A Musculoskeletal Simulation Framework for In Silico Design and Optimization of a Soft Exosuit for Children with Muscular Dystrophy

项目概要/摘要 肌营养不良（MD），如Duchenne MD和Becker MD，是罕见的遗传性疾病， 进行性肌肉退化在大约14/100，000的5 - 24岁的男性中发生。目前还没有已知的 治疗可以阻止或逆转由MD造成的损害。营养不良的肌肉特别容易受到 离心收缩时损坏。软机器护甲是一种新兴的可穿戴设备， 为了减少在功能性运动例如下坡行走期间的离心收缩， 膝伸肌离心性收缩。Exosuits利用顺应性的基于纺织品的致动器来固有地提供 安全性和舒适性，这对于避免患有MD的男孩的过度肌肉损伤是非常期望的。 然而，为患有MD的男孩开发外装服的一个潜在障碍是人类活动期间受伤的风险。 测试新的原型。肌肉骨骼模拟是早期设计的理想工具， 因为模拟提供了肌肉离心收缩的特征， 这在实验上难以观察，并且在测试原型时避免了对人类受试者造成伤害的风险。 因此，本项目的总体目标是开发一个肌肉骨骼模拟框架，用于计算机设计 和MD儿童软外装服的优化。首先，现有的健康成年人行走的模拟 下坡将被修改，包括模拟软外套。最初的机器护甲设计将基于现有的 物理原型将使用一种新的优化框架来优化机器护甲， 下坡行走时股直肌（膝盖伸肌）的收缩。将进行统计分析 为了检验以下假设：（1）模拟离心收缩（即，净负功）在股直肌中是 通过外装护具显著减少，以及（2）在优化之后进一步减少离心收缩。在 此外，关节反作用力和其他肌肉离心收缩的变化将被量化， 调查潜在的不良副作用。在确定了使用成年人的机器护甲的潜在益处之后， 模型，将开发儿童的肌肉骨骼模型。将使用模拟等距任务来校准 在典型发育和MD儿童模型中获得最大等长肌力，以匹配已发布的力量数据。 通过缩放成人，将生成儿童使用外装服下坡的初步模拟。 移动数据使用重定向算法。机器护甲优化框架将应用于 通常开发儿童和患有MD的儿童模型，以确定优化的机器护甲是否提供了 在未来的人类受试者中，股直肌离心收缩的充分减少具有统计学显著性 实验拟议的研究旨在为设计一种外装服奠定基础， 改善MD儿童的近期生活质量。此外，基于肌肉骨骼模拟的 设计优化框架将促进辅助设备的快速计算机设计和测试， 外装，对人体没有危险。

PROJECT SUMMARY/ABSTRACT Muscular dystrophies (MD), such as Duchenne MD and Becker MD, are rare genetic diseases that cause progressive muscle degeneration in approximately 14 in 100,000 males ages 5-24. Currently there is no known treatment that can stop or reverse the damage caused by MD. Dystrophic muscle is especially susceptible to damage during eccentric contraction. Soft exosuits are an emerging class of wearable device that may be able to reduce eccentric contraction during functional movements such as downhill walking, which requires substantial eccentric knee extensor contraction. Exosuits utilize compliant textile-based actuators to inherently provide safety and comfort to the user, which is highly desirable for avoiding excessive muscle injury in boys with MD. However, one potential barrier to development of exosuits for boys with MD is the risk of injury during human subjects testing of new prototypes. Musculoskeletal simulations are an ideal tool for early-stage design of exosuits for children with MD because simulations provide characterization of muscle eccentric contraction, which is difficult to observe experimentally, and avoid risk of injury to human subjects when testing prototypes. Thus, the overall objective of this project is to develop a musculoskeletal simulation framework for in silico design and optimization of a soft exosuit for children with MD. First, existing simulations of healthy adults walking downhill will be modified to include a simulated soft exosuit. The initial exosuit design will be based on existing physical prototypes. A novel optimization framework will be used to optimize the exosuit to minimize eccentric contraction in the rectus femoris (a knee extensor) during downhill walking. Statistical analysis will be performed to test the hypotheses that (1) simulated eccentric contraction (i.e., net negative work) in the rectus femoris is significantly reduced by the exosuit and (2) eccentric contraction is further reduced following optimization. In addition, changes in joint reaction forces and eccentric contraction in other muscles will be quantified to investigate potential adverse side effects. After establishing potential benefits of the exosuit using the adult model, a musculoskeletal model of a child will be developed. A simulated isometric task will be used to calibrate maximum isometric muscle force in a typically developing and MD child model to match published strength data. Preliminary simulations of a child walking downhill with the exosuit will be generated by scaling the adult movement data using a retargeting algorithm. The exosuit optimization framework will be applied to both the typically developing child and child with MD models to determine whether the optimized exosuit provided a sufficient reduction in rectus femoris eccentric contraction to be statistically significant in future human subjects experiments. The proposed research aims to establish a foundation for design of an exosuit that can substantially improve quality of life for children with MD in the near term. In addition, the musculoskeletal simulation-based design optimization framework will facilitate rapid in silico design and testing of assistive devices, such as soft exosuits, without risk to human subjects.