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Optimising Instrumented Mouthguard Data Analysis: Video Synchronisation Using a Cross-correlation Approach.

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Summary
This summary is machine-generated.

Two computational methods were developed to synchronize instrumented mouthguard (iMG) data with video recordings, enabling efficient analysis of head acceleration events (HAEs) in rugby. These methods expedite HAE analysis, improving feedback and research capabilities.

Keywords:
Brain injuryHead accelerationInstrumented mouthguardsVideo analysis

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Area of Science:

  • Sports Medicine
  • Biomechanics
  • Data Science

Background:

  • Head acceleration events (HAEs) are a significant concern in contact sports like rugby.
  • Instrumented mouthguards (iMGs) are mandated by rugby governing bodies, generating large datasets.
  • Analyzing iMG data presents financial and time constraints for researchers and analysts.

Purpose of the Study:

  • To present two computational methods for synchronizing iMG data with video-coded match events.
  • To enable efficient location of HAEs in video footage using cross-correlation synchronization.
  • To identify specific match events associated with HAEs for probability calculations using post-synchronization event matching.

Main Methods:

  • Cross-correlation synchronization to align iMG data with video playback timestamps.
  • Post-synchronization event matching to link HAEs to coded match events (e.g., tackles, ball carries).
  • Accuracy and validity assessed through video verification across 60 rugby matches, using synchronisation error, PPV, and sensitivity.

Main Results:

  • Cross-correlation synchronization achieved mean errors of 0.61-0.71 seconds, with all matches synchronized within 3 seconds.
  • Post-synchronization event matching demonstrated high diagnostic accuracy with PPVs of 0.90-0.95 and sensitivity of 0.99-1.00.
  • Both methods confirmed high accuracy and validity when utilizing video-coded match event datasets.

Conclusions:

  • The developed computational methods effectively synchronize iMG data with video analysis.
  • Implementation is feasible, especially when leveraging commercial video-coded match event datasets.
  • These methods can expedite iMG analysis, enhance feedback timeliness, and support further research in sports injury prevention.