Neuro-Fuzzy Prediction of Spine Loads in Response to Ri*

响应 Ri* 的脊柱负荷的神经模糊预测

• 批准号: 6662614
• 负责人: William Steven Marras
• 金额: $ 35.95万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-30 至 2006-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6662614
• 关键词: back injury; backache; bioengineering /biomedical engineering; biomechanics; biomedical equipment development; clinical research; computer human interaction; computer program /software; electromyography; ergonomics; human subject; industry; mechanical pressure; muscle stress; musculoskeletal system; neural information processing; occupational hazard; occupational health /safety; occupational psychology; spine; work site

DESCRIPTION: Occupationally-related low back disorders (LBDs) continue to be the leading cause of lost work days and the most costly occupational safety and health problem facing industry today. It has been well established that most occupationally-related LBD risk is associated with manual materials handling activities as well as psychosocial influences in the workplace (National Academy of Sciences, 2001). In addition, individual factors can influence risk. However, our ability to characterize risk associated with these various dimensions of LBD risk has been rather poorly understood. Recent literature indicates that a common link within each of these risk dimensions involves increases in trunk muscle coactivation that can lead to increased spine loading and subsequent LBD. Electromyographic (EMG)-assisted models provide the only means to accurately assess and quantify the effect of changes in trunk muscle coactivation upon spinal loading. However, the collection of EMG under most industrial conditions is impractical. The objective of this work is to develop a Spine Loading Assessment System (SLAS) that has the capacity to assess trunk muscle coactivation patterns and subsequent spine loading in response to multiple risk dimensions. This system would permit one to accurately estimate spine loading as a result of physical workplace factors, psychosocial factors, and individual factors but would not require the use of EMG. This objective will be achieved through the development of a Hybrid Neuro-Fuzzy Engine (HNFE). This engine would act as a system artificial "brain" able to synthesize information about the workplace and assess how the trunk musculature would behave. The engine will interface with a well-developed biologically-driven dynamic biomechanical model of the trunk. In this manner, we will be able to accurately predict spine loading in the workplace in response to various risk factor dimensions without the need to collect EMG data in the workplace. Collectively, the SLAS will have several benefits. First, it can be used to assess the risk of spine structure damage believed to contribute to low back pain as a function of work dimensions commonly associated with the workplace. Hence, this model will have immediate applications to workplace designs. Second, the system will provide insights as to how the various dimensions of risk synergistically impact the musculoskeletal system. Finally, it will facilitate further investigations regarding stability and coactivity.

描述：与职业相关的腰背部疾病（lbd）仍然是导致工作日损失的主要原因，也是当今行业面临的最昂贵的职业安全和健康问题。已经确定的是，大多数与职业相关的LBD风险与工作场所的手工材料处理活动以及社会心理影响有关（美国国家科学院，2001年）。此外，个体因素也会影响风险。然而，我们描述与LBD风险的这些不同维度相关的风险的能力却鲜为人知。最近的文献表明，这些风险维度中的一个共同联系涉及躯干肌肉协同激活的增加，这可能导致脊柱负荷增加和随后的腰痛。肌电图（EMG）辅助模型提供了准确评估和量化躯干肌肉协同激活变化对脊柱负荷影响的唯一手段。然而，在大多数工业条件下收集肌电图是不切实际的。这项工作的目的是开发一个脊柱负荷评估系统（SLAS），该系统具有评估躯干肌肉协同激活模式和随后的脊柱负荷以响应多个风险维度的能力。该系统将允许人们准确地估计由于物理工作场所因素、社会心理因素和个人因素造成的脊柱负荷，但不需要使用肌电图。这一目标将通过开发混合神经模糊引擎（HNFE）来实现。这个引擎将作为一个系统的人工“大脑”，能够合成有关工作场所的信息，并评估躯干肌肉组织的行为。发动机将与一个发达的生物驱动的动态生物力学模型相连接。通过这种方式，我们将能够在不需要收集工作场所肌电图数据的情况下，准确预测工作场所脊柱负荷对各种风险因素的响应。总的说来，sla将有几个好处。首先，它可以用来评估脊柱结构损伤的风险，这被认为是导致腰痛的原因，是与工作场所通常相关的工作尺寸的函数。因此，该模型将立即应用于工作场所设计。其次，该系统将提供有关风险的各个方面如何协同影响肌肉骨骼系统的见解。最后，它将促进关于稳定性和协同性的进一步研究。