HCC: Medium: Collaborative Research: Neural Control of Powered Artificial Legs

HCC：媒介：合作研究：动力假腿的神经控制

• 批准号: 1302339
• 负责人: Jose Contreras-Vidal
• 金额: $ 60万
• 依托单位: University of Houston
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-06-15 至 2019-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1302339&HistoricalAwards=false
• 关键词: HCC; Medium; Collaborative; Research; Neural

Recent breakthroughs in the mechatronics of powered lower limb (LL) prostheses hold the promise of enabling restoration for the large and growing population of lower limb amputees of a broad spectrum of functionality (e.g., standing up when seated in a chair, climbing stairs, and even running). The PIs argue that to realize this potential it is essential to provide neural control of artificial legs. The application of existing upper limb (UL) neural control approaches is inappropriate to this end, because the UL and LL neural control mechanisms are significantly different. In particular, most activities involving the lower limbs recruit both involuntary (spinal cord) and voluntary (supra-spinal) neural control, present high dynamics, and require multi-joint coordination and control of unstable locomotion, characteristics which combine to make the design specifications for neural control of LL prostheses much more demanding than those for UL devices. In this project the PIs will address this challenge by developing an innovative neural control system for powered artificial legs that can recognize and exploit multi-scale user intent (e.g., general motor commands such as intended task vs. detailed motor commands such as intended joint motion) to modulate intrinsic (autonomous) control of multiple LL prosthetic joints for locomotor and nonlocomotor task performance. The goals are to support reverse-engineering of the neural control of human locomotion while creating innovative neural-machine interfacing (NMI) technology that enables users to control the dynamics of LL prostheses in a natural, adaptive and flexible way. Inspired by what is currently known about the neurological organization and function of the human motor control system, the PIs' approach is to design a novel NMI based on a combination of noninvasive scalp electroencephalography (EEG) and surface electromyography (EMG). The hypothesis is that fusion of low-level peripheral and high-level central neural control sources can achieve multi-scale user intent recognition with higher accuracy and more rapid response time than can be realized with either EEG or EMG alone. A hierarchical control scheme for powered LL prostheses, in which multi-scale user intent identified by the NMI modulates intrinsic (autonomous) control, will support intuitive and efficient prosthesis use in dynamic, multi-joint coordinated movements while significantly reducing the mental burden of the prosthesis user in locomotion because the cyclic motion is achieved autonomously (this is desired because we rarely think about knee and ankle control when walking). The PIs will also explore correlation across EEG and EMG signals, which may provide insight into neural adaption and the time course of cortical control during the initiation and generation of gait, including how the brain initiates walking and regulates motor output in anticipation of key events such as foot placement at landing or during stepping up and down, weight acceptance, and push-off into swing phase. Finally, the PIs will use translational research to validate their novel approach in patients with trans-femoral amputations (a high and challenging amputation level).Broader Impacts: The PIs' long-term objective is to develop true bionic prostheses that feel and work just like real legs. Their approach in this project represents a paradigm shift in the control of lower limb wearable prosthetics. As such, project outcomes will directly impact both the Human-Robot Interaction and Brain-Machine Interface research communities. The findings will also be relevant to the neuroscience and rehabilitation communities, in that they will help elucidate the adaptive spinal cord and cortical contributions to human locomotion, while providing innovative and functional neuro-prosthetics solutions to improve the lives of lower limb amputees.

动力下肢（LL）假体的机电一体化的最新突破有望使大量且不断增长的下肢截肢者能够恢复广泛的功能（例如，坐在椅子上时站起来，爬楼梯，甚至跑步）。 PI认为，要实现这一潜力，必须提供人工腿的神经控制。 现有的上肢（UL）神经控制方法的应用是不合适的，因为UL和LL神经控制机制显着不同。 特别是，涉及下肢的大多数活动招募非自愿（脊髓）和自愿（脊髓上）神经控制，呈现高动态，并需要多关节协调和不稳定运动的控制，这些特征联合收割机结合起来，使LL假体的神经控制的设计规范比UL设备的要求高得多。 在这个项目中，PI将通过开发一种创新的神经控制系统来应对这一挑战，该系统用于动力人工腿，可以识别和利用多尺度用户意图（例如，一般的运动命令，例如预期的任务与详细的运动命令，例如预期的关节运动），以调节多个LL假肢关节的内在（自主）控制，用于运动和非运动任务性能。 目标是支持人类运动神经控制的逆向工程，同时创建创新的神经-机器接口（NMI）技术，使用户能够以自然，自适应和灵活的方式控制LL假肢的动力学。 受目前已知的人类运动控制系统的神经组织和功能的启发，PI的方法是设计一种基于非侵入性头皮脑电图（EEG）和表面肌电图（EMG）组合的新型NMI。 该假设是，低级别外围和高级别中枢神经控制源的融合可以实现多尺度用户意图识别，其具有比单独使用EEG或EMG所实现的更高的准确度和更快的响应时间。 动力LL假体的分层控制方案，其中由NMI识别的多尺度用户意图调制固有的（自主）控制，将支持直观和有效的假体使用动态，多关节协调运动，同时由于循环运动是自主实现的，（这是期望的，因为我们很少考虑走路时的膝盖和脚踝控制）。 PI还将探索EEG和EMG信号之间的相关性，这可能有助于了解步态启动和生成期间的神经适应和皮层控制的时间过程，包括大脑如何启动行走并调节运动输出，以预测关键事件，例如着陆时或上下踏步期间的脚放置，重量接受和推离进入摆动阶段。 最后，PI将使用转化研究来验证他们在经股截肢患者中的新方法（截肢水平高且具有挑战性）。更广泛的影响：PI的长期目标是开发真正的仿生假肢，其感觉和工作就像真实的腿一样。 他们在这个项目中的方法代表了下肢可穿戴假肢控制的范式转变。 因此，项目成果将直接影响人机交互和脑机接口研究社区。 这些发现也将与神经科学和康复社区相关，因为它们将有助于阐明自适应脊髓和皮质对人类运动的贡献，同时提供创新和功能性的神经修复解决方案，以改善下肢截肢者的生活。