Persistent fluctuations in stride intervals under fractal auditory stimulation

Copyright @ 2014 Marmelat et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Stride sequences of healthy gait are characterized by persistent long-range correlations, which become anti-persistent in the presence of an isochronous metronome. The latter phenomenon is of particular interest because auditory cueing is generally considered to reduce stride variability and may hence be beneficial for stabilizing gait. Complex systems tend to match their correlation structure when synchronizing. In gait training, can one capitalize on this tendency by using a fractal metronome rather than an isochronous one? We examined whether auditory cues with fractal variations in inter-beat intervals yield similar fractal inter-stride interval variability as isochronous auditory cueing in two complementary experiments. In Experiment 1, participants walked on a treadmill while being paced by either an isochronous or a fractal metronome with different variation strengths between beats in order to test whether participants managed to synchronize with a fractal metronome and to determine the necessary amount of variability for participants to switch from anti-persistent to persistent inter-stride intervals. Participants did synchronize with the metronome despite its fractal randomness. The corresponding coefficient of variation of inter-beat intervals was fixed in Experiment 2, in which participants walked on a treadmill while being paced by non-isochronous metronomes with different scaling exponents. As expected, inter-stride intervals showed persistent correlations similar to self-paced walking only when cueing contained persistent correlations. Our results open up a new window to optimize rhythmic auditory cueing for gait stabilization by integrating fractal fluctuations in the inter-beat intervals.Commission of the European Community and the Netherlands Organisation for Scientific Research

Data for: Fractal analysis of gait in people with Parkinson’s disease: three minutes is not enough

Supplementary data S1. Experimental stride time series. ‘StrideTimeIntervals_3minTrials’ is divided by groups, then within each group the 3-min trials are presented continuously for all participants (i.e., column 1 to 5 correspond to 3-min trials 1 to 5 from Subject 1; column 6 to 10 correspond to 3-min trials 1 to 5 from Subject 2; etc.). ‘StrideTimeIntervals_15minTrial’ is divided by groups, then within each group the 15-min trial time series (truncated to 512 strides) are found

Data for: Fractal analysis of gait in people with Parkinson’s disease: three minutes is not enough

Supplementary data S1. Experimental stride time series. ‘StrideTimeIntervals_3minTrials’ is divided by groups, then within each group the 3-min trials are presented continuously for all participants (i.e., column 1 to 5 correspond to 3-min trials 1 to 5 from Subject 1; column 6 to 10 correspond to 3-min trials 1 to 5 from Subject 2; etc.). ‘StrideTimeIntervals_15minTrial’ is divided by groups, then within each group the 15-min trial time series (truncated to 512 strides) are found.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Barcoding, linear and nonlinear analysis of full-day leg movements in infants with typical development and infants at risk of developmental disabilities: Cross-sectional study

: Traditional methods do not capture the multidimensional domains and dynamic nature of infant behavioral patterns. We aim to compare full-day, in-home leg movement data between infants with typical development (TD) and infants at risk of developmental disabilities (AR) using barcoding and nonlinear analysis. Eleven infants with TD (2-10 months) and nine infants AR (adjusted age: 2-14 months) wore a sensor on each ankle for 7 days. We calculated the standard deviation for linear variability and sample entropy (SampEn) of leg acceleration and angular velocity for nonlinear variability. Movements were also categorized into 16 barcoding states, and we calculated the SampEn and proportions of the barcoding. All variables were compared between the two groups using independent-samples t-test or Mann-Whitney U test. The AR group had larger linear variability compared to the TD group. SampEn was lower in the AR group compared to TD group for both acceleration and angular velocity. Two barcoding states' proportions were significantly different between the two groups. The results showed that nonlinear analysis and barcoding could be used to identify the difference of dynamic multidimensional movement patterns between infants AR and infants with TD. This information may help early diagnosis of developmental disabilities in the future