regularity and symmetry. By curvilinear interpolation, speed-dependent gait parameters could be compared at a normalized speed. It was demonstrated that analysis of gait cycle parameters which previously required fixed laboratory equipment and paced walking procedures, now can be made from data obtained by a timing device and a portable sensor at free walking speeds.

                Author and article information

                Role: Editor
                PLoS One
                PLoS ONE
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                23 April 2013
                : 8
                : 4
                : e61691
                [1 ]Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Cambridge, United Kingdom
                [2 ]MoveLab, Institute of Cellular Medicine, Newcastle University, Newcastle Upon Tyne, United Kingdom
                [3 ]Institute for Medical Statistics and Epidemiology, Klinikum rechts der Isar der TU München, Munich, Germany
                [4 ]Fakultät für Informatik, TU München, Munich, Germany
                [5 ]Computer Laboratory, Cambridge University, Cambridge, United Kingdom
                [6 ]Department of Sport Medicine, Norwegian School of Sport Sciences, Oslo, Norway
                [7 ]Genetic Epidemiology and Clinical Research Group, Department of Public Health and Clinical Medicine, Section for Medicine, Umeå University Hospital, Umeå, Sweden
                [8 ]Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, Sweden
                Wageningen University, The Netherlands
                Author notes

                Competing Interests: Vincent van Hees, who led on this manuscript, was funded by a BBSRC industry-CASE studentship. This studentship came with funding from both the BBSRC and an industry partner, Unilever Discover Ltd in this case ( http://www.bbsrc.ac.uk/web/FILES/Guidelines/studentship_handbook.pdf). Unilever Discover Ltd had no involvement in the study as presented and was only informed about progress and final results. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials.

                Conceived and designed the experiments: VVH LG ECDL ME MP ST UE FR PWF AH SB. Performed the experiments: VVH LG ME ECDL FR. Analyzed the data: VVH. Contributed reagents/materials/analysis tools: VVH ECDL ME. Wrote the paper: VVH SB.

                Copyright @ 2013

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                : 19 December 2012
                : 12 March 2013
                Page count
                Pages: 10
                The Medical Research Council funded the study (MC_U106179473). VVH was funded by a BBSRC industry CASE studentship (Unilever, UK). The Doubly labelled water pat of the study was supported by Grants from LifeGene (Torsten and Ragnar Söderbergs Foundation), Fredrik and Ingrid Thurings Foundation, Umeå University Young Investigator’s Award, and the Västerbottens regional health authority (all grants to PWF). GENEA monitors in the Swedish study were loaned pro gratis from Unilever Discover Ltd. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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