Automatic detection of collisions in elite level rugby union using a wearable sensing device

Abstract

Elite rugby union teams currently employ the latest technology to monitor and evaluate the physical demands of training and games on their players. Tackling has been shown to be the most common cause of injury in rugby union, yet current player monitoring technology does not effectively evaluate player tackling measurements. Currently, to evaluate measurements specific to player tackles, a time-consuming manual analysis of player sensor data and video footage is required. The purpose of this work is to investigate tackle modeling techniques which can be utilised to automatically detect player tackles and collisions using sensing technology already being used by elite international and club level rugby union teams. This paper discusses issues relevant to automatic tackle analysis, describes a technique to detect tackles using sensing data and validates the technique by comparing automatically detected collisions to manually labeled collisions using data from elite club and international level players. The results of the validation show that the system is able to consistently identify collisions with very few false posi- tives and false negatives, achieving a recall and precision rating of 0.933 and 0.958, respectively. The aim is that the automatically detected tackles can provide coaching, medical and strength and conditioning staff with objective tackle-specific measurements, in real time, which can be used in injury prevention and rehabilitation strategies.