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: 6895543
• 负责人: William Steven Marras
• 金额: $ 36.21万
• 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 2007-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6895543
• 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：与职业相关的下背部疾病（LBD）仍然是当今工作日期损失的主要原因，是当今行业面临的最昂贵的职业安全和健康问题。已经有很好的确定，大多数与职业相关的LBD风险都与手动材料处理活动以及工作场所的心理心理影响有关（美国国家科学院，2001年）。此外，个体因素会影响风险。但是，我们表征与LBD风险各个方面相关的风险的能力已被鲜为人知。最近的文献表明，这些风险维度中的每一个中的一个共同联系涉及躯干肌肉共激活的增加，这会导致脊柱负荷增加和随后的LBD。肌电图（EMG）辅助模型提供了准确评估和量化脊柱载荷时躯干肌肉共激活变化的影响的唯一手段。但是，在大多数工业条件下的EMG收集是不切实际的。这项工作的目的是开发脊柱加载评估系统（SLA），该系统具有评估躯干肌肉共激活模式和随后的脊柱加载，以响应多个风险维度。该系统将允许人们准确地估计脊柱负载，这是由于身体工作场所因素，社会心理因素和个体因素的结果，但不需要使用EMG。通过开发混合神经模糊引擎（HNFE）来实现这一目标。该发动机将充当一个人工“大脑”，能够综合有关工作场所的信息，并评估躯干肌肉的表现。该发动机将与良好发达的生物驱动的动力学生物力学模型接口。通过这种方式，我们将能够准确预测工作场所中的脊柱载荷，以响应各种风险因素维度，而无需在工作场所收集EMG数据。总体而言，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，该脊柱结构损害被认为会导致腰痛，这是通常与工作场所相关的工作维度的函数。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维度如何协同影响肌肉骨骼系统的见解。最后，它将促进有关稳定性和相互抗偿的进一步研究。