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The percentage of time spent in static trunk posture was calculated by dividing the time spent in static trunk posture by the total time of postural data collection of the whole working day.
RGB-D sensors can collect postural data in an automatized way.
Analysis of postural data was performed on trials without a consecutive fall.
To nullify the effect of the anthropometric factors on the inverse pendulum model of postural control [28], all postural data were normalised with respect to body height.
After applying the homogeneity test, the postural data were subjected to a mixed ANOVA design with the viewing distance as main factor with two levels (40 cm and 200 cm), and one inter-subject factor with three levels (nonspecific chronic LBP, HSVH, HSVO).
Postural data were recorded using a seesaw force plate (techno concept®, Posturwin software, version P3-03).
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Because of the high dimensionality of the postural analysis data and the correlations between the parameters, a data reduction technique (specifically, factor analysis with six factors extracted) was used as an input for nonhierarchical k means clustering.
Acceleration of the hand and fingers, forearm EMG activity and postural sway data were recorded.
We are grateful to Jennifer Forsyth, Colleen Merrill, Ashley Steffen, and Sam Kaiser for their assistance in collecting postural sway data.
Postural sway data were submitted to conventional quantitative analyses of the magnitude of sway area using the center of pressure measurement.
Posturography (postural control) data were recorded by the EVaRT 5.0 software (Motion Analysis Corporation) and we used a routine developed and processed in the Matlab 2008a software (Mathworks) to process the data.
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