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

电动下肢假体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，坐在椅子上站立、爬楼梯，甚至跑步)。PI认为，为了实现这一潜力，提供人工腿的神经控制是必不可少的。现有的上肢神经控制方法在这方面的应用是不合适的，因为上肢和上肢的神经控制机制有很大的不同。特别是，大多数涉及下肢的活动同时招募非自愿(脊髓)和自愿(脊髓上)神经控制，呈现高动力学，需要多关节协调和控制不稳定的运动，这些特点结合在一起，使得LL假体的神经控制设计规范比UL假体的设计规范要求更高。在这个项目中，PI将通过开发一种用于电动假肢的创新神经控制系统来应对这一挑战，该系统可以识别和利用多尺度用户意图(例如，预期任务等一般运动命令与预期关节运动等详细运动命令)，以调节对多个LL假肢关节的内在(自主)控制，以实现运动和非运动任务的性能。其目标是支持人类运动神经控制的逆向工程，同时创造创新的神经-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假体的动力学。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(EEG)和表面肌电(EMG)的新型NMI。假设融合低水平的外周神经控制源和高水平的中枢神经控制源，可以实现多尺度的用户意图识别，比单独使用脑电或肌电可以实现更高的准确率和更快的反应时间。一种用于动力型LL假体的分级控制方案，其中由NMI识别的多尺度用户意图调节固有(自主)控制，将支持在动态、多关节协调运动中直观和高效地使用假体，同时显著减轻假体使用者在运动中的心理负担，因为循环运动是自主实现的(这是所期望的，因为我们在行走时很少考虑膝盖和脚踝控制)。PI还将探索EEG和EMG信号之间的相关性，这可能提供对步态开始和生成期间的神经适应和皮质控制的时间过程的洞察，包括大脑如何启动行走和调节运动输出，以预测关键事件，如落地或上下踏步时的脚部放置、体重接受和推入摆动阶段。最后，PI将使用转译研究来验证他们的新方法在经股截肢(一个高而具有挑战性的截肢水平)患者中的有效性。广泛影响：PI的长期目标是开发感觉和工作方式与真实腿一样的真正仿生假体。他们在这个项目中的方法代表了对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神经假体解决方案，以改善下肢截肢者的生活。

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.