Skip to main content

Classifying the variability in impact and active peak vertical ground reaction forces during running using DFA and ARFIMA models

Winter, Samantha L., Challis, John H. (2017) Classifying the variability in impact and active peak vertical ground reaction forces during running using DFA and ARFIMA models. Human Movement Science, 51 . pp. 153-160. ISSN 0167-9457. E-ISSN 1872-7646. (doi:10.1016/j.humov.2016.12.003) (KAR id:60362)

XML Word Processing Document (DOCX) Author's Accepted Manuscript
Language: English

Restricted to Repository staff only
Contact us about this Publication
[thumbnail of acceptedManuscriptAuthorVersion.docx]
PDF Author's Accepted Manuscript
Language: English

Download (607kB) Preview
[thumbnail of Classifying AAM.pdf]
This file may not be suitable for users of assistive technology.
Request an accessible format
Official URL


The vertical ground reaction force (VGRF) during rear-foot striking running typically exhibits peaks referred to as the impact peak and the active peak; their timings and magnitudes have been implicated in injury. Identifying the structure of time-series can provide insight into associated control processes. The purpose here was to detect long-range correlations associated with the time from first contact to impact peak (TIP) and active peak (TAP); and the magnitudes of impact (IPM) and active peaks (APM) using a Detrended Fluctuation Analysis, and Auto-Regressive Fractionally Integrated Moving Average models. Twelve subjects performed an 8 minute trial at their preferred running speed on an instrumented treadmill. TIP, TAP; IPM, and APM were identified from the VGRF profile for each footfall. TIP and TAP time-series did not demonstrate long-range correlations, conversely IPM and APM time-series did. Short range correlations appeared as well as or instead of long range correlations for IPM. Conversely pure powerlaw behaviour was demonstrated in 11 of the 24 time series for APM, and long range dependencies along with short range correlations were present in a further 9 time series. It has been hypothesised that control mechanisms for IPM and APM are different, these results support this hypothesis.

Item Type: Article
DOI/Identification number: 10.1016/j.humov.2016.12.003
Uncontrolled keywords: Variability Measurement; Running; Motor Processes
Subjects: G Geography. Anthropology. Recreation > GV Recreation. Leisure > Sports sciences
Q Science > QP Physiology (Living systems)
Divisions: Divisions > Division of Natural Sciences > School of Sport and Exercise Sciences
Depositing User: Samantha Winter
Date Deposited: 27 Feb 2017 20:36 UTC
Last Modified: 16 Feb 2021 13:43 UTC
Resource URI: (The current URI for this page, for reference purposes)
Winter, Samantha L.:
  • Depositors only (login required):


Downloads per month over past year