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

当人们在长时间的运动中感到疲劳时，外骨骼可以为他们提供运动帮助。虽然该奖项的重点是身体健全的人使用自适应外骨骼，但其结果可以应用于帮助患有多发性硬化症等疾病的人，这些疾病使人们在一天中变得更加疲惫时需要更多的帮助。该跨学科团队将通过自适应外骨骼控制开发新的人机交互方法，使用新型织物嵌入式传感器来测量人的运动方式，并开发一个模型来了解这些运动如何指示人何时变得疲劳。可商购的外骨骼没有明确地解决疲劳问题以增强耐力。另外，用于感测穿戴者的身体运动和肌肉激活的市售传感器是刚性的，并且当穿戴在身体和外骨骼系统之间时可能是不舒服的，外骨骼系统通常也具有刚性部分。舒适和透气的织物嵌入式传感器与自适应外骨骼控制器相结合，可以真实的时间测量人的疲劳度，这将允许增强人类和外骨骼性能的耐力。该项目包括一个软机器人设计课程，以扩大参与计算。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柔软，透气的传感器服装来感知疲劳并提出疲劳指数。这样的织物嵌入式感测将允许开发更功率高效的外骨骼，仅在需要时（在基于穿戴者的疲劳水平所需的功率水平下）提供辅助，并且降低穿戴者的代谢成本，同时防止可能由始终在线的外骨骼辅助引起的潜在肌肉萎缩。一个跨学科的方法，结合控制和机器人技术，人类性能测量，材料和软机器人技术，以及人机交互，将被用来实现这一目标。这项研究将建立一个疲劳指数，可以1）可靠和定量地表明身体疲劳的水平，2）容易获得的基础上运动学和动力学数据。应变场和织物嵌入式传感器将提供运动学数据，然后将用于估计动力学数据。利用这些数据，该研究将借鉴反馈控制理论和人体生物力学建模，以创建一个系统的方法来监测疲劳与可证明的估计精度。一个自适应外骨骼控制框架将系统地推导出明确解决疲劳问题，通过三个协同连接层：激活器，优化器和实时控制器。由此产生的外骨骼控制器将通过延迟穿戴者的疲劳发作和允许更好地使用外骨骼功率来实现对耐力增强的自适应辅助。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估而被认为值得支持。

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.