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

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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.