Development and application of novel technologies and models for the assessment of spine stability, neuromuscular control, and loading.

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The long-term aim of my NSERC research program is two-fold: 1) to develop novel models and technologies to improve the accuracy and accessibility of spine biomechanical and neuromuscular control measures in the laboratory and field, and 2) to use these advancements to better understand the fundamental processes underlying the neuromuscular control of spine stability and movement, as well as how intrinsic and extrinsic factors affect spine stability, loading, and injury risk. The specific objectives of this grant cycle are to merge biomechanics, motor control, and artificial intelligence (i.e. machine and deep learning), to advance our previous work through a series of studies along two primary themes: 1) spine stability and neuromuscular control, and 2) musculoskeletal modelling of spine loading. More specifically, and directly in line with my global NSERC objectives, the goals of the first theme will be to: i) validate and improve the accuracy of a novel wearable sensor framework that our team is building to quickly and efficiently collect and analyze spine stability and neuromuscular control variables in the laboratory and/or field; ii) test a large sample of healthy participants across age groups and sexes and combine these results with supervised and unsupervised machine learning techniques to better understand stabilizing and control strategies across groups and how these are altered when exposed to muscle fatigue; and iii) complement these analyses on a subset of participants with surface and high-density electromyography and ultrasound analyses to further understand the effects of muscle activation strategies and tissue properties on the control of spine movement and stability. Complementary to this, the goals of the second theme will be to: i) improve the accuracy of our previously developed OpenSim musculoskeletal model of the whole body with a focus on the lumbar spine by improving its biofidelity and customizability, as well as linking outputs to finite-element analyses; ii) validate the model for a greater number of movement tasks beyond lifting, and iii) develop methods to drive the model using deep-learning-based markerless motion capture and/or wearable sensors to improve the ease of data collection and ability to assess spinal loads in the field. This grant cycle will train a large, diverse and inclusive group of highly qualified personnel at the MSc, PhD, and Post-Doctoral level to generate breakthrough knowledge and capabilities to assess spine stability, neuromuscular control, and loading with higher fidelity in the lab and in the field, which will ultimately lead to a better understanding of the risk factors and mechanisms underlying spine injury mechanics. As a specific use-case, we will be able to assess the effects of military load carriage and tasks on soldier burden and musculoskeletal injury risk; something that we are actively working on with Defence Research and Development Canada.