A Hybrid Neural-Machine Interface for Volitional Control of a Powered Lower Limb Prosthesis

用于动力下肢假肢意志控制的混合神经机器接口

• 批准号: 10269941
• 负责人: Justin Alexander Brantley
• 金额: $ 7.26万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-28 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10269941
• 关键词: Activities of Daily Living; Affect; Amputees; Ankle; Artificial Intelligence; Award; BRAIN initiative; Bone Diseases; Brain; Brain imaging; Chronic; Communication; Complex; Detection; Development; Electroencephalography; Electromyography; Electronic Mail; Energy Metabolism; Engineering; Frequencies; Funding Agency; Future Teacher; Gait; Gait abnormality; Goals; Grant; Health; Human; Hybrids; Image; Imaging Techniques; Imaging technology; Impairment; Individual; Injury; Intelligence; International; Intuition; Joints; Knee; Knowledge; Leadership; Limb Prosthesis; Link; Locomotion; Lower Extremity; Machine Learning; Measures; Medical; Medical Technology; Mentors; Mentorship; Metabolic; Methods; Mission; Motion; Movement; Muscle; Nervous System Trauma; Neurologic; Neurologic Effect; Neurosciences; Oral; Orthotic Devices; Patients; Pattern; Performance; Population; Positioning Attribute; Prosthesis; Public Health; Ramp; Research; Research Project Grants; Residual state; Robotics; Scalp structure; Signal Transduction; Social Network; Source; Surface; System; Teacher Professional Development; Technical Expertise; Technology; Telephone; Time; Training Programs; Translating; United States; United States National Institutes of Health; Upper Extremity; Visual Cortex; Volition; Walking; Work; arthropathies; base; clinically significant; exhaustion; experience; fall risk; improved; innovation; instrument; kinematics; limb amputation; limb movement; multimodality; neural correlate; neurological rehabilitation; neuroregulation; neurotransmission; novel strategies; physically handicapped; post stroke; posters; powered exoskeleton; powered prosthesis; prediction algorithm; programs; rehabilitation paradigm; relating to nervous system; robot control; robot exoskeleton; robot rehabilitation; robotic system; sensor; signal processing; skills; stroke rehabilitation; symposium; synergism; undergraduate student; visual motor

Project Summary The goal of the proposed work is to develop a robust hybrid neural-machine interface (NMI), combining brain and muscle signals, to improve overall control of a lower limb prosthetic device during activities of daily living. Limb amputation affects over 600,000 individuals annually in the US, and is a major cause of physical disability that causes activities of daily living to become difficult or impossible for the amputee. The limitations of current lower-limb prostheses are associated with limited volitional control, reduced mobility, and chronic gait abnormalities, which have been linked to exhaustion from increased energy expenditure, increased risk of falling, and degenerative bone and joint disorders in both the intact and amputated limb. In this study, EMG signals from both residual and intact lower limbs and EEG signals from the cortex are leveraged to decode transitions to and from various modes of locomotion modes in able-bodied individuals and transfemoral amputees, and to provide a global understanding of movement at the cortical, muscular, and kinematic level in amputees. Specifically, time and frequency domain features are leveraged to create a prediction algorithm capable of identifying upcoming terrain transitions in advance. In lower limb amputees, this hybrid NMI paradigm translates to volitional control of a powered lower-limb prosthesis, which allows for seamless transitions between various movement conditions. The high-level of control is expected to result in significant increases in level of activity and overall improvements in gait. Previous studies have demonstrated the feasibility of EEG or EMG based NMIs for orthotic and prosthetic devices; however, no study to date has integrated EEG and EMG in a NMI for powered lower limb prostheses. This study is motivated by the need to explore advanced neural control sources for intuitive control of artificial limbs. This project aligns directly with the Mission & Goals of the NIH, the Brain Initiative, and NIH’s Blueprint Program by expanding fundamental knowledge of neuroscience, human, health and wellness; by utilizing an innovative research strategy; and ultimately returning the knowledge to the public through the development of a highly advanced medical technology. Furthermore, the technology developed through this work has implications beyond the amputee population in the treatment of many neurological conditions and injuries, such as in neurorehabilitation after stroke. The innovation of this project lies in the novel approach of using multimodal neural signals and movement synergies as a framework for interpreting movement of the lower limb. The scientific impact is realized by a greater understanding of the neural correlates of movement after lower-limb amputation. The direct clinical significance for the patient can be measured directly through improved gait performance and walking confidence, leading to increased mobility and a reduced risk of falling, exhaustion, and bone and joint disorders.

项目摘要 拟议工作的目的是开发强大的混合神经机界面（NMI）， 结合大脑和肌肉信号，以改善对下肢假肢的总体控制 日常生活的活动。肢体截肢每年在美国影响超过60万人，并且是主要的 肢体残疾的原因导致日常生活的活动变得困难或不可能。 当前下LIMB假体的局限性与有限的意志控制，迁移率降低有关， 和慢性步态异常，与能量消耗增加有关 在完整和截肢的肢体中，降落的风险增加，骨和关节疾病的变性。在这个 研究，将来自残留和完整下肢的EMG信号和来自皮质的脑电图信号均被杠杆化为 在能力的个体和经济型中，往返各种运动模式的转变和从各种模式 截肢者，并在皮质，肌肉和运动学水平的运动中提供全球理解 amputees。特别是，利用时间域和频域特征来创建预测算法 能够事先确定即将进行的地形过渡。在下肢截肢者中，这种混合NMI范式 转化为对下限下限假体的自愿控制，这使得无缝过渡 各种运动条件。预计高水平的控制将导致显着增加 步态的活动和整体改进。先前的研究表明脑电图或EMG的可行性 基于矫形器和假肢设备的NMI；但是，迄今为止尚无研究将脑电图和EMG整合在 NMI用于下肢的动力下肢。这项研究是由于需要探索先进的神经控制的动机 直观控制人造四肢的来源。 该项目直接与NIH的使命和目标保持一致，大脑倡议和NIH的蓝图 通过扩大神经科学，人，健康和健康的基本知识来计划；通过使用 创新研究策略；并最终通过发展将知识归还给公众 高级医疗技术。此外，通过这项工作开发的技术有含义 除了截肢者人群以外的许多神经系统疾病和伤害，例如 中风后神经康复。该项目的创新在于使用多模式的新方法 神经信号和运动协同作用是解释下肢运动的框架。这 科学影响是通过对下限后运动的神经元相关性的更多了解来实现的 截肢。可以通过改进的步态直接测量患者的直接临床意义 性能和步行信心，导致流动性提高，降低，疲惫和疲惫的风险降低 骨骼和关节疾病。