Films Full BluRay | Trails To Tsukiji S04E11 Cabbage 1080p HDTV x264-DARKFLiX 27 minutes | Play Movie

Assessment of differenced center of pressure time series improves detection of age-related changes in postural coordination

Assessment of differenced center of pressure time series improves detection of age-related changes in postural coordination

Gait & Posture 38 (2013) 345–348 Contents lists available at SciVerse ScienceDirect Gait & Posture journal homepage: www.elsevier.com/locate/gaitpos...

676KB Sizes 0 Downloads 2 Views

Recommend Documents

Multivariate changes in coordination of postural control following spaceflight
Postural and gait instabilities in astronauts returning from spaceflight are thought to result from in-flight adaptation

Aging and the complexity of center of pressure in static and dynamic postural tasks
•We investigated the complexity of COP as a function of age and postural task.•The COP complexity was lower for the old

Change point detection in heteroscedastic time series
Many time series exhibit changes both in level and in variability. Generally, it is more important to detect a change in

Recurrence and symmetry of time series: Application to transition detection
•A new theoretical framework based on the symmetry concept is proposed.•Four types of symmetry present in any time serie

Detection of glaciers displacement time-series using SAR
•An extension of the PO-SBAS technique for computing displacement time-series•Faster glaciers can move tens to hundreds

Direct detection of nonlinear modal interactions from time series measurements
•We present a new method for estimating frequency-energy plots from measured data.•The FEPs allow for the identification

On-line motif detection in time series with SwiftMotif
This article presents SwiftMotif, a novel technique for on-line motif detection in time series. With this technique, fre

Gait & Posture 38 (2013) 345–348

Contents lists available at SciVerse ScienceDirect

Gait & Posture journal homepage: www.elsevier.com/locate/gaitpost

Short communication

Assessment of differenced center of pressure time series improves detection of age-related changes in postural coordination Adam J. Strang a,*, Angela DiDomenico b, William P. Berg c, Raymond W. McGorry b a

Consortium Research Fellows Program, 4214 King St., Alexandria, VA 22302, United States Liberty Mutual Research Institute for Safety, 71 Frankland Road, Hopkinton, MA 01748, United States c Department of Kinesiology and Health, 106 Phillips Hall, 420 S. Oak Street, Oxford, OH 45056, United States b

A R T I C L E I N F O

A B S T R A C T

Article history: Received 27 August 2012 Received in revised form 18 October 2012 Accepted 6 November 2012

Center of Pressure (CoP) time series exhibit non-stationarity. Most CoP analyses assume a stationary signal, which could lead to measurement inaccuracy. Despite this, few researchers have reported the incidence of CoP non-stationarity or employed procedures to mitigate non-stationarity prior to timeseries analysis. Differencing is a pre-processing technique that reduces non-stationarity, though it has only recently been used with CoP data. This study sought to report the incidence of CoP non-stationarity in a sample data set and determine whether differencing mitigated any CoP non-stationarity that was detected. In addition, researchers examined whether analysis of differenced CoP improved the ability to detect age-related changes in postural coordination. ß 2012 Elsevier B.V. All rights reserved.

Keywords: Center of Pressure Postural coordination Non-stationarity Differencing Time series

1. Introduction Center of Pressure (CoP) time-series analysis is the prevailing method for examining human postural coordination in stance [1,2]. It is known that CoP time series exhibit non-stationarity (i.e., unstable mean and variance over time), which can negatively affect the sensitivity and accuracy of time-series measures, since most assume a stationary signal [3,4]. Few studies have investigated the incidence of non-stationarity in CoP data [3–5]. Newell et al. [4] performed the most rigorous assessment in a study that recorded CoP in stance from participants aged 3–92 years. Using an Analysis of Variance approach, researchers determined that all of the CoP time-series examined in that study contained significant time-domain non-stationarity. A few studies have employed a pre-processing technique known as differencing to mitigate CoP non-stationarity [2,5,6]. Differencing refers to deriving a new time series from the sampled data according to xi+1  xi, where xi is the ith sampled data point. Advantages of differencing over other detrending techniques (e.g., high-pass filter, empirical mode decomposition, polynomial line fit) include computational simplicity, effectiveness [7], and the rendering of transformed data that retains face and content

