Development of a Novel EMG-Based Neural Interface for Control of Transradial Prostheses with Gripping Assistance

开发一种新型的基于肌电图的神经接口，用于通过抓取辅助控制经桡动脉假体

• 批准号: 10748341
• 负责人: Joseph Berman
• 金额: $ 4.12万
• 依托单位: NORTH CAROLINA STATE UNIVERSITY RALEIGH
• 依托单位国家: 美国
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10748341
• 关键词: Algorithms; Amputation; Amputees; Back; Calibration; Cognitive; Data; Data Collection; Development; Devices; Dryness; Electrodes; Electromyography; Equation; Forms Controls; Freedom; Goals; Hand; Hand functions; Hybrids; Individual; Intuition; Joints; Learning; Limb Prosthesis; Location; Machine Learning; Maps; Measures; Metacarpophalangeal joint structure; Methods; Modeling; Monitor; Motion; Motor; Muscle; Musculoskeletal; Outcome; Paper; Patients; Pattern Recognition; Performance; Positioning Attribute; Prosthesis; Psyche structure; Psychological reinforcement; Public Health; Quality of life; Radial; Randomized; Research; Research Personnel; Residual state; Running; Scheme; Signal Transduction; Task Performances; Techniques; Testing; Time; Torque; Training; Update; Upper Extremity; Validation; Variant; Work; Workload; artificial neural network; cognitive load; computer monitor; force sensor; grasp; hand grasp; improved; kinematics; learning algorithm; limb amputation; motor control; neural; novel; operation; powered prosthesis; prosthesis control; prosthetic hand; prototype; residual limb; sensor; skills; spelling; transradial amputee; usability; virtual

PROJECT SUMMARY An upper limb amputation can make many basic tasks difficult or nearly impossible. In recent years, research in algorithms that can predict motion intentions from electromyographic (EMG) signals of a residual limb has led to the development of prosthetic hands that allow control of multiple degrees of freedom (DOF) and has restored the basic functionality of an upper-limb. Some of the most advanced commercially available prosthetic hands use a machine learning-based control scheme known as EMG pattern recognition (PR). Many EMG PR approaches predict a motion class (e.g. hand open/close) and set the velocity of the motors proportional to the magnitude of EMG signals. Some new proposed approaches involve simultaneously controlling the position of multiple DOF. However, all of these control schemes allow users minimal control of the force applied to objects grasped by the prosthetic hand which makes holding and transporting fragile objects difficult. The overall objective of this project is to develop a novel control scheme that allows simultaneous control of the positions of multiple DOF of a transradial prosthesis as well as control of grip force when an object has made contact with the fingertips of the prosthesis. To achieve this objective, this proposal consists of the following 2 aims: 1) Develop a novel shared control framework for real-time upper limb prosthesis control and gripping and 2) Evaluate the performance and cognitive workload of the shared control framework. The shared controller will use an artificial neural network (ANN) to map the features of EMG signals to joint torque and a forward dynamics model to calculate joint kinematics. EMG and joint motion data will be collected from subjects and a reinforcement learning algorithm will be used to train the ANN to minimize the error between estimated and measured joint positions. A force sensor attached to the fingertip of a prosthetic hand will detect when contact with an object has been made and measure the grip force. The estimated torque of the metacarpophalangeal (MCP) joint will be used to estimate a desired grip force and a PID controller will drive the measured grip force to this desired grip force. To evaluate the framework, a virtual task will be used to test a subjects’ ability to control the grip force of the hand by having them follow a given force trajectory displayed on a monitor. Then, subjects will use both the shared controller and EMG PR to complete 2 functional tasks involving transporting fragile/deformable objects. Tasks will be completed with and without a mentally demanding dual task and the differences in performance will be used to estimate cognitive loads. This proposed work is expected to introduce a method of controlling transradial prostheses that provides reliable position-based control of multiple DOF and precise control of the grip force the prosthetic hand applies to objects with various levels of compliance. This method can reduce the difficulties and mental demands of object grasping tasks and lead to a higher acceptance rate of powered upper limb prostheses.

项目摘要 上肢截肢会使许多基本任务变得困难或几乎不可能。近年来，研究 可以预测残留肢体肌电图（EMG）的运动意图的算法已导致 假肢的发展，允许控制多个自由度（DOF）并恢复 上限的基本功能。一些最先进的市值假肢手 使用基于机器学习的控制方案，称为EMG模式识别（PR）。许多EMG PR 方法可以预测运动类（例如，手开/关闭），并设置电动机的速度成正比 EMG信号的大小。一些新提出的方法涉及简单地控制 多个DOF。但是，所有这些控制方案允许用户最小的控制对象的力量 被假肢的手抓住，这使握住和运输脆弱的物体变得困难。 该项目的总体目的是制定一种新颖的控制方案，允许对 当物体制成的物体时，跨放体的多个DOF的位置以及对握力的控制 与假体的指尖接触。为了实现这一目标，该提议包括以下2 目的：1）为实时上肢假体控制和磨削和磨削和 2）评估共享控制框架的性能和认知工作量。共享控制器将 使用人工神经网络（ANN）将EMG信号的特征映射到关节扭矩和正向动力学 计算关节运动学的模型。 EMG和联合运动数据将从受试者和加固中收集 学习算法将用于训练ANN，以最大程度地减少估计和测量的关节之间的误差 位置。连接到假肢的指尖的力传感器将检测到与物体接触时 已经制作并测量握力。掌pophangeal（MCP）关节的估计扭矩将 用于估计所需的握力，而PID控制器将驱动测得的握力到达所需的 握力。为了评估框架，将使用虚拟任务来测试受试者控制握力的能力 通过让他们遵循显示器上显示的给定力轨迹的手。然后，受试者将同时使用 共享控制器和EMG PR完成2个功能任务，涉及运输脆弱/可变形 对象。任务将在有或没有精神要求的双重任务和差异的情况下完成 性能将用于估计认知负荷。 预计这项拟议的工作将引入一种控制跨放体的方法，以提供可靠的 基于位置的多重DOF控制和对握力的精确控制假肢适用于物体 具有不同级别的合规性。此方法可以减少对象抓住的难度和心理需求 任务并导致较高的上肢假体的接受率。