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

CPS：协同：协作研究：利用可穿戴超声成像传感器测量疲劳的闭环混合外骨骼

• 批准号: 1646204
• 负责人: Siddhartha Sikdar
• 金额: $ 39.99万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1646204&HistoricalAwards=false
• 关键词: CPS; Synergy; Collaborative; Research; Closed

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.

该项目的目的是开发一种能够在有运动障碍的人中恢复步行和站立功能的自动辅助设备。尽管对辅助设备的研究，例如积极的和被动的矫形器和外骨骼，已经进行了几十年，但由于许多局限性，移动性的改善一直有所改善。一个主要的挑战是感知和解释人类状态的能力有限，包括自愿运动意图和疲劳。所提出的设备将由电动机以及人自己的肌肉产生的功率组成。这项工作建议开发新型传感器来监测肌肉功能，并且发现肌肉疲劳，系统将切换到电动机，直到肌肉恢复为止。通过研究混合辅助装置的无缝自动控制方法，同时最大程度地减少肌肉疲劳，本研究解决了先前工作的重大局限性。拟议的项目具有长期的潜力，可以显着改善脊髓损伤和中风的个体的步行和生活质量。拟议的工作还将通过通过计算算法将基于可穿戴图像的生物传感与物理外骨骼系统整合到网络物理系统的新科学。该项目将为研究生和本科生提供身临其境的跨学科培训，以将计算方法与成像，机器人技术，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健康问题。作为我们机构结构化计划的一部分，将强烈重视与本科生一起研究。此外，通过利用正在进行的NSF资助的项目，将涉及残疾学生。该项目包括开发可穿戴超声成像传感器和实时图像分析算法，这些算法可以直接测量基础肌肉的功能和状态。这将允许开发动态控制分配算法，这些算法利用此信息在致动和刺激之间分配控制。这种基于肌肉特异性反馈的闭环控制方法代表了仅依靠关节运动学的传统下肢外骨骼的范式转移。作为对这种新方法的测试床，团队将利用一种混合外骨骼，将主动的关节执行器与人体自身肌肉的功能电刺激相结合。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效用，并可能增加了损伤的风险。该项目的目标是：开发新颖的超声传感技术和图像分析算法，以实时感测肌肉功能和疲劳；研究使用测得的肌肉收缩率来最大程度地减少疲劳的闭环控制分配算法；将传感和控制方法整合到闭环杂种外骨骼系统中，并对脊髓损伤患者进行评估。提出的方法将通过（1）与基于图像的新型实时感知到复杂的时间变化的物理现象的新型实时感知的新型实时感知的人类物理外骨骼系统，从而导致创新的CPS科学，例如动态神经肌肉活性和疲劳，以及（2）开发新的计算模型来解释这种现象和有效的控制策略。这项研究将实现实用的基于图像的生物传感，并在医疗保健中使用更广泛的应用。该框架可以广泛适用于许多涉及循环中人类的医学CPS问题，包括上肢和下肢假体和外骨骼，康复和外科机器人。依赖传感器测量的新的控制分配算法在容忍和冗余的执行器系统中可能具有更广泛的适用性，并且对无人机的可靠耐受性控制。