Abstract
Postural control is commonly assessed by quantifying center of pressure (CoP) variability during quiet stance. CoP data is traditionally filtered prior to analysis. However, some researchers suggest filtering may lead to undesirable consequences. Further, sampling frequency may also affect CoP analysis, as filtering CoP signals of different sampling frequencies may influence variability metrics. This study examined the influence of sampling frequency and filtering on metrics that index the magnitude and structure of variability in CoP displacement and velocity. Healthy adults (N=8, 27.4±2.6 years) balanced on their right foot for 60s on a force plate. CoP data recorded at 100Hz was then downsampled and/or filtered (2nd order dual-pass 10Hz low-pass Butterworth) to create six different CoP time series for each participant: (1) original, (2) filtered, (3) downsampled to 50Hz, (4) downsampled to 25Hz, (5) downsampled to 50Hz and filtered, and (6) down-sampled to 25Hz and filtered. Data were then analyzed using four common variability metrics (standard deviation [SD], root mean square [RMS], detrended fluctuation analysis α [DFA α], and sample entropy [SampEn]). Data processing techniques did not influence the magnitude of variability (SD and RMS), but did influence the structure of variability (DFA α and SampEn) in CoP displacement. All metrics were influenced by data processing techniques in CoP velocity. Thus, when interpreting changes in CoP variability, one must be careful to identify how much change is driven by the neuromotor system and how much is a function of data processing technique.
Highlights
Upright stance is inherently unstable because two-thirds of the body's mass is located in the head/arms/trunk, creating an inverted pendulum effect [1]
Two seconds of each 60 s time series are presented in Fig. 1 (CoP displacement) and Fig. 2 (CoP velocity) to show the qualitatively different characteristics in each time series
Three main themes were observed across the data processing techniques: (1) center of pressure (CoP) velocity metrics are more affected than CoP displacement metrics, (2) structure of variability metrics are more sensitive than magnitude of variability metrics, and (3) filtering the data produced the largest differences in the structure of variability metrics of the CoP velocity time series
Summary
Upright stance is inherently unstable because two-thirds of the body's mass is located in the head/arms/trunk, creating an inverted pendulum effect [1]. Prior to a variability analysis, traditional signal processing guidelines for human movement data, including postural control data, recommend that a signal is filtered to remove any artifacts unassociated with neuromotor control [8]. It is possible that filtering the signal may remove parts of the signal (both deterministic and random) that are rooted in the postural control process [3], [10]. While variability in CoP displacement is a commonly measured postural control variable, it has been suggested that CoP velocity is the variable attended to by the neuromotor system to maintain upright stance [11], [12]. This study examined whether different sampling frequencies and/or filtering affect the magnitude and structure of variability in CoP displacement and velocity signals
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