scholarly journals Complexity of human walking: the attractor complexity index is sensitive to gait synchronization with visual and auditory cues

2019 ◽  
Author(s):  
Philippe Terrier
Keyword(s):  
Long Range ◽  
Center Of Pressure ◽  
Sensorimotor Synchronization ◽  
Fluctuation Analysis ◽  
Scaling Exponent ◽  
Stepping Stones ◽  
Complexity Index ◽  
Gait Parameters ◽  
Fractal Patterns ◽  
Logistic Regressions

Background. During steady walking, gait parameters fluctuate from one stride to another with complex fractal patterns and long-range statistical persistence. When a metronome is used to pace the gait (sensorimotor synchronization), long-range persistence is replaced by stochastic oscillations (anti-persistence). Fractal patterns present in gait fluctuations are most often analyzed using detrended fluctuation analysis (DFA). This method requires the use of a discrete times series, such as intervals between consecutive heel strikes, as an input. Recently, a new nonlinear method, the attractor complexity index (ACI), has been shown to respond to complexity changes like DFA. But in contrast to DFA, ACI can be applied to continuous signals, such as body accelerations. The aim of this study was to further compare DFA and ACI in a treadmill experiment that induced complexity changes through sensorimotor synchronization. Methods. Thirty-six healthy adults walked 30 minutes on an instrumented treadmill under three conditions: no cueing, auditory cueing (metronome walking), and visual cueing (stepping stones). The center-of-pressure trajectory was discretized into time series of gait parameters, after which a complexity index (scaling exponent alpha) was computed via DFA. Continuous pressure position signals were used to compute the ACI. Correlations between ACI and DFA were then analyzed. The predictive ability of DFA and ACI to differentiate between cueing and no-cueing conditions was assessed using regularized logistic regressions and areas under the receiver operating characteristic curves (AUROC). Results. DFA and ACI were both significantly different among the cueing conditions. DFA and ACI were correlated (Pearson’s r = 0.78). Logistic regressions showed that DFA and ACI could differentiate between cueing/no cueing conditions with a high degree of confidence (AUROC = 1.0 and 0.96, respectively). Conclusion. Both DFA and ACI responded similarly to changes in cueing conditions and had comparable predictive power. This support the assumption that ACI could be used instead of DFA to assess the long-range complexity of continuous gait signals.

scholarly journals Complexity of human walking: the attractor complexity index is sensitive to gait synchronization with visual and auditory cues

2019 ◽  
Author(s):  
Philippe Terrier
Keyword(s):  
Long Range ◽  
Center Of Pressure ◽  
Sensorimotor Synchronization ◽  
Fluctuation Analysis ◽  
Scaling Exponent ◽  
Stepping Stones ◽  
Complexity Index ◽  
Gait Parameters ◽  
Fractal Patterns ◽  
Logistic Regressions

Background. During steady walking, gait parameters fluctuate from one stride to another with complex fractal patterns and long-range statistical persistence. When a metronome is used to pace the gait (sensorimotor synchronization), long-range persistence is replaced by stochastic oscillations (anti-persistence). Fractal patterns present in gait fluctuations are most often analyzed using detrended fluctuation analysis (DFA). This method requires the use of a discrete times series, such as intervals between consecutive heel strikes, as an input. Recently, a new nonlinear method, the attractor complexity index (ACI), has been shown to respond to complexity changes like DFA. But in contrast to DFA, ACI can be applied to continuous signals, such as body accelerations. The aim of this study was to further compare DFA and ACI in a treadmill experiment that induced complexity changes through sensorimotor synchronization. Methods. Thirty-six healthy adults walked 30 minutes on an instrumented treadmill under three conditions: no cueing, auditory cueing (metronome walking), and visual cueing (stepping stones). The center-of-pressure trajectory was discretized into time series of gait parameters, after which a complexity index (scaling exponent alpha) was computed via DFA. Continuous pressure position signals were used to compute the ACI. Correlations between ACI and DFA were then analyzed. The predictive ability of DFA and ACI to differentiate between cueing and no-cueing conditions was assessed using regularized logistic regressions and areas under the receiver operating characteristic curves (AUROC). Results. DFA and ACI were both significantly different among the cueing conditions. DFA and ACI were correlated (Pearson’s r = 0.78). Logistic regressions showed that DFA and ACI could differentiate between cueing/no cueing conditions with a high degree of confidence (AUROC = 1.0 and 0.96, respectively). Conclusion. Both DFA and ACI responded similarly to changes in cueing conditions and had comparable predictive power. This support the assumption that ACI could be used instead of DFA to assess the long-range complexity of continuous gait signals.

