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Related Concept Videos

Harmonic Mean01:09

Harmonic Mean

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The arithmetic mean is usually skewed towards the larger values in the data set. Therefore, to avoid this inherent bias towards smaller values, the harmonic mean is used.
Take the example of the speed of a car, which is the measure of the rate of distance traveled. If the vehicle traverses the same distance back-and-forth, its average speed equals the total distance traveled divided by the total time taken. However, if the car moves with varying speeds, then the arithmetic mean is more skewed...
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Mean From a Frequency Distribution01:11

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Sometimes, data gathered from an experiment on a large sample or population are organized into concise tables. In such cases, the frequency of the quantitative data set is plotted in the form of a table. Or else, the data values are grouped into the quantity’s intervals, which form classes, and their respective frequencies are known. That is, the data values are distributed over different categories or classes. This is known as frequency distribution.
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Discrete Fourier Transform01:15

Discrete Fourier Transform

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The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
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Correlation between ECG and Cardiac Cycle01:25

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The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
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Root Mean Square00:57

Root Mean Square

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If in an experiment, data values have a probability of being both positive and negative, neither the arithmetic mean, the geometric mean, nor the harmonic mean can be used to calculate the central tendency of the data set. In particular, if the positive and negative values are equally likely, the arithmetic mean is close to zero.
For example, consider the velocity of gas molecules in a container. The gas molecules are moving in different directions, which might impart positive and negative...
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Average Acceleration01:30

Average Acceleration

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The importance of understanding acceleration spans our day-to-day experiences, as well as the vast reaches of outer space and the tiny world of subatomic physics. In everyday conversation, to accelerate means to speed up. For instance, we are familiar with the acceleration of our car; the harder we apply our foot to the gas pedal, the faster we accelerate. The greater the acceleration, the greater the change in velocity over a given time. Acceleration is widely seen in experimental physics. In...
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  11. Comparison Of Mean Values And Entropy In Accelerometry Time Series From Two Microtechnology Sensors Recorded At 100 Vs. 1000 Hz During Cumulative Tackles In Young Elite Rugby League Players.
  1. Home
  3. Research Domains
  5. Information And Computing Sciences
  7. Distributed Computing And Systems Software
  9. Cyberphysical Systems And Internet Of Things
  11. Comparison Of Mean Values And Entropy In Accelerometry Time Series From Two Microtechnology Sensors Recorded At 100 Vs. 1000 Hz During Cumulative Tackles In Young Elite Rugby League Players.

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Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
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Comparison of Mean Values and Entropy in Accelerometry Time Series from Two Microtechnology Sensors Recorded at 100 vs. 1000 Hz During Cumulative Tackles in Young Elite Rugby League Players.

Bruno Fernández-Valdés1, Ben Jones2,3,4,5,6, Sharief Hendricks2,3

  • 1Research Group in Technology Applied to High Performance and Health, TecnoCampus, Department of Health Sciences, Universitat Pompeu Fabra, 08302 Barcelona, Spain.

Sensors (Basel, Switzerland)
|January 8, 2025

View abstract on PubMed

Summary
This summary is machine-generated.
Keywords:
frequencyrugbysport technologytackle

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Higher sampling rates capture more accurate motion data during explosive actions. A 1000 Hz accelerometer is suitable for analyzing rugby tackles, providing better insights into player performance and injury prevention.

Area of Science:

  • Sports Science
  • Biomechanics
  • Wearable Technology

Background:

  • Current Global Positioning System (GPS) devices for team sports typically sample at 5-15 Hz, which may be insufficient for capturing short, explosive actions like collisions.
  • Very high-frequency sampling is known to capture rapid changes in movement, crucial for analyzing high-impact events in sports.
  • Understanding the impact of sampling frequency on data accuracy is vital for optimizing performance analysis and injury risk assessment in elite athletes.

Purpose of the Study:

  • To compare mean acceleration and entropy values derived from 100 Hz and 1000 Hz tri-axial accelerometers during tackling actions in rugby players.
  • To evaluate the influence of sampling frequency on the quantification of external load and movement complexity in short, explosive athletic movements.

Main Methods:

  • Eleven elite adolescent male rugby league players participated, performing 200 tackling actions.
  • Two tri-axial accelerometers, sampling at 100 Hz and 1000 Hz, were worn simultaneously by each player.
  • Analysis focused on mean acceleration, sample entropy (SampEn), and approximate entropy (ApEn) to assess data differences between frequencies.

Main Results:

  • The 1000 Hz accelerometer recorded significantly higher mean acceleration values compared to the 100 Hz device (p < 0.05).
  • Sample entropy (SampEn) and approximate entropy (ApEn) were significantly higher when analyzed with the 100 Hz accelerometer (p < 0.05).
  • A strong correlation (R² > 0.5, p < 0.0001) was found between the two devices across all analyzed parameters, indicating good agreement despite frequency differences.

Conclusions:

  • Sampling frequency significantly impacts the data quality and metrics obtained from accelerometers in team sports analysis.
  • A 1000 Hz sampling frequency appears more suitable for accurately capturing the nuances of short, explosive actions like rugby tackles.
  • Higher sampling frequencies offer potential for more precise motion data collection, aiding in detailed performance and biomechanical analysis.