Effect of sampling frequency on fractal fluctuations during treadmill walking
Autoři:
Vivien Marmelat aff001; Austin Duncan aff001; Shane Meltz aff001
Působiště autorů:
Department of Biomechanics, University of Nebraska at Omaha, Omaha, Nebraska, United States of America
aff001
Vyšlo v časopise:
PLoS ONE 14(11)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0218908
Souhrn
The temporal dynamics of stride-to-stride fluctuations in steady-state walking reveal important information about locomotor control and can be quantified using so-called fractal analyses, notably the detrended fluctuation analysis (DFA). Gait dynamics are often collected during treadmill walking using 3-D motion capture to identify gait events from kinematic data. The sampling frequency of motion capture systems may impact the precision of event detection and consequently impact the quantification of stride-to-stride variability. This study aimed i) to determine if collecting multiple walking trials with different sampling frequency affects DFA values of spatiotemporal parameters during treadmill walking, and ii) to determine the reliability of DFA values across downsampled conditions. Seventeen healthy young adults walked on a treadmill while their gait dynamics was captured using different sampling frequency (60, 120 and 240 Hz) in each condition. We also compared data from the highest sampling frequency to downsampled versions of itself. We applied DFA to the following time series: step length, time and speed, and stride length, time and speed. Reliability between experimental conditions and between downsampled conditions were measured with 1) intraclass correlation estimates and their 95% confident intervals, calculated based on a single-measurement, absolute-agreement, two-way mixed-effects model (ICC 3,1), and 2) Bland-Altman bias and limits of agreement. Both analyses revealed a poor reliability of DFA results between conditions using different sampling frequencies, but a relatively good reliability between original and downsampled spatiotemporal variables. Collectively, our results suggest that using sampling frequencies of 120 Hz or 240 Hz provide similar results, but that using 60 Hz may alter DFA values. We recommend that gait kinematics should be collected at around 120 Hz, which provides a compromise between event detection accuracy and processing time.
Klíčová slova:
Biological locomotion – Fractals – Gait analysis – Kinematics – Research validity – Toes – Walking – Young adults
Zdroje
1. Delignières D, Torre K. Fractal dynamics of human gait: a reassessment of the 1996 data of Hausdorff et al. J Appl Physiol. 2009;106(4): 1272–1279. doi: 10.1152/japplphysiol.90757.2008 19228991
2. Dingwell JB, Cusumano JP. Re-interpreting detrended fluctuation analyses of stride-to-stride variability in human walking. Gait Posture. 2010;32: 348–353. doi: 10.1016/j.gaitpost.2010.06.004 20605097
3. Dingwell JB, Cusumano JP. Identifying stride-to-stride control strategies in human treadmill walking. PLoS ONE. 2015; 10(4): e0124879. doi: 10.1371/journal.pone.0124879 25910253
4. Hausdorff JM, Peng CK, Ladin Z, Wei JY, Goldberger AL. Is walking a random walk? Evidence for long-range correlations in the stride interval of human gait. J Appl Physiol. 1995;78: 349–358. doi: 10.1152/jappl.1995.78.1.349 7713836
5. Rock CG, Marmelat V, Yentes J, Sui KC, Takahashi KZ. Interaction between step-to-step variability and metabolic cost of transport during human walking. J Exp Biol. 2018;221: jeb181834. doi: 10.1242/jeb.181834 30237239
6. Roerdink M, Daffertshofer A, Marmelat V, Beek PJ. How to sync to the beat of a persistent fractal metronome without falling off the treadmill? PLoS ONE. 2015;10(7): e0134148. doi: 10.1371/journal.pone.0134148 26230254
7. Hausdorff JM, Mitchell SL, Firtion R, Peng CK, Cudkowicz ME, Wei JY, Goldberger, AL. Altered fractal dynamics of gait: reduced stride-interval correlations with aging and Huntington’s disease. J Appl Physiol. 1997;82: 262–269. doi: 10.1152/jappl.1997.82.1.262 9029225
8. Warlop T, Detrembleur C, Bollens B, Stoquart G, Crevecoeur F, Jeanjean A, Lejeune TM. Temporal organization of stride duration variability as a marker of gait instability in Parkinson’s disease. J Rehab Med. 2016;48: 865–871.
