权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Robust biomimetic models of human legs to solve high-dimensional real-time control problems

鲁棒的人体腿部仿生模型解决高维实时控制问题

基本信息

批准号：
9979392
负责人：
Sergiy Yakovenko
金额：
$ 7.6万
依托单位：
WEST VIRGINIA UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-15 至 2022-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9979392
关键词：
Address Age Anatomy Articular Range of Motion Basic Science Behavioral Biological Biomimetics Clinical Clinical assessments Data Data Set Development Devices Electric Stimulation Electromyography Engineering Ensure Evaluation Exercise Physiology Failure Funding Opportunities Future Generations Goals Grant Hand Human Intuition Joints Leg Length Limb Prosthesis Limb structure Lower Extremity Machine Learning Measurement Methods Modeling Morphology Motion Movement Muscle Musculoskeletal Musculoskeletal System Neuromechanics Orthopedics Outcome Participant Pathway interactions Pattern Performance Physical therapy Polynomial Models Posture Process Prosthesis Publishing Reaction Rehabilitation therapy Reproducibility Research Signal Transduction Slide Speed Surface Technology Testing Time Upper Extremity Validation Variant Work arm base computer human interaction high dimensionality human model innovation kinematics locomotor deficit locomotor tasks model development motor control neuroprosthesis novel real time model relating to nervous system repository sex simulation spatiotemporal tool treadmill

项目摘要

Project Summary Anatomically validated musculoskeletal models of human limbs computed faster than real-time are tools that will help advance the control of neuroprosthetics, rehabilitation, and the study of motor control principles. However, the current state-of-the-art models cannot be both accurate and fast. We propose to develop a new generation of validated real-time human leg models with musculoskeletal dynamics that are robust over the full range of multidimensional motion. The first aim is to validate a lower-limb model in the full range of static postures. The second aim is to validate a lower-limb model during dynamic locomotor tasks. Building on our previous work, we will use OpenSim model repository as a starting point for the iterative process of validating the muscle anatomy and function using published anatomical data. We expect to recreate the full range of leg postures with the validated model. We will then collect data during locomotor tasks performed by healthy humans on the split-belt treadmill with simultaneous re- cordings of ground reaction forces, full-body motion capture, and surface electromyography from rep- resentative leg muscles. The model will be validated over a rich experimental dataset for locomotor pat- terns required in asymmetric stepping on a self-paced treadmill. We expect to validate the dynamic model by estimating in real-time the observed full body kinematics from muscle activity and ground reaction forces. The inverse solutions will allow us to estimate the ongoing spatiotemporal patterns of muscle activity. At the conclusion of this study we will develop the detailed lower-limb model with high-di- mensional robust muscle path simulations to predict limb motion in real-time. The outcomes of this proposal will inform future work on the use of the real-time musculoskeletal models for the develop- ment of augmentation devices and the clinical assessment of locomotor deficits.

项目摘要人体四肢的解剖学验证的肌肉骨骼模型计算速度比实时更快这些工具将有助于推进神经修复术的控制、康复和运动神经的研究。控制原则。然而，目前最先进的模型不能既准确又快速。我们建议开发新一代经过验证的实时人体腿部模型，在多维运动的整个范围内具有鲁棒性的动力学。第一个目标是验证一个下肢模型中的全方位静态姿势。第二个目标是验证下肢模型在动态运动任务中。在我们之前工作的基础上，我们将使用OpenSim模型库作为使用已发表的解剖学数据验证肌肉解剖学和功能的迭代过程。我们希望用经过验证的模型重新创建腿部姿势的全部范围。我们将收集数据在分裂带跑步机上由健康人执行的运动任务期间，地面反作用力、全身运动捕捉和表面肌电图的记录，腿部肌肉发达。该模型将在丰富的实验数据集上进行验证，在自行步速跑步机上进行不对称踩踏时所需的能量。我们希望验证动态通过从肌肉活动和地面实时估计观察到的全身运动学来建立模型反作用力逆解将使我们能够估计正在进行的时空模式，肌肉活动在这项研究的最后，我们将开发详细的下肢模型与高迪，肌肉路径模拟，以实时预测肢体运动。这个结果该提案将为未来使用实时肌肉骨骼模型进行开发的工作提供信息- 增强装置的安装和运动缺陷的临床评估。