New paper!📰 This study details two computational methods leveraging commercial video analysis data that have been central for: - Synchronise HAEs to video footage - Quantify HAE risk from rugby match events - Rapidly generate iMG reports for teams

📉How can we reduce HAE exposure in rugby league and rugby union? ❓Why is probability so much higher in rugby union? 📈How can we monitor and manage players with elevated HAE exposure? https://t.co/8W3YV8cgfj

Despite these lower findings on average, some players exhibit elevated values 🧠If these are persisted over multiple matches and seasons, these players may be at an increased risk of neurological effects

During a tackle event, players were less likely to record HAEs in rugby league than rugby union. Probability of an HAE exceeding 25 g during a tackle event: 💪Tacklers: 4.26% league vs. 18.90% union 🏉Ball-carriers: 6.29% league vs. 13.90% union

Incidence was higher in our previous rugby union research than this study. Number of HAEs exceeding a peak linear acceleration of 25 g per player match: ⏩Forwards: 2.16 league vs. 5.42 union ⏪Backs: 1.51 league vs. 3.92 union

We recorded 775 player matches across 91 players from all @SuperLeague teams in the 2023 season using @PreventBio iMGs: 🏉10,000 tackle involvements measured 💢17,077 HAEs recorded and analysed

✏️New paper! Head acceleration event (HAE) exposure in professional men’s rugby league: 📉Fewer HAEs per player match in rugby league compared to union 📉HAEs less likely in rugby league tackles compared to union 📈Individuals with elevated HAE values 🔓 https://t.co/8W3YV8cgfj

Here is the link!

Speaking to the publishers to get the Supplementary Materials added to the website!

These methods continue to be used in rugby research and practice, and may also be implemented in different sports. All source code is available in the Supplementary Materials of the paper!

It is important to note that these methods rely on the availability of a dataset of video-coded match events, however, they have also been effective with another dataset since this paper was written!

With our datasets, this process was also very effective; the PPV for identifying the correct event was > 0.9 for both rugby union and rugby league!

Post-synchronisation event matching (catchy, I know!) simply aligns each SAE to the coded match event which we think caused it, based on their newly aligned timestamps. For example, if we have a dataset of coded rugby tackles, we can identify which one caused each SAE.

Using our datasets, this process was very reliable and had reasonably good accuracy!

Cross-correlation synchronisation takes a dataset of potential head impacts (PHI) and a dataset of sensor acceleration events (SAEs) to determine the synchronisation point that aligns the most together. This allows us to identify the SAEs in video footage.

📢New paper by @xianghao_zhan et al.! We used an AI model to eliminate some of the noise measured by instrumented mouthguards: "peak kinematics after denoising were more accurate" Such models will help improve the quality of our head impact datasets! 👀 https://t.co/x3eV2Mg6E9

A fantastic read 🤓👏🏼

A great piece to understand limitations of head impact sensors: 🗝️Triggering mechanisms and processing algorithms are far from perfect. 🗝️Head impact measures are estimations of true exposure and should always be interpreted with caution. More thoughts below [1/?]...

Therefore, hopefully this can serve as a useful resource for all iMG stakeholders, including researchers, readers of research, practitioners, journalists, etc., etc. Here is the link to read the full article again: https://t.co/RbLcwcqmUf🔓 [13/13]

To conclude, this article was written to highlight technical constraints of iMGs and considerations of iMG data with the goal of improving the interpretation of iMG data within research, practice, and the media. [12/13]