scholarly journals Complexity of human walking: the attractor complexity index is sensitive to gait synchronization with visual and auditory cues

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7417 ◽  
Author(s):  
Philippe Terrier
Keyword(s):  
Long Range ◽  
Complexity Analysis ◽  
Center Of Pressure ◽  
Sensorimotor Synchronization ◽  
Fluctuation Analysis ◽  
Scaling Exponent ◽  
Complexity Index ◽  
Gait Parameters ◽  
Fractal Patterns ◽  
Logistic Regressions

Background During steady walking, gait parameters fluctuate from one stride to another with complex fractal patterns and long-range statistical persistence. When a metronome is used to pace the gait (sensorimotor synchronization), long-range persistence is replaced by stochastic oscillations (anti-persistence). Fractal patterns present in gait fluctuations are most often analyzed using detrended fluctuation analysis (DFA). This method requires the use of a discrete times series, such as intervals between consecutive heel strikes, as an input. Recently, a new nonlinear method, the attractor complexity index (ACI), has been shown to respond to complexity changes like DFA, while being computed from continuous signals without preliminary discretization. Its use would facilitate complexity analysis from a larger variety of gait measures, such as body accelerations. The aim of this study was to further compare DFA and ACI in a treadmill experiment that induced complexity changes through sensorimotor synchronization. Methods Thirty-six healthy adults walked 30 min on an instrumented treadmill under three conditions: no cueing, auditory cueing (metronome walking), and visual cueing (stepping stones). The center-of-pressure trajectory was discretized into time series of gait parameters, after which a complexity index (scaling exponent alpha) was computed via DFA. Continuous pressure position signals were used to compute the ACI. Correlations between ACI and DFA were then analyzed. The predictive ability of DFA and ACI to differentiate between cueing and no-cueing conditions was assessed using regularized logistic regressions and areas under the receiver operating characteristic curves (AUC). Results DFA and ACI were both significantly different among the cueing conditions. DFA and ACI were correlated (Pearson’s r = 0.86). Logistic regressions showed that DFA and ACI could differentiate between cueing/no cueing conditions with a high degree of confidence (AUC = 1.00 and 0.97, respectively). Conclusion Both DFA and ACI responded similarly to changes in cueing conditions and had comparable predictive power. This support the assumption that ACI could be used instead of DFA to assess the long-range complexity of continuous gait signals. However, future studies are needed to investigate the theoretical relationship between DFA and ACI.

scholarly journals Early Warning Signals for Bearing Failure Using Detrended Fluctuation Analysis

2020 ◽  
Vol 10 (23) ◽  
pp. 8489
Author(s):  
Laith Shalalfeh ◽  
Ashraf AlShalalfeh
Keyword(s):  
Long Range ◽  
Early Warning ◽  
Detrended Fluctuation Analysis ◽  
Short Range ◽  
Fluctuation Analysis ◽  
Scaling Exponent ◽  
Bearing Failure ◽  
Kendall’S Tau ◽  
Vibration Data ◽  
Detrended Fluctuation

Prognostic techniques play a critical role in predicting upcoming faults and failures in machinery or a system by monitoring any deviation in the operation. This paper presents a novel method to analyze multidimensional sensory data and use its characteristics in bearing health prognostics. Firstly, detrended fluctuation analysis (DFA) is exploited to evaluate the long-range correlations in ball bearing vibration data. The results reveal the existence of the crossover phenomenon in vibration data with two scaling exponents at the short-range and long-range scales. Among several data sets, applying the DFA method to vibration signals shows a consistent increase in the short-range scaling exponent toward bearing failure. Finally, Kendall’s tau is used as a ranking coefficient to quantify the trend in the scaling exponent. It was found that the Kendall’s tau coefficient of the vibration scaling exponent could provide an early warning signal (EWS) for bearing failure.

