Computational Digital Twin Modelling for Impact Reconstruction and Traumatic Brain Injury in Sports

High energy multibody impacts in sport can cause Head Acceleration Events (HAE) and concussion, resulting in short-term functional impairment and increased risk of neurodegenerative disease; the latter potentially occurring also in multiple sub-concussive impacts. At present, the common diagnostic and prevention solutions overlook the dynamic role of human biomechanics and active response to impacts.

It remains unclear as to how our postures and muscle tensions affect the consequence of HAE. The understanding of this is particularly challenging due to the dynamic nature of sports and athlete training. In amateur youth teams, the task is further complicated by a generalised lack of resources to monitor or track the impact events.

Consequently, it becomes difficult to determine whether inappropriate techniques, rules or poorly designed PPEs are the main source of HAE.

Research into the effects of head impacts is limited by 1) simplifications in neck biomechanics modelling, 2) insufficient data of the head-neck dynamics during the event, and 3) subject-specific Finite Element Modelling (FEM). While recent work has aimed at bypassing the subject-specific requirement by considering an additional upper machine learning layer to tune the FEM outputs as a function of additional metadata (e.g., sex, height, etc.), more accurate neck kinematics should be included in the current pipeline to differentiate accidental collision (e.g., head-to-head impact in football) from intentional collision (e.g., header in football).

I proposed to create both a physical twin and a digital twin to overlay the 3D motion capture data and implement it alongside FEM-based head models to reconstruct complete impact scenarios. The novel method has practical applications for sideline intervention as well as in further research. The aims and objectives of the project are to;

1.Develop a soft robotic neck as the Physical Twin (PT). Design and develop a soft robotic neck system with actuating artificial muscles to replicate human neck biomechanics for physical testing and synthesise data for the computational model.

2.Autonomously reconstruct and synthesise impact conditions. Reconstruct initial impact conditions in silico by processing visual motion capture data and coupling to biomechanics and multibody simulations.

3.Develop active multibody physics-based Digital Twin (DT). Use inverse dynamics to estimate model parameters representing internal body forces which most closely replicate kinematic motions. Muscles of the neck are of particular interest as they can transmit forces to the head on impact, resulting in HAE.

4. Reduce simulation time for HAE estimates and injury assessment. Incorporate resulting model in physics-informed machine learning layer to replace computationally expensive models previously developed in Aim 3.

It can be further developed with the Medical Sciences Division at the University of Oxford to generate intervention advice for coaches in grassroots sporting events. Given the prevalence of indirect HAE in contact sports, there remains a lack of full-body computational models developed to study such impacts. The novel approach will introduce active musculoskeletal dynamics to consider the response to indirect impacts and the forces transmitted through the neck which are not considered in passive impact models.

The use of FEM for impact reconstruction is limited by long simulation runtimes, which make them unfeasible for real-time applications. Integration of data-driven Machine Learning (ML) models can develop computational physics-informed surrogate capable of fast simulation runtimes with a high degree of biofidelity. This project falls within the EPSRC "Robotics" and "Assistive technology, rehabilitation and musculoskeletal biomechanics" research areas, specifically modelling and simulation of the musculoskeletal biomechanics under dynamic conditions. Collaborators involved are the Podium Analytics Institute & University of Cardiff