Neuromechanics of postural responses to balance loss

跌倒是造成老年人意外伤害的主要原因，也是加拿大工人伤害赔偿索赔的第三大常见原因。为了避免跌倒和受伤，人类依靠一系列姿势反应来避免失去平衡，这些反应分为：1)平衡恢复反应(例如，踩着或抓住扶手)以避免跌倒，以及2)跌倒保护反应(例如，上肢支撑)，以减少跌倒时对身体部位的影响。我们对人类如何控制失去平衡的姿势反应的理解是有限的。姿势对失去平衡的反应比自愿动作(例如，听到声音后伸手)要快得多，需要更高的肌肉力量和关节扭矩来停止重心(COM)和躯干的动量。这些根本的差异意味着，来自自愿运动的知识在控制失去平衡的姿势反应方面应用得很差。我们需要更深入地了解人类如何控制体位反应，考虑到个人因素的复杂性(例如，年龄或性别)、他们的任务(例如，负重或行走)以及他们的环境(例如，灯光或有图案的表面)，以提供基于证据的跌倒和受伤预防策略。我的长期目标是建立神经和肌肉骨骼系统如何相互作用的综合模型(即神经力学)，以控制姿势反应以平衡丢失，并研究这些反应如何依赖于个人、运动任务和环境，以指导摔倒和受伤预防策略。对于建议的DG，我的团队将进行创新的实验室实验，以确定个人因素(例如，年龄、性别、力量、本体感觉和着陆位置)如何控制跌倒保护反应的控制。我们还将测试任务对平衡恢复和跌倒保护反应控制的影响，重点是负重和其他同时进行的任务(走路或说话)、年龄和性别。关于环境因素，我们将测试照明条件(环境照明与头盔或地板/扶手照明)的影响，以及年龄、性别和分心对平衡恢复反应控制的交互影响。我们将用COM/躯干动量来量化机械控制的有效性(使用运动捕捉)，以及用肌肉激活和努力来量化控制的神经方面(使用肌电图)。我的团队的结果将为探索未来其他个人、任务和环境因素对姿势反应控制的交互影响提供基础，例如照明系统和睡眠剥夺，或负重和肌肉疲劳。除了生成新的和改进现有的姿势反应控制模型外，本DG的研究结果还将指导负重和照明系统的最佳实践设计，以避免在家庭、社区或工作中摔倒和受伤。

Falls are the leading cause of unintentional injuries to older adults and the third-most common cause of worker's injury compensation claims in Canada. To avoid falls and injuries, humans rely on a repertoire of postural responses to balance loss, classified as: 1) balance recovery responses (e.g., stepping or grasping handrails) to avoid falls, and 2) fall protective responses (e.g., upper limb bracing) to reduce impact to body parts during a fall. Our understanding of how humans control postural responses to balance loss is limited. Postural responses to balance loss are much faster than voluntary movements (e.g., reaching after hearing a sound), and involve higher rates of muscle force and joint torque production to halt the momentum of the centre of mass (COM) and torso. These fundamental differences mean that knowledge from voluntary movements applies poorly to the control of postural responses to balance loss. We need a deeper understanding of how humans control postural responses that considers the complexity of individual factors (e.g., age or sex), their task (e.g., carrying a load or walking), and their environment (e.g., lighting or patterned surfaces), to inform evidence-based strategies for fall and injury prevention. My long-term goal is to generate comprehensive models of how the nervous and musculoskeletal systems interact (i.e., neuromechanics) to control postural responses to balance loss, and examine how these responses depend on the individual, the movement task, and the environment, to guide fall and injury prevention strategies. For the proposed DG, my team will run innovative lab experiments to identify how individual factors (e.g., age, sex, strength, proprioception, and landing position) govern the control of fall protective responses. We will also test the effect of tasks on the control of balance recovery and fall protective responses, focusing on carrying loads and other concurrent tasks (walking or talking), age, and sex. Regarding environmental factors, we will test the effect of lighting conditions (ambient lighting, versus head-mounted or floor/handrail-mounted lighting) and the interacting effects of age, sex, and distractions on the control of balance recovery responses. We will quantify the effectiveness of mechanical control with COM/torso momentum (using motion capture), and neural aspects of control with muscle activation and effort (using electromyography). My team's results will provide a foundation to explore future lines of inquiry on the interacting effects of other individual, task and environmental factors on the control of postural responses, such as lighting systems and sleep deprivation, or load carrying and muscle fatigue. In addition to generating new and improving existing models on the control of postural responses, findings from this DG will guide the design of best practices for carrying loads and of lighting systems, for avoiding falls and injuries in the home, community or at work.