Long-range dependence in Indian stock market: a study of Indian sectoral indices

2014 ◽  
Vol 9 (4) ◽  
pp. 505-519 ◽  
Author(s):  
Dilip Kumar
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Frequency Domain ◽  
Long Range ◽  
Time Domain ◽  
Long Range Dependence ◽  
Fluctuation Analysis ◽  
Scaling Exponent ◽  
Simulation Experiments ◽  
Content Type

Purpose – The purpose of this paper is to test the efficient market hypothesis for major Indian sectoral indices by means of long memory approach in both time domain and frequency domain. This paper also tests the accuracy of the detrended fluctuation analysis (DFA) approach and the local Whittle (LW) approach by means of Monte Carlo simulation experiments. Design/methodology/approach – The author applies the DFA approach for the computation of the scaling exponent in the time domain. The robustness of the results is tested by the computation of the scaling exponent in the frequency domain by means of the LW estimator. The author applies moving sub-sample approach on DFA to study the evolution of market efficiency in Indian sectoral indices. Findings – The Monte Carlo simulation experiments indicate that the DFA approach and the LW approach provides good estimates of the scaling exponent as the sample size increases. The author also finds that the efficiency characteristics of Indian sectoral indices and their stages of development are dynamic in nature. Originality/value – This paper has both methodological and empirical originality. On the methodological side, the author tests the small sample properties of the DFA and the LW approaches by using simulated series of fractional Gaussian noise and find that both the approach possesses superior properties in terms of capturing the scaling behavior of asset prices. On the empirical side, the author studies the evolution of long-range dependence characteristics in Indian sectoral indices.

scholarly journals Long-range memory in internal and forced dynamics of millennium-long climate model simulations

2014 ◽  
Vol 5 (2) ◽  
pp. 295-308 ◽  
Author(s):  
L. Østvand ◽  
T. Nilsen ◽  
K. Rypdal ◽  
D. Divine ◽  
M. Rypdal
Keyword(s):  
Long Range ◽  
General Circulation ◽  
Climate Model ◽  
General Circulation Models ◽  
Climate System ◽  
Fluctuation Analysis ◽  
Scaling Exponent ◽  
External Forcing ◽  
Internal Dynamics ◽  
Temperature Records

Abstract. Northern Hemisphere (NH) temperature records from a palaeoclimate reconstruction and a number of millennium-long climate model experiments are investigated for long-range memory (LRM). The models are two Earth system models and two atmosphere–ocean general circulation models. The periodogram, detrended fluctuation analysis and wavelet variance analysis are applied to examine scaling properties and to estimate a scaling exponent of the temperature records. A simple linear model for the climate response to external forcing is also applied to the reconstruction and the forced climate model runs, and then compared to unforced control runs to extract the LRM generated by internal dynamics of the climate system. The climate models show strong persistent scaling with power spectral densities of the form S(f) ~ f −β with 0.8 < β < 1 on timescales from years to several centuries. This is somewhat stronger persistence than found in the reconstruction (β &amp;approx; 0.7). We find no indication that LRM found in these model runs is induced by external forcing, which suggests that LRM on sub-decadal to century time scales in NH mean temperatures is a property of the internal dynamics of the climate system. Reconstructed and instrumental sea surface temperature records for a local site, Reykjanes Ridge, are also studied, showing that strong persistence is found also for local ocean temperature.

scholarly journals IMPACT OF TANDEM REPEATS ON THE SCALING OF NUCLEOTIDE SEQUENCES

2006 ◽  
Vol 16 (10) ◽  
pp. 3103-3108
Author(s):  
RADHAKRISHNAN NAGARAJAN ◽  
MEENAKSHI UPRETI
Keyword(s):  
Long Range ◽  
Detrended Fluctuation Analysis ◽  
Tandem Repeats ◽  
Nucleotide Sequences ◽  
Fluctuation Analysis ◽  
Scaling Exponent ◽  
Long Range Correlations ◽  
Reliable Estimation ◽  
Detrended Fluctuation

