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REVIEW article

Front. Bioeng. Biotechnol.

Sec. Biomechanics

Volume 13 - 2025 | doi: 10.3389/fbioe.2025.1658222

This article is part of the Research TopicEnhancing Sports Injury Management through Medical-Engineering InnovationsView all 18 articles

Quantitative Video Analysis of Head Acceleration Events: A Review

Provisionally accepted
Thomas  AstonThomas Aston*Filipe  Teixeira-DiasFilipe Teixeira-Dias
  • University of Edinburgh, Edinburgh, United Kingdom

The final, formatted version of the article will be published soon.

The biomechanics of head acceleration events (HAEs) in sport have received increasing attention due to growing concern over concussion and long-term neurodegenerative disease risk. While wearable sensors, such as instrumented mouthguards (iMGs), are now commonly used to measure HAEs, these devices face well-documented challenges, including poor skull coupling, limited compliance, and high false-positive rates. Video footage is routinely collected in sports for performance analysis, and is a perhaps underutilised source for both retrospective and in situ measurement surrounding HAEs. Traditionally used to confirm HAE exposure in wearable sensor studies, video has more recently been explored as a quantitative tool in its own right. This review synthesises the current state of the art in video-based measurement of HAEs, with a particular focus on videogrammetric methods, including manual point tracking and model-based image matching. Recent advances in computer vision and deep learning that offer the potential to automate and extend these approaches are also examined. Key limitations of current video-based methods are discussed, alongside opportunities to improve their scalability, accuracy, and biomechanical insight. By consolidating evidence across traditional and emerging approaches, this review highlights the potential of video as a practical and valuable measurement source for quantatative measurement and modelling of HAEs in sport.

Keywords: Head Acceleration Events, Contact sports, Videogrammetry, Computer Vision, Sports Medicine, concussion, neurodegenerative disease

Received: 02 Jul 2025; Accepted: 07 Aug 2025.

Copyright: © 2025 Aston and Teixeira-Dias. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Thomas Aston, University of Edinburgh, Edinburgh, United Kingdom

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