Cazzola, D. | bioRxiv Science

bioRxiv2022

Hyperflexion is unlikely to be the primary cervical spine injury mechanism in accidental head-on rugby tackling

In Rugby a high proportion of catastrophic cervical spine injuries occur during tackling. In the injury prevention literature, there is still an open debate on the injury mechanisms related to such injuries, with hyperflexion and buckling being under scrutiny. The aims of this study were to determine the primary cervical spine injury mechanism during head-on rugby tackling, and evaluate the effect of tackling technique on cervical spine intervertebral loading. We conducted an in silico study to examine the dynamic response of the cervical spine under loading conditions representative of accidental head-on rugby tackles by using a subject-specific musculoskeletal model of a rugby player. The computer simulations were driven by experimental in vivo data of an academy rugby player tackling a punchbag, and in vitro data of head-first impacts using a dummy head. Results showed that: i) the earlier generation of high compression and anterior shear loads with low values of flexion moments provides evidence that hyperflexion is unlikely to be the primary injury mechanism in the sub-axial cervical spine (C3-C7) during central and posterior head impact locations; ii) a higher degree of neck flexion at impact poses the cervical spine in a more hazardous position. These findings provide objective evidence to inform injury prevention strategies or rugby law changes, with the final view of improving the safety of the game of rugby.

bioengineering↗

bioRxiv2021

Development and validation of FootNet; a new kinematic algorithm to improve foot-strike and toe-off detection in treadmill running

The accurate detection of foot-strike and toe-off is often critical in the assessment of running biomechanics. The gold standard method for step event detection requires force data which are not always available. Although kinematics-based algorithms can also be used, their accuracy and generalisability are limited, often requiring corrections for speed or foot-strike pattern. The purpose of this study was to develop FootNet, a novel kinematics and deep learning-based algorithm for the detection of step events in treadmill running. Five treadmill running datasets were gathered and processed to obtain segment and joint kinematics, and to identify the contact phase within each gait cycle using force data. The proposed algorithm is based on a long short-term memory recurrent neural network and takes the distal tibia anteroposterior velocity, ankle dorsiflexion/plantar flexion angle and the anteroposterior and vertical velocities of the foot centre of mass as input features to predict the contact phase within a given gait cycle. The chosen model architecture underwent 5-fold cross-validation and the final model was tested in a subset of participants from each dataset (30%). Non-parametric Bland-Altman analyses (bias and [95% limits of agreement]) and root mean squared error (RMSE) were used to compare FootNet against the force data step event detection method. The association between detection errors and running speed, foot-strike angle and incline were also investigated. FootNet outperformed previously published algorithms (foot-strike bias = 0 [-10, 7] ms, RMSE = 5 ms; toe-off bias = 0 [-10, 10] ms, RMSE = 6 ms; and contact time bias = 0 [-15, 15] ms, RMSE = 8 ms) and proved robust to different running speeds, foot-strike angles and inclines. We have made FootNets source code publicly available for step event detection in treadmill running when force data are not available.

bioinformatics↗