CPS: Synergy: Collaborative Research: Closed-loop Hybrid Exoskeleton utilizing Wearable Ultrasound Imaging Sensors for Measuring Fatigue

该项目的目标是开发一种自动化辅助设备，能够恢复运动障碍者的行走和站立功能。虽然对辅助设备，如主动和被动矫形器和外骨骼的研究已经进行了几十年，但由于一些限制，移动性方面的改善并不大。一个主要的挑战是有限的能力来感知和解释人类的状态，包括意志运动意图和疲劳。拟议中的装置将包括电动马达，以及由人自己的肌肉产生的电力。这项工作建议开发新的传感器来监测肌肉功能，当肌肉疲劳被识别时，系统将切换到电机，直到肌肉恢复。通过对混合辅助设备的无缝自动化控制方法的研究，同时将肌肉疲劳降至最低，本研究解决了以往工作的重大局限性。拟议的项目具有显著改善脊髓损伤和中风患者的步行和生活质量的长期潜力。这项拟议的工作还将通过计算算法将可穿戴式基于图像的生物传感与物理外骨骼系统相结合，从而为网络物理系统的新科学做出贡献。该项目将为研究生和本科生提供身临其境的跨学科培训，将计算方法与成像、机器人、人类功能活动和人工设备相结合，以解决具有挑战性的公共卫生问题。我们将把重点放在让本科生参与研究，这是我们机构有组织的课程的一部分。此外，残疾学生将通过利用正在进行的国家科学基金会资助的项目来参与这项研究活动。该项目包括开发可穿戴的超声成像传感器和实时图像分析算法，可以直接测量底层肌肉的功能和状态。这将允许开发动态控制分配算法，该算法利用该信息在激励和刺激之间分配控制。这种基于肌肉特定反馈的闭环控制方法代表了传统的下肢外骨骼的范式转变，传统的外骨骼仅依赖关节运动学进行反馈。作为这一新方法的试验台，该团队将利用一种混合外骨骼，将主动关节致动器与对人自身肌肉的功能性电刺激结合在一起。重复的电刺激会导致肌肉疲劳的快速发作，从而限制了这些混合系统的实用性，并可能增加受伤的风险。该项目的目标是：开发用于实时感知肌肉功能和疲劳的新型超声传感技术和图像分析算法；研究利用测量的肌肉收缩速率将疲劳降至最低的闭环控制分配算法；将传感和控制方法集成到闭环混合外骨骼系统中，并对脊髓损伤患者进行评估。提出的方法将通过(1)将人在环的物理外骨骼系统与基于图像的复杂时变物理现象(如动态神经肌肉活动和疲劳)的实时稳健传感相结合，以及(2)开发新的计算模型来解释这些现象并有效地适应控制策略，从而产生创新的CPS科学。这项研究将使基于图像的实际可穿戴生物传感在医疗保健中有更广泛的应用。该框架可以广泛应用于许多涉及人在环路中的医疗CPS问题，包括上肢和下肢假肢和外骨骼、康复和手术机器人。这种新的基于传感器测量的控制分配算法可以在容错和冗余执行器系统以及无人机的可靠容错控制中具有更广泛的适用性。

The goal of this project is to develop an automated assistive device capable of restoring walking and standing functions in persons with motor impairments. Although research on assistive devices, such as active and passive orthoses and exoskeletons, has been ongoing for several decades, the improvements in mobility have been modest due to a number of limitations. One major challenge has been the limited ability to sense and interpret the state of the human, including volitional motor intent and fatigue. The proposed device will consist of powered electric motors, as well as the power generated by the person's own muscles. This work proposes to develop novel sensors to monitor muscle function, and, muscle fatigue is identified, the system will switch to the electric motors until the muscles recover. Through research on methods of seamless automated control of a hybrid assistive device while minimizing muscle fatigue, this study addresses significant limitations of prior work. The proposed project has the long-term potential to significantly improve walking and quality of life of individuals with spinal cord injuries and stroke. The proposed work will also contribute to new science of cyber-physical systems by integrating wearable image-based biosensing with physical exoskeleton systems through computational algorithms. This project will provide immersive interdisciplinary training for graduate and undergraduate students to integrate computational methods with imaging, robotics, human functional activity and artificial devices for solving challenging public health problems. A strong emphasis will be placed on involving undergraduate students in research as part of structured programs at our institutions. Additionally, students with disabilities will be involved in this research activities by leveraging an ongoing NSF-funded project. This project includes the development of wearable ultrasound imaging sensors and real-time image analysis algorithms that can provide direct measurement of the function and status of the underlying muscles. This will allow development of dynamic control allocation algorithms that utilize this information to distribute control between actuation and stimulation. This approach for closed-loop control based on muscle-specific feedback represents a paradigm shift from conventional lower extremity exoskeletons that rely only on joint kinematics for feedback. As a testbed for this new approach, the team will utilize a hybrid exoskeleton that combines active joint actuators with functional electrical stimulation of a person's own muscles. Repetitive electrical stimulation leads to the rapid onset of muscle fatigue that limits the utility of these hybrid systems and potentially increases risk of injury. The goals of the project are: develop novel ultrasound sensing technology and image analysis algorithms for real-time sensing of muscle function and fatigue; investigate closed-loop control allocation algorithms utilizing measured muscle contraction rates to minimize fatigue; integrate sensing and control methods into a closed loop hybrid exoskeleton system and evaluate on patients with spinal cord injury. The proposed approach will lead to innovative CPS science by (1) integrating a human-in-the-loop physical exoskeleton system with novel image-based real-time robust sensing of complex time-varying physical phenomena, such as dynamic neuromuscular activity and fatigue, and (2) developing novel computational models to interpret such phenomena and effectively adapt control strategies. This research will enable practical wearable image-based biosensing, with broader applications in healthcare. This framework can be widely applicable in a number of medical CPS problems that involve a human in the loop, including upper and lower extremity prostheses and exoskeletons, rehabilitation and surgical robots. The new control allocation algorithms relying on sensor measurements could have broader applicability in fault-tolerant and redundant actuator systems, and reliable fault-tolerant control of unmanned aerial vehicles.