Determining the interactions between postural variability structure and discomfort development using nonlinear analysis techniques during prolonged standing work

Nonlinear analysis techniques provide a powerful approach to explore dynamics of posture-related time-varying signals. The aim of this study was to investigate the fundamental interactions between postural variability structure and discomfort development during prolonged standing. Twenty participants, with equal distribution for gender and standing work experience (SWE), completed a simulated long-term standing test. Low back and legs discomfort, center of pressure, lumbar curvature, and EMG activity of trunk and leg muscles were monitored. Nonlinear measures including largest lyapunov exponent, multi-scale entropy, and detrended fluctuation analysis were applied to characterize the variability structure (i.e., complexity) in each signal. The size (i.e., amount) of variability was also computed using traditional linear metrics. With progress of low back and legs discomfort over standing periods, significant lower levels were perceived by the participants having SWE. The amount of variability in all signals (except external oblique EMG activity) were significantly increased with the time progress for all participants. The structure of variability in most signals demonstrated a lower complexity (more regularity) with fractal properties that deviated from 1/f noise. The SWE group showed a higher complexity levels. Overall, the findings verified variations in structure and amount of the postural variability. However, nonlinear analysis identified postural strategies according to the perceived discomfort in a different way. These results provide supports for future application of nonlinear tools in evaluating standing tasks and related ergonomics interventions as it allows further insight into how discomfort development impact the structure of postural changes. •Low back and legs discomfort ratings increased during standing time.•Greater amount of postural variability was found with progress of time.•Postural variability structure indicated lower complexity over standing periods.•Findings from nonlinear metrics were more consistent with the different levels of discomfort.