Techniques such as detrended fluctuation analysis (DFA) and its extensions have been widely used to determine the nature of scaling in nucleotide sequences. In this brief communication we show that tandem repeats which are ubiquitous in nucleotide sequences can prevent reliable estimation of possible long-range correlations. Therefore, it is important to investigate the presence of tandem repeats prior to scaling exponent estimation.

scholarly journals Influence of Autocorrelated Rhythmic Auditory Stimulations on Parkinson’s Disease Gait Variability: Comparison With Other Auditory Rhythm Variabilities and Perspectives

2020 ◽  
Vol 11 ◽  
Author(s):  
Alexis Lheureux ◽  
Thibault Warlop ◽  
Charline Cambier ◽  
Baptiste Chemin ◽  
Gaëtan Stoquart ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Long Range ◽  
Coefficient Of Variation ◽  
Gait Speed ◽  
Step Length ◽  
Temporal Organization ◽  
Fluctuation Analysis ◽  
Gait Parameters ◽  
Stride Duration

Parkinson’s Disease patients suffer from gait impairments such as reduced gait speed, shortened step length, and deterioration of the temporal organization of stride duration variability (i.e., breakdown in Long-Range Autocorrelations). The aim of this study was to compare the effects on Parkinson’s Disease patients’ gait of three Rhythmic Auditory Stimulations (RAS), each structured with a different rhythm variability (isochronous, random, and autocorrelated). Nine Parkinson’s Disease patients performed four walking conditions of 10–15 min each: Control Condition (CC), Isochronous RAS (IRAS), Random RAS (RRAS), and Autocorrelated RAS (ARAS). Accelerometers were used to assess gait speed, cadence, step length, temporal organization (i.e., Long-Range Autocorrelations computation), and magnitude (i.e., coefficient of variation) of stride duration variability on 512 gait cycles. Long-Range Autocorrelations were assessed using the evenly spaced averaged Detrended Fluctuation Analysis (α-DFA exponent). Spatiotemporal gait parameters and coefficient of variation were not modified by the RAS. Long-Range Autocorrelations were present in all patients during CC and ARAS although all RAS conditions altered them. The α-DFA exponents were significantly lower during IRAS and RRAS than during CC, exhibiting anti-correlations during IRAS in seven patients. α-DFA during ARAS was the closest to the α-DFA during CC and within normative data of healthy subjects. In conclusion, Isochronous RAS modify patients’ Long-Range Autocorrelations and the use of Autocorrelated RAS allows to maintain an acceptable level of Long-Range Autocorrelations for Parkinson’s Disease patients’ gait.

scholarly journals Use of Virtual Reality to Assess Dynamic Posturography and Sensory Organization: Instrument Validation Study (Preprint)

2020 ◽  
Author(s):  
Matthew William Wittstein ◽  
Anthony Crider ◽  
Samantha Mastrocola ◽  
Mariana Guerena Gonzalez
Keyword(s):  
Virtual Reality ◽  
Center Of Pressure ◽  
Force Plate ◽  
Fluctuation Analysis ◽  
Scaling Exponent ◽  
Sensory Organization Test ◽  
Dynamic Posturography ◽  
Sensory Organization ◽  
Intraclass Correlations ◽  
Absolute Agreement

BACKGROUND The Equitest system (Neurocom) is a computerized dynamic posturography device used by health care providers and clinical researchers to safely test an individual’s postural control. While the Equitest system has evaluative and rehabilitative value, it may be limited owing to its cost, lack of portability, and reliance on only sagittal plane movements. Virtual reality (VR) provides an opportunity to reduce these limitations by providing more mobile and cost-effective tools while also observing a wider array of postural characteristics. OBJECTIVE This study aimed to test the plausibility of using VR as a feasible alternative to the Equitest system for conducting a sensory organization test. METHODS A convenience sample of 20 college-aged healthy individuals participated in the study. Participants completed the sensory organization test using the Equitest system as well as using a VR environment while standing atop a force plate (Bertec Inc). The Equitest system measures the equilibrium index. During VR trials, the estimated equilibrium index, 95% ellipse area, path length, and anterior-posterior detrended fluctuation analysis scaling exponent alpha were calculated from center of pressure data. Pearson correlation coefficients were used to assess the relationship between the equilibrium index and center of pressure–derived balance measures. Intraclass correlations for absolute agreement and consistency were calculated to compare the equilibrium index and estimated equilibrium index. RESULTS Intraclass correlations demonstrated moderate consistency and absolute agreement (0.5 &lt; intraclass correlation coefficient &lt; 0.75) between the equilibrium index and estimated equilibrium index from the Equitest and VR sensory organization test (SOT), respectively, in four of six tested conditions. Additionally, weak to moderate correlations between force plate measurements and the equilibrium index were noted in several of the conditions. CONCLUSIONS This research demonstrated the plausibility of using VR as an alternative method to conduct the SOT. Ongoing development and testing of virtual environments are necessary before employing the technology as a replacement to current clinical tests.

