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

开发和应用用于评估脊柱稳定性、神经肌肉控制和负载的新技术和模型。

• 批准号: RGPIN-2020-04748
• 负责人: Graham, Ryan
• 金额: $ 2.91万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717272
• 关键词: Development; application; novel; technologies; models

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.

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 风险。 本赠款周期的具体目标是合并生物力学，运动控制和人工智能（即机器和深度学习），以通过沿两个主要主题进行一系列研究来推进我们以前的工作：1）脊柱稳定性和神经肌肉控制，以及2）脊柱负载的肌肉骨骼模型。更具体地说，直接与我的全球NSERC目标保持一致，第一个主题的目标将是：i）验证和提高我们的团队正在构建新型可穿戴传感器框架的准确性，以快速有效地收集和分析实验室和/或现场中的神经肌肉控制变量； ii）测试跨年龄段和性别的大量健康参与者样本，并将这些结果与监督和无监督的机器学习技术相结合，以更好地了解跨组的稳定和控制策略，以及在暴露于肌肉疲劳时如何改变这些策略； iii）对这些分析进行了对表面和高密度肌电图和超声分析的参与者的补充，以进一步了解肌肉激活策略和组织特性对控制脊柱运动和稳定性的影响。对此的补充，第二个主题的目标将是：i）提高我们先前开发的整个身体开发的OpenSim肌肉骨骼模型的准确性，重点是通过提高其生物性和自定义性，并将输出与有限元分析联系起来。 ii）验证模型以外的更多运动任务，而iii）开发了使用基于深度学习的无标记运动捕获和/或可穿戴传感器来驱动模型的方法，以提高数据收集的易度性和评估现场脊柱载荷的能力。 这个赠款周期将在MSC，博士学位和博士后培训一组高素质的人员组，以产生突破性的知识和能力，以评估脊柱稳定性，神经肌肉控制，并在实验室和现场具有更高的忠诚度，并在实验室和现场中延长了延长的忠诚度，这最终会更好地了解风险因素和机械性损害机构的机构，使机械化机构造成的机构和机械性机制机制。作为一种特定的用例，我们将能够评估军事负荷运输和任务对士兵负担和肌肉骨骼损伤风险的影响；我们正在积极与加拿大国防研究和发展一起工作的事情。