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）：计算机化和动力式假腿的进步显示出巨大的希望，可以让下肢截肢的人进行平地行走以外的多种活动。然而，由于缺乏神经控制，这些假肢不足以让用户在活动之间执行无缝转换。为了“告诉”假肢预期的运动，用户必须做出额外的身体动作或使用遥控钥匙，这既麻烦又不坚固。通过神经控制接口直接从用户那里获得决策对于提供计算机化假腿的准确、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工膝关节和/或踝关节，以改善计算机化假腿的功能和下肢截肢患者的生活质量。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，可以实时识别用户预期的下肢任务。该项目结束时将交付一个功能性的嵌入式神经接口系统，这可能会开始计算机化假腿设计的彻底范式转变。该资助的具体目标是：目标 1：开发一种神经接口算法，能够准确、快速地解码用户预期的下肢任务和任务转换。目标 2：在实时嵌入式硬件上实现目标 1 中设计的算法。目标 3：评估膝关节离断或股骨截肢受试者的实时神经接口系统。我们提出了一种基于神经机械融合的接口设计来开发算法（目标 1）。该算法将通过肌电图（EMG）记录收集的神经肌肉控制信息与假肢的机械反馈相结合，以提高识别用户意图的准确性。提出了一种相位相关的模式识别策略，以确保实时应用的快速系统时间响应。将设计传感器故障检测器和有限状态机等附加组件来增强系统的鲁棒性。设计的算法将在由自约束仪器腿和嵌入式系统组成的实时测试硬件上实现（目标 2）。数据结构和程序将被优化，以充分利用嵌入式架构和多级内存层次结构进行实时操作。最终确定的实时神经机器接口将在膝关节离断或股骨截肢患者身上进行评估，这些患者的水平较高且具有挑战性（目标 3）。公共健康相关性：为人工腿的神经控制而开发的神经机器接口将改善受损肢体的功能使用，减少残疾，并提高下肢截肢患者的生活质量。