CHS: Medium: Collaborative Research: Fabric-Embedded Dynamic Sensing for Adaptive Exoskeleton Assistance

CHS：媒介：协作研究：用于自适应外骨骼辅助的织物嵌入式动态传感

基本信息

批准号：
1955979
负责人：
Holly Yanco
金额：
$ 62.83万
依托单位：
University of Massachusetts Lowell
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1955979&HistoricalAwards=false
关键词：
CHS Medium Collaborative Research Fabric

项目摘要

Exoskeletons can provide people with movement assistance when they become fatigued during long periods of exertion. While the focus of this award is on the use of adaptive exoskeletons by people who are able-bodied, the results could be applied to help people who have diseases such as multiple sclerosis, where people need increased assistance throughout the day as they become more tired. The interdisciplinary team will develop new human-robot interaction methods through adaptive exoskeleton control by using novel fabric-embedded sensors to measure how a person is moving and to develop a model to understand how these movements indicate when a person is becoming tired. Commercially-available exoskeletons do not explicitly address fatigue issues for enhancing endurance. Additionally, commercially-available sensors for sensing the wearer's body movement and muscle activation are rigid and can be uncomfortable when worn between the body and an exoskeleton system, which often also has rigid parts. The comfortable and breathable fabric-embedded sensors combined with an adaptive exoskeleton controller that can measure a person's fatigue in real time will allow endurance enhancement for human and exoskeleton performance. The project includes a soft-robotics design curriculum for broadening participation in computing.Understanding and quantifying fatigue in human body is a complex research problem. This project will utilize a soft, breathable sensor garment between the wearer's body and the exoskeleton to sense fatigue and come up with a fatigue index. Such a fabric embedded sensing will allow for the development of exoskeletons that are more power efficient, provide assistance only when needed (at the power level that is needed based upon the wearer's fatigue level), and reduce metabolic costs for the wearer while preventing potential muscle atrophy that could arise from always-on exoskeleton assistance. An interdisciplinary approach, combining controls and robotics, human performance measurement, materials and soft robotics, and human-robot interaction, will be used to accomplish this goal. This research will establish a fatigue index that can 1) reliably and quantitatively indicate the level of physical fatigue, and 2) be easily obtained based on kinematic and kinetic data. The strain-field and fabric-embedded sensors will provide the kinematic data, which will then be used to estimate the kinetic data. Exploiting these data, the research will draw upon feedback control theory and human biomechanical modeling to create a systematic method for monitoring fatigue with provable estimation accuracy. An adaptive exoskeleton control framework will be systematically derived to explicitly address fatigue issues through three synergistically connected layers: activator, optimizer, and real-time controller. The resulting exoskeleton controller will enable adaptive assistance for endurance enhancement by delaying the onset of wearers' fatigue and allowing better usage of exoskeleton power.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

外骨骼在长时间的劳累疲劳时会为人们提供运动援助。虽然该奖项的重点是有能力的人使用适应性外骨骼，但结果可以应用于帮助患有多发性硬化症等疾病的人，因为人们在变得更加疲倦的情况下需要全天增加援助。跨学科团队将通过使用新颖的织物包含的传感器来衡量一个人的移动并开发模型以了解这些运动如何表明某人何时疲倦的方式，从而通过自适应外骨骼控制来开发新的人类机器人交互方法。商业上可用的外骨骼并未明确解决疲劳问题以增强耐力。此外，用于感知佩戴者身体运动和肌肉激活的商业上可用的传感器是刚性的，当身体和外骨骼系统之间穿着时，可能会感到不舒服，这通常也具有刚性的部位。舒适且透气的织物包裹的传感器与自适应外骨骼控制器相结合，可以实时测量一个人的疲劳，将允许增强人类和外骨骼性能的耐力。该项目包括用于扩大计算参与的软机器人设计课程。理解和量化人体的疲劳是一个复杂的研究问题。该项目将利用佩戴者的身体和外骨骼之间的柔软，透气的传感器服装，以感知疲劳，并提出疲劳指数。这种织物嵌入的感应将允许开发更有效的外骨骼，仅在需要时提供帮助（基于佩戴者的疲劳水平所需的功率水平），并降低佩戴者的代谢成本，同时又可以防止始终在外骨骨骼援助中引起的潜在肌肉萎缩。将控制和机器人技术，人类绩效测量，材料和软机器人技术以及人类机器人相互作用结合起来的跨学科方法将用于实现这一目标。这项研究将建立一个疲劳指数，该指数可以1）可靠，定量地表明身体疲劳的水平，2）根据运动学和动力学数据很容易获得。应变场和织物包裹的传感器将提供运动学数据，然后将其用于估计动力学数据。利用这些数据，研究将利用反馈控制理论和人类生物力学建模，以创建一种系统的方法，以可证明的估计精度监测疲劳。自适应外骨骼控制框架将被系统地得出，以通过三个协同连接的层：激活器，优化器和实时控制器明确解决疲劳问题。由此产生的外骨骼控制器将通过延迟佩戴者的疲劳发作并允许更好地使用外骨骼功率来实现自适应辅助，以增强耐力。该奖项反映了NSF的法定任务，并认为通过基金会的知识分子和更广泛的影响，可以通过评估来进行评估。