Possibilities for Real-time DFA Based Injury Detection and Skill Level Differentiation

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

doi:10.1016/j.proeng.2016.06.249

Possibilities for Real-time DFA Based Injury Detection and Skill Level Differentiation

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

University of Limerick

Research output: Contribution to journal › Conference article › peer-review

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
Number of pages	6
Journal	Procedia Engineering
Volume	147
DOIs	https://doi.org/10.1016/j.proeng.2016.06.249
Publication status	Published - 2016
Event	11th conference of the International Sports Engineering Association, ISEA 2016 - Delft, Netherlands Duration: 11 Jul 2016 → 14 Jul 2016

Keywords

Feedback
gait
locomotion
stride
variability

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10.1016/j.proeng.2016.06.249

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@article{9f0029a16d3e44ddab24ea7d16e5af7c,

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.",

keywords = "Feedback, gait, locomotion, stride, variability",

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

note = "Publisher Copyright: {\textcopyright} 2016 The Authors. Published by Elsevier Ltd.; 11th conference of the International Sports Engineering Association, ISEA 2016 ; Conference date: 11-07-2016 Through 14-07-2016",

year = "2016",

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

language = "English",

volume = "147",

pages = "700--705",

journal = "Procedia Engineering",

issn = "1877-7058",

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TY - JOUR

T1 - Possibilities for Real-time DFA Based Injury Detection and Skill Level Differentiation

AU - Norris, M.

AU - Kenny, I. C.

AU - Healy, R.

AU - Anderson, R.

PY - 2016

Y1 - 2016

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.

KW - Feedback

KW - gait

KW - locomotion

KW - stride

KW - variability

UR - https://www.scopus.com/pages/publications/84982958112

U2 - 10.1016/j.proeng.2016.06.249

DO - 10.1016/j.proeng.2016.06.249

M3 - Conference article

AN - SCOPUS:84982958112

SN - 1877-7058

VL - 147

SP - 700

EP - 705

JO - Procedia Engineering

JF - Procedia Engineering

T2 - 11th conference of the International Sports Engineering Association, ISEA 2016

Y2 - 11 July 2016 through 14 July 2016

ER -

Possibilities for Real-time DFA Based Injury Detection and Skill Level Differentiation

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Keywords

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