Towards Neural Control of Artificial Legs: Design of a Real-Time Fusion-based Neu

走向假腿的神经控制：基于实时融合的神经元的设计

基本信息

批准号：
8059600
负责人：
He Huang
金额：
$ 22.09万
依托单位：
UNIVERSITY OF RHODE ISLAND
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-04-15 至 2013-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8059600
关键词：
Activities of Daily Living Adopted Algorithms Amputation Amputees Ankle Architecture Artificial Leg Back Brain Cells Classification Consumption Cues Data Detection Development Electromyography Ensure Evaluation Feedback Goals Grant Human Impairment Kinetics Knee Lead Leg Limb structure Locomotion Lower Extremity Measurement Mechanics Memory Methods Monitor Motion Movement Patients Pattern Recognition Performance Persons Phase Process Property Proprioception Prosthesis Qualifying Quality of life Reaction Time Recovery Recruitment Activity Research Research Personnel Safety Sensory Signal Transduction Simulate Stream Structure Surface Surgical Disarticulation System Systems Development Testing Time Walking base computerized design detector disability experience falls feeding improved improved functioning information gathering instrument kinematics knowledge base neuromuscular neuroregulation novel operation pressure programs prototype public health relevance relating to nervous system research study response sensor success

项目摘要

DESCRIPTION (provided by applicant): Advances in computerized and powered artificial legs show great promise to permit persons with lower limb amputations to perform versatile activities beyond level ground walking. These prostheses are, however, inadequate for users to perform seamless transitions between activities due to the lack of neural control. To "tell" the prosthesis the intended movement, the user must make extra body motions or use a remote key fob, which are both cumbersome and not robust. Obtaining decisions directly from the user through a neural control interface is crucial to providing accurate, intuitive control of computerized artificial legs. Our long-term goal is to develop a neural-controlled artificial knee and/or ankle to improve the function of computerized artificial legs and the quality of life of people with lower limb amputations. Towards this goal, we propose to develop a robust neural-machine interface that can recognize the user's intended lower limb tasks in real-time. A functional, embedded neural interfacing system will be delivered at the end of this project that may start a complete paradigm shift in the design of computerized artificial legs. The specific aims of this grant are: Aim 1: Develop a neural interface algorithm that accurately and responsively decodes the user's intended lower limb tasks and task transitions. Aim 2: Implement the algorithm designed in Aim 1 on real-time embedded hardware. Aim 3: Evaluate the real-time neural interfacing system on subjects with knee disarticulation or transfemoral amputations. We propose a neural-mechanical-fusion-based interfacing design for the development of the algorithm (Aim 1). The algorithm will integrate the neuromuscular control information gathered through electromyographic (EMG) recordings with mechanical feedback from the prosthesis to achieve improved accuracy for identifying user intent. A phase-dependent pattern recognition strategy is proposed to ensure a fast system time response for real-time application. Additional components such as sensor fault detectors and a finite-state machine will be designed to enhance the system robustness. The designed algorithm will be implemented on real-time testing hardware composed of a self-constrained instrumented leg and an embedded system (Aim 2). The data structures and programs will be optimized to make the best use of the embedded architecture and the multilevel memory hierarchy for real-time operation. The finalized real-time neural- machine interface will be evaluated on patients with knee disarticulation or transfemoral amputations, which are high and challenging levels (Aim 3). PUBLIC HEALTH RELEVANCE: The neural-machine interface developed for neural control of artificial legs will lead to improved functional usage of impaired limbs, reduced disability, and improved quality of life of patients with lower limb amputations.

描述（由申请人提供）：计算机化和动力的人造腿的进步表现出巨大的希望，可以允许下肢截肢的人进行超越地面步行的多功能活动。但是，由于缺乏神经控制，这些假体不足以在活动之间进行无缝过渡。要“告诉”预期运动的假体，用户必须进行额外的身体动作或使用远程钥匙fob，这既麻烦又不健壮。直接通过神经控制界面从用户获得决策对于提供计算机人造腿的准确，直观的控制至关重要。我们的长期目标是开发神经控制的人造膝盖和/或脚踝，以改善计算机人造腿的功能以及下肢截肢的人的生活质量。为了实现这一目标，我们建议开发一个强大的神经机器接口，该接口可以实时识别用户的下肢任务。在该项目的末尾，将传递功能性的，嵌入式的神经接口系统，可能会在计算机人造腿的设计中开始完全范式转移。该赠款的具体目的是：目标1：开发一种神经接口算法，该算法准确响应地解码用户预期的下肢任务和任务转换。 AIM 2：在AIM 1中实现在实时嵌入式硬件上设计的算法。 AIM 3：评估具有膝关节脱节或经济截肢的受试者的实时神经接口系统。我们为开发算法提出了一种基于神经机械融合的接口设计（AIM 1）。该算法将整合通过肌电图（EMG）记录收集的神经肌肉控制信息，并与假体的机械反馈相结合，以提高准确性以识别用户意图。提出了相关模式识别策略，以确保实时应用的快速系统时间响应。其他组件（例如传感器故障探测器和有限状态机器）将设计以增强系统的鲁棒性。该设计的算法将用于实时测试的硬件，该硬件由自由的仪器腿和嵌入式系统组成（AIM 2）。将优化数据结构和程序，以充分利用嵌入式体系结构以及多级内存层次结构进行实时操作。最终的实时神经机界面将对膝关节脱节或瞬间截肢的患者进行评估，这些患者高且具有挑战性的水平（AIM 3）。公共卫生相关性：开发用于人造腿神经控制的神经机界面将导致肢体受损，残疾减少以及下肢截肢患者的生活质量改善的功能使用。