Publication

Prediction of on-field rugby scrummaging contact forces from videos using artificial neural networks

Journal Article (2026)

Journal

PLOS One

Pages

e0330097

Volume

21

Number

4

Doc link

https://doi.org/10.1371/journal.pone.0330097

File

Download the digital copy of the doc pdf document

Authors

Abstract

Rugby is a contact sport with a high risk of injury. Scrums are a key component of the game and represent a collision event associated with serious injuries, especially to the cervical spine. Traditional methods for measuring scrum forces rely on both pressure sensors and scrum machines, but these have limitations for on-field monitoring. Thus, this study investigates the feasibility of using a machine learning approach to predict contact forces in rugby scrummaging during real-game situations, based on only anatomical landmarks of players extracted from video footage. A recurrent neural network (RNN) model was trained to predict contact forces from velocity signals. A dataset of eleven rugby teams (22 forward packs, n = 176 players) during on-field scrum engagements was used. Data augmentation techniques using a generative adversarial network and the Mixup technique were applied to increase the size of the training dataset. The RNN was trained using time-dependent data, with 2D trajectories from video landmarks as inputs and force signals from pressure sensors as outputs. The RNN´s prediction results showed good to excellent agreement between the predicted and measured time-dependent normal contact force signals, with a mean correlation of 0.95 ± 0.05. The mean normalized RMSE was 9.3 ± 4.3% and the mean normalized absolute difference in peak force was 6.7 ± 5.5%. This study demonstrated the feasibility of using RNNs to quantify contact forces in rugby scrummaging using only single-camera video, without players wearing sensors. In combination with a 2D human pose estimation model, the ANN trained in this study could support performance analysis, coaching, technique correction, and injury prediction using in-game data.

Categories

image recognition, learning (artificial intelligence), pattern classification.

Author keywords

contact force prediction, recurrent neural networks, sports biomechanics

Scientific reference

J. Cordero-Sánchez, Z. Sheehy, G. Serrancolí, E. Preatoni, G. Trewartha and D. Cazzola. Prediction of on-field rugby scrummaging contact forces from videos using artificial neural networks. PLOS One, 21(4): e0330097, 2026.