EVALUATION OF TACKLING BIOMECHANICS IN RUGBY: VIDEO INCIDENT ANALYSIS AND EXPERIMENTAL SET UP

This study consisted of a video incident analysis of rugby tackles leading to spinal injuries, where players’ behaviours and observed loading mechanisms were coded for each incident. The key features of these events were summarised, revealing the role of highspeed impacts, illegal tackles, and poor tackle technique in injury-causing tackles. In addition, lateral bending moments and lateral flexion movements were more prevalent than suggested by previous research. This investigation informed an experimental protocol for the analysis of simulated rugby tackles, with the final goal to obtain measures of cervical spine biomechanics during tackles. Data captured from this protocol could also be input into a full-body musculoskeletal model to provide descriptions of internal cervical spine loading in different tackle event scenarios

FROM MEASUREMENT TO MODELLING IN SPORT COLLISIONS

Understanding the factors and the mechanism causing injury is one of the fundamental stages of the “sequence of prevention” of injury in sport (van Mechelen et al., 1992). Sports activities typically impose high and repetitive biomechanical demands on the neuro-musculo-skeletal system (e.g. Dufek & Bates, 1991; Trewartha et al., 2015), which research can try to capture and characterise. However, despite the progress of technologies and experimental methods, it is often impossible to directly measure the effects of specific sport events on the anatomical structures of the human body. In particular, the analysis of injury mechanisms in sports involving impacts (e.g. scrummaging and tackling in rugby, landing after a jump, or kicking in martial arts) needs to face a number of interdependent challenges, for which conventional approaches are not always adequate

ESTIMATION OF GROUND REACTION FORCE DURING RUNNING USING CONSUMER-LEVEL WEARABLE INSOLES AND MACHINE LEARNING

Data from NURVV Run, a consumer-level wearable technology product, embedding pressure insoles and inertial transducers, were used as an input into a deep learning model for the estimation of vertical ground reaction forces (vGRF) during running. Force data were collected from an instrumented treadmill during a running protocol of mixed gradients and speeds, serving as the gold standard to evaluate the model accuracy. Mean difference in peak vGRF was 0.36 ± 0.26 BW across participants and mean root mean squared error was 0.27 ± 0.15 BW. Model accuracy varied considerably between participants; it would be expected that a larger dataset with a greater variety of input variables would improve on this. A future version of this model could allow continual assessment of load accumulation during distance running, helping identify early signs of elevated injury risk

Modifications to the net knee moments lead to the greatest improvements in accelerative sprinting performance: a predictive simulation study

The current body of sprinting biomechanics literature together with the front-side mechanics coaching framework provide various technique recommendations for improving performance. However, few studies have attempted to systematically explore technique modifications from a performance enhancement perspective. The aims of this investigation were therefore to explore how hypothetical technique modifications affect accelerative sprinting performance and assess whether the hypothetical modifications support the front-side mechanics coaching framework. A three-dimensional musculoskeletal model scaled to an international male sprinter was used in combination with direct collocation optimal control to perform (data-tracking and predictive) simulations of the preliminary steps of accelerative sprinting. The predictive simulations differed in the net joint moments that were left ‘free’ to change. It was found that the ‘knee-free’ and ‘knee-hip-free’ simulations resulted in the greatest performance improvements (13.8% and 21.9%, respectively), due to a greater knee flexor moment around touchdown (e.g., 141.2 vs. 70.5 Nm) and a delayed and greater knee extensor moment during stance (e.g., 188.5 vs. 137.5 Nm). Lastly, the predictive simulations which led to the greatest improvements were also found to not exhibit clear and noticeable front-side mechanics technique, thus the underpinning principles of the coaching framework may not be the only key aspect governing accelerative sprinting.Peer ReviewedPostprint (published version

BIOMECHANICAL LOADS IN RUGBY UNION TACKLING ARE AFFECTED BY TACKLE DIRECTION AND IMPACT SHOULDER

Approximately 25% of Rugby Union injuries occur to players executing a tackle and they mostly involve upper-body regions. We designed novel tackle simulator to investigate upper-body loading under different tackling conditions: direction of approach and side of body used. Dominant shoulder tackles in the frontal direction generated the highest impact forces, 5.3 ± 1.0 kN (15% higher than non-dominant) and the lowest range of neck flexion (20% lower than non-dominant) at impact. Impact load decreased going from frontal to diagonal (-3%) and lateral tackling (-10%). The lowest peak head acceleration and angular velocity resulted from diagonal tackles with the dominant shoulder. For injury prevention, the tackler should approach from an offset angle from frontal and coaching should aim to reduce the deficiencies in tackling technique on the non-dominant side