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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.

当人们在长时间的运动中感到疲惫时，外骨骼可以为他们提供运动辅助。虽然该奖项的重点是身体健全的人使用自适应外骨骼，但这一结果也可以应用于帮助患有多发性硬化症等疾病的人，这些疾病的人在变得更加疲惫时需要增加一天的帮助。这个跨学科的团队将通过自适应外骨骼控制开发新的人-机器人交互方法，方法是使用新型的嵌入织物的传感器来测量人的移动方式，并开发一个模型来理解这些动作如何表明人何时变得疲惫。商业上可用的外骨骼并不明确地解决疲劳问题来增强耐力。此外，市面上可以买到的传感器用于感知佩戴者的身体运动和肌肉激活，这些传感器是刚性的，当佩戴在身体和外骨骼系统之间时可能会感到不舒服，外骨骼系统通常也有刚性部件。舒适透气的面料嵌入式传感器与自适应外骨骼控制器相结合，可以实时测量人的疲劳程度，从而提高人体和外骨骼性能的耐力。该项目包括一个软机器人设计课程，以扩大对计算的参与。了解和量化人体疲劳是一个复杂的研究问题。该项目将在穿戴者的身体和外骨骼之间使用一种柔软、透气的传感器服装来感知疲劳并得出疲劳指数。这种面料嵌入传感将允许开发更具能效的外骨骼，仅在需要时提供帮助(根据佩戴者的疲劳程度提供所需的功率水平)，并降低佩戴者的新陈代谢成本，同时防止由于始终处于外骨骼帮助下可能导致的潜在肌肉萎缩。为了实现这一目标，将使用一种跨学科的方法，将控制和机器人技术、人类性能测量、材料和软机器人以及人-机器人交互结合起来。这项研究将建立一个疲劳指数，该指数可以1)可靠地、定量地指示身体疲劳的程度，2)根据运动学和动力学数据很容易获得。应变场和嵌入织物的传感器将提供运动学数据，然后这些数据将被用于估计运动学数据。利用这些数据，这项研究将利用反馈控制理论和人体生物力学模型来创建一种系统的方法来监测疲劳，并具有可证明的估计精度。一个自适应外骨骼控制框架将被系统地派生出来，通过三个协同连接的层：激活器、优化器和实时控制器来明确地解决疲劳问题。由此产生的外骨骼控制器将通过推迟佩戴者疲劳的开始并允许更好地使用外骨骼动力来实现增强耐力的适应性援助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。