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Purpose <p>Head acceleration events (HAEs) are a growing concern in contact sports, prompting two rugby governing bodies to mandate instrumented mouthguards (iMGs). This has resulted in an influx of data imposing financial and time constraints. This study presents two computational methods that leverage a dataset of video-coded match events: <i>cross-correlation synchronisation</i> aligns iMG data to a video recording, by providing playback timestamps for each HAE, enabling analysts to locate them in video footage; and <i>post-synchronisation event matching</i> identifies the coded match event (e.g. tackles and ball carries) from a video analysis dataset for each HAE, this process is important for calculating the probability of match events resulting in HAEs. Given the professional context of iMGs in rugby, utilising commercial sources of coded match event datasets may expedite iMG analysis.</p> Methods <p>Accuracy and validity of the methods were assessed via video verification during 60 rugby matches. The accuracy of <i>cross-correlation synchronisation</i> was determined by calculating synchronisation error, whilst the validity of <i>post-synchronisation event matching</i> was evaluated using diagnostic accuracy measures (e.g. positive predictive value [PPV] and sensitivity).</p> Results <p><i>Cross-correlation synchronisation</i> yielded mean synchronisation errors of 0.61–0.71&#xa0;s, with all matches synchronised within 3&#xa0;s’ error. <i>Post-synchronisation event matching</i> achieved PPVs of 0.90–0.95 and sensitivity of 0.99–1.00 for identifying correct match events for SAEs.</p> Conclusion <p>Both methods achieved high accuracy and validity with the data sources used in this study. Implementation depends on the availability of a dataset of video-coded match events; however, integrating commercially available video-coded datasets offers the potential to expedite iMG analysis, improve feedback timeliness, and augment research analysis.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月21日

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  • James Tooby,
  • Steve Rowson,
  • Kevin Till,
  • David Allan,
  • Melanie Dawn Bussey,
  • Dario Cazzola,
  • Éanna Falvey,
  • Kenzie Friesen,
  • Andrew J. Gardner,
  • Cameron Owen,
  • Gregory Roe,
  • Thomas Sawczuk,
  • Lindsay Starling,
  • Keith Stokes,
  • Gregory Tierney,
  • Ross Tucker,
  • Ben Jones

摘要

Purpose

Head acceleration events (HAEs) are a growing concern in contact sports, prompting two rugby governing bodies to mandate instrumented mouthguards (iMGs). This has resulted in an influx of data imposing financial and time constraints. This study presents two computational methods that leverage a dataset of video-coded match events: cross-correlation synchronisation aligns iMG data to a video recording, by providing playback timestamps for each HAE, enabling analysts to locate them in video footage; and post-synchronisation event matching identifies the coded match event (e.g. tackles and ball carries) from a video analysis dataset for each HAE, this process is important for calculating the probability of match events resulting in HAEs. Given the professional context of iMGs in rugby, utilising commercial sources of coded match event datasets may expedite iMG analysis.

Methods

Accuracy and validity of the methods were assessed via video verification during 60 rugby matches. The accuracy of cross-correlation synchronisation was determined by calculating synchronisation error, whilst the validity of post-synchronisation event matching was evaluated using diagnostic accuracy measures (e.g. positive predictive value [PPV] and sensitivity).

Results

Cross-correlation synchronisation yielded mean synchronisation errors of 0.61–0.71 s, with all matches synchronised within 3 s’ error. Post-synchronisation event matching achieved PPVs of 0.90–0.95 and sensitivity of 0.99–1.00 for identifying correct match events for SAEs.

Conclusion

Both methods achieved high accuracy and validity with the data sources used in this study. Implementation depends on the availability of a dataset of video-coded match events; however, integrating commercially available video-coded datasets offers the potential to expedite iMG analysis, improve feedback timeliness, and augment research analysis.