Possibilities for real time DFA based injury detection and skill level differentiation

Michelle Norris; I. C. Kenny; Healy R. Anderson

doi:10.1016/j.proeng.2016.06.249

Possibilities for real time DFA based injury detection and skill level differentiation

Michelle Norris, I. C. Kenny, Healy R. Anderson

Research output: Contribution to journal › Article › peer-review

110 Downloads (Pure)

Abstract

Detrended fluctuation analysis (DFA) is applied to running stride time series for the detection of long range correlations. However, there is no concurrent real-time data collection and DFA feedback mechanism. We aimed to verify a running analysis system for production of real-time DFA α values. Data were collected utilising an accelerometer, attached to the tibia. The accelerometer data were transmitted to MATLAB for processing. Results demonstrated DFA α value output < 2 seconds post individual running Epochs, and < 5 seconds post run completion. Our running analysis system provides rapid advanced variability information, important in both training, and injury prevention.

Original language	English
Pages (from-to)	700-705
Journal	Procedia Engineering
Volume	147
Early online date	9 Jul 2016
DOIs	https://doi.org/10.1016/j.proeng.2016.06.249
Publication status	Published - 9 Jul 2016

Access to Document

10.1016/j.proeng.2016.06.249

Possibilities for real timeFinal published version, 298 KBLicence: CC BY-NC-ND

Cite this

@article{9c22ee8b3e12408792e19834b1cb7f6c,

title = "Possibilities for real time DFA based injury detection and skill level differentiation",

abstract = "Detrended fluctuation analysis (DFA) is applied to running stride time series for the detection of long range correlations. However, there is no concurrent real-time data collection and DFA feedback mechanism. We aimed to verify a running analysis system for production of real-time DFA α values. Data were collected utilising an accelerometer, attached to the tibia. The accelerometer data were transmitted to MATLAB for processing. Results demonstrated DFA α value output < 2 seconds post individual running Epochs, and < 5 seconds post run completion. Our running analysis system provides rapid advanced variability information, important in both training, and injury prevention.",

author = "Michelle Norris and Kenny, {I. C.} and Anderson, {Healy R.}",

year = "2016",

month = jul,

day = "9",

doi = "10.1016/j.proeng.2016.06.249",

language = "English",

volume = "147",

pages = "700--705",

journal = "Procedia Engineering",

issn = "1877-7058",

publisher = "Elsevier BV",

}

TY - JOUR

T1 - Possibilities for real time DFA based injury detection and skill level differentiation

AU - Norris, Michelle

AU - Kenny, I. C.

AU - Anderson, Healy R.

PY - 2016/7/9

Y1 - 2016/7/9

N2 - Detrended fluctuation analysis (DFA) is applied to running stride time series for the detection of long range correlations. However, there is no concurrent real-time data collection and DFA feedback mechanism. We aimed to verify a running analysis system for production of real-time DFA α values. Data were collected utilising an accelerometer, attached to the tibia. The accelerometer data were transmitted to MATLAB for processing. Results demonstrated DFA α value output < 2 seconds post individual running Epochs, and < 5 seconds post run completion. Our running analysis system provides rapid advanced variability information, important in both training, and injury prevention.

AB - Detrended fluctuation analysis (DFA) is applied to running stride time series for the detection of long range correlations. However, there is no concurrent real-time data collection and DFA feedback mechanism. We aimed to verify a running analysis system for production of real-time DFA α values. Data were collected utilising an accelerometer, attached to the tibia. The accelerometer data were transmitted to MATLAB for processing. Results demonstrated DFA α value output < 2 seconds post individual running Epochs, and < 5 seconds post run completion. Our running analysis system provides rapid advanced variability information, important in both training, and injury prevention.

U2 - 10.1016/j.proeng.2016.06.249

DO - 10.1016/j.proeng.2016.06.249

M3 - Article

SN - 1877-7058

VL - 147

SP - 700

EP - 705

JO - Procedia Engineering

JF - Procedia Engineering

ER -

Possibilities for real time DFA based injury detection and skill level differentiation

Abstract

Access to Document

Fingerprint

Cite this