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Original research
Can a novel computer vision-based framework detect head-on-head impacts during a rugby league tackle?
  1. Manish Mohan1,
  2. Dan Weaving1,2,3,
  3. Andrew J Gardner1,4,
  4. Sharief Hendricks1,5,
  5. Keith A Stokes6,7,8,
  6. Gemma Phillips1,9,10,
  7. Matt Cross1,11,
  8. Cameron Owen1,9,
  9. Ben Jones1,5,9,11,12
  1. 1Carnegie Applied Rugby Research (CARR) centre, Carnegie School of Sport, Leeds Beckett University, Leeds, UK
  2. 2The University of Newcastle, Callaghan, New South Wales, Australia
  3. 3Edge Hill University, Ormskirk, UK
  4. 4Sydney School of Health Sciences, The University of Sydney, Camperdown, New South Wales, Australia
  5. 5Division of Physiological Sciences, Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
  6. 6Centre for Health and Injury and Illness Prevention in Sport, University of Bath, Bath, UK
  7. 7Rugby Football Union, Twickenhamn, UK
  8. 8UK Collaborating Centre on Injury and Illness Prevention in Sport (UKCCIIS), University of Bath, Bath, UK
  9. 9England Performance Unit, Rugby Football League, Manchester, UK
  10. 10Uno-X Mobility Pro Cyclining Team, Oslo, Norway
  11. 11Premiership Rugby, London, UK
  12. 12School of Behavioural and Health Sciences, Faculty of Health Sciences, Australian Catholic University, Brisbane, Queensland, Australia
  1. Correspondence to Mr Manish Mohan; m.mohan{at}leedsbeckett.ac.uk

Abstract

Background Head-on-head impacts are a risk factor for concussion, which is a concern for sports. Computer vision frameworks may provide an automated process to identify head-on-head impacts, although this has not been applied or evaluated in rugby.

Methods This study developed and evaluated a novel computer vision framework to automatically classify head-on-head and non-head-on-head impacts. Tackle events from professional rugby league matches were coded as either head-on-head or non-head-on-head impacts. These included non-televised standard-definition and televised high-definition video clips to train (n=341) and test (n=670) the framework. A computer vision framework consisting of two deep learning networks, an object detection algorithm and three-dimensional Convolutional Neural Networks, was employed and compared with the analyst-coded criterion. Sensitivity, specificity and positive predictive value were reported.

Results The overall performance evaluation of the framework to classify head-on-head impacts against manual coding had a sensitivity, specificity and positive predictive value (95% CIs) of 68% (58% to 78%), 84% (78% to 88%) and 0.61 (0.54 to 0.69) in standard-definition clips, and 65% (55% to 75%), 84% (79% to 89%) and 0.61 (0.53 to 0.68) in high-definition clips.

Conclusion The study introduces a novel computer vision framework for head-on-head impact detection. Governing bodies may also use the framework in real time, or for retrospective analysis of historical videos, to establish head-on-head rates and evaluate prevention strategies. Future work should explore the application of the framework to other head-contact mechanisms and also the utility in real time to identify potential events for clinical assessment.

  • Concussion
  • Traumatic Brain Injury
  • Recreation / Sports
  • Sports / Leisure Facility

Data availability statement

All data relevant to the study are included in the article or uploaded as supplementary information.

https://doi.org/10.1136/ip-2023-045129

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    Data availability statement

    All data relevant to the study are included in the article or uploaded as supplementary information.

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    Footnotes

    • X @mattjcrossie, @23benjones

    • Contributors MM: conceptualisation, methodology, investigation, data curation, writing–original draft. DW: conceptualisation, data curation, writing–review and editing. AJG: writing–review and editing. SH: writing–review and editing. KAS: writing–review and editing. GP: writing–review and editing. MC: writing–review and editing. CO: conceptualisation, data curation, writing–review and editing. BJ: conceptualisation, data curation, writing–review and editing, project administration. MM is responsible for the overall content as the guarantor.

    • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

    • Competing interests AJG has a clinical practice in neuropsychology involving individuals who have sustained sport-related concussion. He is a contracted concussion consultant to Rugby Australia. He is the global clinical lead for the World Rugby Brain Health Service. He is a member of the World Rugby Concussion Working Group, and a member of the Australian Football League Concussion Scientific Advisory Committee. He has received travel funding or been reimbursed by professional sporting bodies, and commercial organisations for discussing or presenting sport-related concussion research at meetings, scientific conferences, workshops, and symposiums. Previous grant funding includes the NSW Sporting Injuries Committee, the Brain Foundation (Australia), an Australian-American Fulbright Commission Postdoctoral Award, a Hunter New England Local Health District, Research, Innovation and Partnerships Health Research & Translation Centre and Clinical Research Fellowship Scheme, and the Hunter Medical Research Institute (HMRI), supported by Jennie Thomas, and the HMRI, supported by Anne Greaves. He is currently supported by a National Health and Medical Research Council (NHMRC) Investigator Grant. He acknowledges unrestricted philanthropic support from the Tooth Foundation for concussion research and the National Rugby League for research in former professional rugby league players.

      MC is employed by Premiership Rugby and is a member of the World Rugby Concussion Working Group.

    • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

    • Provenance and peer review Not commissioned; externally peer reviewed.