9. Marmelat V, Torre K, Beek PJ, Daffertshofer A. Persistent fluctuations in stride intervals under fractal auditory stimulation. PLoS ONE. 2014;9(3): e91949. doi: 10.1371/journal.pone.0091949 24651455
10. Terrier P. Fractal fluctuations in human walking: comparison between auditory and visually guided stepping. Ann Biomed Eng. 2016;44: 2785–93. doi: 10.1007/s10439-016-1573-y 26903091
11. Rhea CK, Kiefer AW, Wittstein MW, Leonard KB, MacPherson RP, Wright WG, Haran FJ. Fractal gait patterns are retained after entrainment to a fractal stimulus. PLoS ONE. 2014; 9(9), e106755. doi: 10.1371/journal.pone.0106755 25221981
12. Rhea CK, Kiefer AW, D’Andrea SE, Warren WH, Aaron RK. Entrainment to a real time fractal visual stimulus modulates fractal gait dynamics. Hum Mov Sci. 2014; 36, 20–34. doi: 10.1016/j.humov.2014.04.006 24911782
13. Wittstein MW, Starobin JM, Schmitz RJ, Shulz SJ, Haran FJ, Rhea CK. Cardiac and gait rhythms in healthy younger and older adults during treadmill walking tasks. Aging Clin Exp Res. 2019; 31(3), 367–375. doi: 10.1007/s40520-018-0962-5 29777477
14. Peng CK, Mietus J, Hausdorff JM, Havlin S, Stanley HE, Goldberger AL. Long-range anticorrelations and non-Gaussian behavior of the heartbeat. Phys Rev Lett. 1993;70: 1343–1346. doi: 10.1103/PhysRevLett.70.1343 10054352
15. Chen Z, Ivanov PC, Hu K, Stanley HE. Effect of nonstationarities on detrended fluctuation analysis. Phys Rev E. 2002;65(4): 041107.
16. Delignières D, Ramdani S, Lemoine L, Torre K, Fortes M, Ninot G. Fractal analysis for short time series: a reassessement of classical methods. J Math Psychol. 2006;50: 525–544.
17. Marmelat V, Delignieres D. Complexity, coordination, and health: avoiding pitfalls and erroneous interpretations in fractal analyses. Medicina (Kaunas). 2011;47:393–398.
18. Marmelat V, Meidinger RL. Fractal analysis of gait in people with Parkinson’s disease: three minutes is not enough. Gait Posture. 2019;70:229–234. doi: 10.1016/j.gaitpost.2019.02.023 30909002
19. Marmelat V, Reynolds NR, Hellman A. Gait dynamics in Parkinson’s disease: short gait trials “stitched” together provide different fractal fluctuations compared to longer trials. Front Physiol. 2018;9:861. doi: 10.3389/fphys.2018.00861 30038582
20. Warlop T, Bollens B, Detrembleur C, Stoquart G, Lejeune T, Crevecoeur F. Impact of series length on statistical precision and sensitivity of autocorrelation assessment in human locomotion. Hum Mov Sci. 2018;55: 31–42.
21. Damouras S, Chang MD, Sejdić E, Chau T. An empirical examination of detrended fluctuation analysis for gait data. Gait Posture. 2010; 31: 336–340. doi: 10.1016/j.gaitpost.2009.12.002 20060298
22. Liddy JJ, Haddad JM. Evenly spaced Detrended Fluctuation Analysis: Selecting the number of points for the diffusion plot. Physica A. 2018; 491: 233–248.
23. Almurad ZMH., Delignières D. Evenly spacing in Detrended Fluctuation Analysis. Physica A. 2016;451: 63–69.
24. Liddy JJ, Ducharme SW, van Emmerick REA, Haddad JM. Temporal correlations in human locomotion: Recommendations for sampling rate and foot strike detection. Physica A. 2019; 532: 121784.
