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

Norris, Michelle; Kenny, Ian C.; Healy, Robin; Anderson, Ross

doi:10.1016/j.proeng.2016.06.249

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…Any application of the results of this study to outdoor running should be done with caution. However, systems that sequentially calculate the α during running from accelerometer data and provide real-time information have also been developed and verified [ 58 , 59 ]. Thus, although these limitations, this study demonstrated the possibility of non-invasive monitoring of the LT by real-time calculation of the α. Additionally, monitoring the α only from accelerometers could be applicable to overground running, not only in a laboratory environment.…”

Section: Discussionmentioning

confidence: 99%

Stride-to-stride variability and fluctuations at intensities around lactate threshold in distance runners

Watanabe

Kondo

Kakinoki³

et al. 2023

Heliyon

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Section: Discussionmentioning

confidence: 99%

Stride-to-stride variability and fluctuations at intensities around lactate threshold in distance runners

Watanabe

Kondo

Kakinoki³

et al. 2023

Heliyon

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“…Additionally, there is a lack of information in the literature regarding the potential use of IMU configurations for assessing running variability. For example, Norris et al [14,15] developed and then tested a new system based on an IMU positioned on the runner's tibia to estimate running variability in real-time. Recently, Hunter et al [16] used a couple of IMUs placed on each runner's foot to monitor DFA-α during an outdoor marathon.…”

Section: Plos Onementioning

confidence: 99%

“…Decreasing variability has been observed after a heavy period of training [12] and in runners previously affected by a running-related injury [13]. The idea of monitoring variability to flag impaired running patterns potentially prone to injuries is therefore very appealing, but counts only few actual attempts [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Indoor running temporal variability for different running speeds, treadmill inclinations, and three different estimation strategies

2023

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Inertial measurement units (IMU) constitute a light and cost-effective alternative to gold-standard measurement systems in the assessment of running temporal variables. IMU data collected on 20 runners running at different speeds (80, 90, 100, 110 and 120% of preferred running speed) and treadmill inclination (±2, ±5, and ±8%) were used here to predict the following temporal variables: stride frequency, duty factor, and two indices of running variability such as the detrended fluctuation analysis alpha (DFA-α) and the Higuchi’s D (HG-D). Three different estimation methodologies were compared: 1) a gold-standard optoelectronic device (which provided the reference values), 2) IMU placed on the runner’s feet, 3) a single IMU on the runner’s thorax used in conjunction with a machine learning algorithm with a short 2-second or a long 120-second window as input. A two-way ANOVA was used to test the presence of significant (p<0.05) differences due to the running condition or to the estimation methodology. The findings of this study suggest that using both IMU configurations for estimating stride frequency can be effective and comparable to the gold-standard. Additionally, the results indicate that the use of a single IMU on the thorax with a machine learning algorithm can lead to more accurate estimates of duty factor than the strategy of the IMU on the feet. However, caution should be exercised when using these techniques to measure running variability indices. Estimating DFA-α from a short 2-second time window was possible only in level running but not in downhill running and it could not accurately estimate HG-D across all running conditions. By taking a long 120-second window a machine learning algorithm could improve the accuracy in the estimation of DFA-α in all running conditions. By taking these factors into account, researchers and practitioners can make informed decisions about the use of IMU technology in measuring running biomechanics.

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Reliability of the fluctuations within the stride time series measured in runners during treadmill running to exhaustion

Chow

2019

Gait & Posture

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

Cited by 4 publications

References 10 publications

Stride-to-stride variability and fluctuations at intensities around lactate threshold in distance runners

Stride-to-stride variability and fluctuations at intensities around lactate threshold in distance runners

Indoor running temporal variability for different running speeds, treadmill inclinations, and three different estimation strategies

Reliability of the fluctuations within the stride time series measured in runners during treadmill running to exhaustion

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