* Corresponding author. Tel.: +1 513 505 6855. E-mail addresses: [email protected] (A.J. Strang), [email protected] (A. DiDomenico), [email protected] (W.P. Berg), [email protected] (R.W. McGorry). 0966-6362/$ – see front matter ß 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.gaitpost.2012.11.006

validity. Face validity is attributed to the sampling rate of CoP being fixed, which makes differencing equivalent to obtaining the first-order derivative of CoP position (CoPpos), i.e., CoP instantaneous velocity (CoPvel). Content validity comes from research indicating that postural sway velocity, as opposed to position and acceleration, is the most accurate and relied-upon kinesthetic information used in maintaining human balance [8]. A known disadvantage of differencing is noise amplification [9], which can result in increased time-domain variance and a shift in spectral power toward higher frequencies. In support of differencing, Ramdani et al. [5] showed that this technique was necessary for detecting differences in CoP between eyes-open and eyes-closed stance using Sample Entropy (SEn), a nonlinear measure that assumes stationarity. In another study, Rhea et al. [2] examined the influence of noise and sampling frequency on the accuracy and reliability of three entropy measures, i.e., Approximate Entropy, SEn, and Recurrence Quantification Analysis Entropy, and determined that estimates for all three measures were more robust to noise and increasing sampling rates when derived from differenced CoP. The purpose of the current experiment was to report the incidence of CoP non-stationarity in a sample data set and determine whether differencing mitigated CoP non-stationarity that was detected using an objective inferential method. This study also sought to examine whether analysis of differenced CoP improved the ability to detect age-related changes in postural coordination.

A.J. Strang et al. / Gait & Posture 38 (2013) 345–348

346 2. Methods 2.1. Participants

Forty-five men between the ages of 18 and 65 years, who were free of musculoskeletal and cardiovascular pathology, participated. Mean (SD) age, height and body mass of participants were 40.5 (14.8) years, 1.77 (0.07) m and 83.6 (14.1) kg. 2.2. Apparatus

CoPRD and CoPRV were subjected to similar measures: Rectified Mean Amplitude (MA), Mean Spectral Frequency (MF), SEn [10], and Detrended Fluctuation Analysis (DFA) [11]. Prior to applications of SEn, CoPRD and CoPRV were converted to Z-scores. CoPRD (M = 4, r = .04) and CoPRV (M = 4, r = .12) SEn parameters were established separately, mimicking the methodological use [6] of an optimization procedure [5]. DFA scaling exponents (a) were estimated using window ranges of 5–20 for CoPRD and CoPRV, respectively, following visual inspection indicating that long-range correlations across that range fit a linear trend for both data types.

Anterior–posterior (AP) and medial-lateral (ML) CoP were sampled at 100 Hz from two force plates (Model #9286AA, Kistler Instruments AG, Winterthur, Switzerland).

3. Results and discussion

2.3. Procedure

3.1. Determining the incidence of CoPRD non-stationarity and whether differencing mitigated these effects

Participants performed three trials (each lasting 60 s) in a single laboratory session where they were asked to stand ‘‘as still as possible’’ with feet placed shoulder width apart near the center of adjacent force plates. Participants stood with arms at their sides and directed their gaze on an eye-level target located 7 m away. All participants wore athletic shoes (Nike Bandolier II) provided by experimenters. 2.4. Data analysis The first 1000 CoP data points were cropped. A single corrupt trial was removed from analysis. The remaining CoP time-series were mean-centered and subjected to a 12.5 Hz 2nd order Butterworth low-pass filter. From the filtered data, 95% Elliptical Area (EA) [1] and two-dimensional (2D) Path Length (PL) [1] were derived. EA and PL do not lend themselves to differencing, since assumptions of stationarity are irrelevant for 2D metrics, but were included due to their popularity. Centered AP and ML CoP were converted to Resultant Vector Distance (CoPRD) qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi time series according to AP2i þ ML2i to eliminate bias in uni-planar CoP, since planar axes are referenced to the force plate, not the participant [1,6]. CoPRD were differenced and divided by the common sampling period (0.01 s), returning a set of CoP resultant velocity (CoPRV) time series (Fig. 1).

(a) 12

CoPRD (mm)

10 8 6

4 2 0

0

10

20

30

40

50

30

40

50

Time (s) (b) 60

CoPRV (mm/s)

40 20 0 -20 -40 -60

0

10

20

Time (s) Fig. 1. Representative CoPRD (a) and CoPRV (b) time series from a 36-year-old participant in this experiment.

The Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test examines the null hypothesis that a time series is stationary along a deterministic (stationary) trend [12]. This test was chosen over others (e.g., qualitative assessment of the auto-correlation function [5], Dickey–Fuller test) because of its popularity [13], easy interpretation, and quick calculation using free software [14]. Independent KPSS tests (a = .01) were applied to all CoPRD and CoPRV. Results revealed that 69 of 134 (51%) CoPRD contained significant non-stationarity. None of the CoPRV was shown to exhibit significant non-stationarity. This indicates that differencing eliminated non-stationarity in all cases. 3.2. Examining whether assessments of differenced CoP improved detection and delineation of age-related changes in postural stability Pearson-product-moment correlations (r; a = .05) comprised of CoP measures and Age were used to address this issue. Fig. 2 reveals that increase in Age was associated with a decrease in SEn and increase in DFA for assessments of both CoPRD and CoPRV. In addition, increase in Age was associated with a decrease in MF for assessment of CoPRV. No relationships with Age were detected for EA, PL, or MA. These findings indicate that increase in Age was associated with decreases in the fractal complexity (DFA) and speed (MF) of CoP trajectories, accompanied by an increase in CoP temporal regularity (SEn). However, no changes in CoP area (EA), amount (PL), or dispersion (MA) were associated with an increase in Age. Findings from DFA and SEn are consistent with previous findings examining age-related changes in postural coordination that employed fractal [15] and entropy measures [16] and may indicate a more attention-demanding [6] and/or more rigid [15] postural coordination with increase in Age. To determine whether correlation strengths observed for SEn and DFA for assessments of CoPRV were statistically stronger than that detected for CoPRD, step-wise regressions (a = .05) were used. In these analyses, SEn and DFA from CoPRD assessments were set as first-order predictors of Age, while estimates from CoPRV assessments served as second-order predictors. The impetus was to determine whether the amount of variability (r2) accounted for by the second-order models was greater than that of the first-order models. If so, this would imply that assessments of differenced CoP improved the ability to account for age-related changes in postural coordination for these measures. For SEn, the second-order model exhibited improvement, F-change (1, 42) = 6.31, p = .02, r2 change = .113, while the second-order model for DFA did not, F-change (1, 42) = .49, p = .07, r2 change = .061. In considering these findings along with that indicating a significant MF-Age relationship when CoPRV, but not CoPRD, was assessed, it appears that differencing slightly improved the ability to detect age-related changes in postural coordination.

A.J. Strang et al. / Gait & Posture 38 (2013) 345–348

347

Fig. 2. Scatterplots depicting relationships between CoP measures and Age. 2D measures plots (EA and PL) are presented using open triangles (a and b), CoPRD measures with open circles (c, e, g, and i), and CoPRV measures with crosses (d, f, h, and j). Pearson product-moment correlations (r) for each relationship are provided with asterisks (*) denoting significant relationship at r-crit two-tailed, df = 43, a = .05 = .294.

348

A.J. Strang et al. / Gait & Posture 38 (2013) 345–348

Conflict of interest statement The authors state that they have no conflict of interest related to this work. References [1] Prieto TE, Myklebust JB, Hoffmann RG, Lovett EG, Myklebust BM. Measures of postural steadiness: differences between healthy young and elderly adults. IEEE Transactions on Biomedical Engineering 1996;43(9):956–66. [2] Rhea CK, Silver TA, Lee Hong S, Hyun Ryu J, Studenka BE, Hughes CML, et al. Noise and complexity in human postural control: interpreting the different estimations of entropy. PLoS ONE 2011;6(3):e17696. http://dx.doi.org/ 10.1371/journal.pone.0017696. [3] Carroll JP, Freedman W. Nonstationary properties of postural sway. Journal of Biomechanics 1993;26(4–5):409–16. [4] Newell KM, Slobounov SM, Solbounova BS, Molenaar PCM. Short-term nonstationarity and the development of postural control. Gait and Posture 1997;6:56–62. [5] Ramdani S, Seigle B, Lagarde J, Bouchara F, Bernard PL. On the use of sample entropy to analyze human postural sway data. Medical Engineering and Physics 2009;31:1023–31. [6] Roerdink M, Hlavackova P, Vuillerme N. Center-of-pressure regularity as a marker for attentional investment in postural control: a comparison between sitting and standing postures. Human Movement Science 2011;30:203–12.

[7] Chatfield C. The Analysis of Time Series: An Introduction, 6th ed., Boca Raton: CRC Press; 2004. [8] Jeka J, Kiemel T, Creath R, Horak F, Peterka R. Controlling human upright posture: velocity information is more accurate than position or acceleration. Journal of Neurophysiology 2004;29:2368–79. [9] Kantz H, Schreiber T. Nonlinear Time Series Analysis, 2nd ed., Cambridge: Cambridge University Press; 2004. [10] Richman JS, Moorman JR. Physiological time-series analysis using approximate and sample entropy. American Journal of Physiology – Heart And Circulatory Physiology 2000;278:H2039–49. [11] Peng CK, Havlin S, Stanley HE, Goldberger AL. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 1995;5(1):82–7. [12] Kwiatkowski D, Phillips PCB, Schmidt P, Shin Y. Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics 1992;54:159–78. [13] Hadri K, Rao Y. KPSS test and model misspecifications. Applied Economics Letters 2009;16:1187–90. [14] Seth AK. A matlab toolbox for granger causal connectivity analysis. Journal of Neuroscience 2010;186:262–73. [15] Thurner S, Mittermaier C, Ehrenberger K. Change of complexity patterns in human posture during aging. Audiology and Neuro-Otology 2002;7: 20–248. [16] Vaillancourt DE, Newell KM. Changing complexity in human behavior and physiology through aging and disease. Neurobiology of Aging 2002;23: 1–11.