scholarly journals Long-range memory in millennium-long ESM and AOGCM experiments

2014 ◽  
Vol 5 (1) ◽  
pp. 363-401 ◽  
Author(s):  
L. Østvand ◽  
T. Nilsen ◽  
K. Rypdal ◽  
D. Divine ◽  
M. Rypdal
Keyword(s):  
Long Range ◽  
Time Scales ◽  
General Circulation ◽  
Climate Model ◽  
Climate System ◽  
Fluctuation Analysis ◽  
Scaling Exponent ◽  
External Forcing ◽  
Internal Dynamics ◽  
Temperature Records

Abstract. Northern Hemisphere (NH) temperature records from a reconstruction and a number of millennium-long climate model experiments are investigated for long-range memory (LRM). The models are two Earth system models and two atmospheric-ocean general circulation models. The periodogram, detrended fluctuation analysis and wavelet variance analysis are applied to examine scaling properties and to estimate a scaling exponent of the temperature records. A simple linear model for the climate response to external forcing is also applied to the reconstruction and the forced climate model runs, and then compared to unforced control runs to extract the LRM generated by internal dynamics of the climate system. With one exception the climate models show strong persistent scaling with power spectral densities of the form S(f) ~ f−β with 0.8 < β < 1 on time scales from years to several centuries. This is somewhat stronger persistence than found in the reconstruction (β ≈ 0.7). The exception is the HadCM3 model, which exhibits β ≈ 0.6. We find no indication that LRM found in these model runs are induced by external forcing, which suggests that LRM on sub-decadal to century time scales in NH mean temperatures is a property of the internal dynamics of the climate system. Temperature records for a local site, Reykjanes Ridge, are also studied, showing that strong persistence is found also for local ocean temperature.

Does instructing attentional focus direction affect static single leg balance performance?

2021 ◽  
pp. 174795412110585
Author(s):  
Cory J Monahan ◽  
Wendy L Hurley
Keyword(s):  
Postural Control ◽  
Motor Skills ◽  
Center Of Pressure ◽  
Attentional Focus ◽  
Fluctuation Analysis ◽  
Scaling Exponent ◽  
Balance Performance ◽  
External Focus ◽  
Balance Task ◽  
And Control

Balance and postural control exercises are generally included as a part of exercise programs, during which movement practitioners can provide instructions to facilitate the performance of motor skills. Instructions can be used as cues to direct attentional focus, which has been found to affect the performance of motor skills, including balance and postural control tasks. However, no known studies to date have investigated the effect of both internal and external attentional focus instructions on static single leg balance performance, and it seems unclear whether effects of such instructions are related specifically to the direction of attention. The purpose of this study was to investigate the effect of instructing the direction of attentional focus on single leg static balance performance as reflected by the complexity of the center of pressure (COP) profile. Participants (N = 46) between the ages of 19–28 years old were randomly assigned to one of three group conditions: internal focus (INTn=15), external focus (EXTn=16) and control (CONn=15). Participants performed a thirty-five second static single leg balance task. Outcome measures were the scaling exponent determined from a detrended fluctuation analysis (DFA) to infer complexity of the COP profile in the anterior-posterior (AP) and medial-lateral (ML) directions, and root mean square error (RMSE) of the COP profile in AP and ML directions. A one-way analysis of variance (ANOVA) determined there were no statistically significant differences in the measured variables among groups. The results do not support the claim that manipulating the direction of attentional focus affects static single leg balance performance.

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