25. Rhea CK, Kiefer AW, Wright WG, Raisbeck LD, Haran FJ. Interpretation of postural control may change due to data processing techniques. Gait Posture. 2015;41(2):731–735. doi: 10.1016/j.gaitpost.2015.01.008 25737236
26. Wijnants ML, Cox RFA, Hasselman F, Bosman AMT, Van Orden G. Does sample rate introduce an artifact in spectral analysis of continuous processes? Front Physio. 2013; 3:495. doi: 10.3389/fphys.2012.00495 23346058
27. Zeni JA, Richards JG, Higginson JS. Two simple methods for determining gait events during treadmill and overground walking using kinematic data. Gait Posture. 2008;27(4): 710–714. doi: 10.1016/j.gaitpost.2007.07.007 17723303
28. Cicchetti DV. Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychol Assess. 1994;6(4): 284.
29. Koo TK, Li MY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropract Med. 2016;15(2): 155–163.
30. Altman DG, Bland JM. Measurement in medicine: the analysis of method comparison studies. Statistician. 1983; 32:307–17. doi: 10.2307/2987937
31. Giavarina D. Understanding Bland Altman analysis. Biochem Med (Zagreb). 2015;25(2): 141–151.
32. Kuznetsov NA, Rhea CK. Power considerations for the application of detrended fluctuation analysis in gait variability studies. PLoS ONE. 2017;12(3): e0174144. doi: 10.1371/journal.pone.0174144 28323871
33. Pierrynowski MR, Gross A, Miles M, Galea V, McLaughlin L, McPhee C. Reliability of the long-range power-law correlations obtained from the bilateral stride intervals in asymptomatic volunteers whilst treadmill walking. Gait Posture. 2005;22: 46–50. doi: 10.1016/j.gaitpost.2004.06.007 15996591
34. Warlop T, Detrembleur C, Stoquart G, Jeanjean A. Gait complexity and regularity are differently modulated by treadmill walking in Parkinson’s disease and healthy population. Front Physiol. 2018;9:68. doi: 10.3389/fphys.2018.00068 29467673
35. Choi S, Kang DW, Seo JW, Tack GR. Reliability of the walking speed and gait dynamics variables while walking on a feedback-controlled treadmill. J Biomech. 2015;48: 1336–1339. doi: 10.1016/j.jbiomech.2015.02.047 25798762
36. Wiens C, Denton W, Schieber MN, Harley R, Marmelat V, Myers SA, Yentes JM. Walking speed and spatiotemporal step mean measures are reliable during feedback-controlled treadmill walking; however, spatiotemporal step variability is not reliable. J Biomech. 2019;83(23), 221–226.
37. Walter SD, Eliasziw M, Donner A. Sample size and optimal designs for reliability studies. Stats Med. 1998;17, 101–110.
38. Raffalt PC, McCamley J, Denton W, Yentes JM. Sampling frequency influences sample entropy of kinematics during walking. Med Biol Eng Comput. 2018; https://doi.org/10.1007/s11517-018-1920-2.
Článek vyšel v časopise
PLOS One
2019 Číslo 11
- Jak a kdy u celiakie začíná reakce na lepek? Možnou odpověď poodkryla čerstvá kanadská studie
- Pomůže v budoucnu s triáží na pohotovostech umělá inteligence?
- Spermie, vajíčka a mozky – „jednohubky“ z výzkumu 2024/38
- Metamizol jako analgetikum první volby: kdy, pro koho, jak a proč?
- Infekce se v Americe po příjezdu Kolumba šířily nesrovnatelně déle, než se traduje
Nejčtenější v tomto čísle
- A daily diary study on maladaptive daydreaming, mind wandering, and sleep disturbances: Examining within-person and between-persons relations
- A 3’ UTR SNP rs885863, a cis-eQTL for the circadian gene VIPR2 and lincRNA 689, is associated with opioid addiction
- A substitution mutation in a conserved domain of mammalian acetate-dependent acetyl CoA synthetase 2 results in destabilized protein and impaired HIF-2 signaling
- Molecular validation of clinical Pantoea isolates identified by MALDI-TOF
Zvyšte si kvalifikaci online z pohodlí domova
Všechny kurzy