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

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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

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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.",

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AU - Anderson, Healy R.

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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.

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JO - Procedia Engineering

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

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