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.

项目概要 上肢截肢会使许多基本任务变得困难或几乎不可能。 可以根据残肢的肌电图（EMG）信号预测运动意图的算法已经导致 允许控制多个自由度 (DOF) 的假手的开发并恢复了 一些最先进的商用假手的基本功能。 使用基于机器学习的控制方案，称为 EMG 模式识别 (PR)。 方法预测运动类别（例如，手打开/关闭）并设置与运动成正比的电机速度 一些新提出的方法涉及同时控制 EMG 信号的位置。 然而，所有这些控制方案都允许用户对施加到物体上的力进行最小程度的控制。 由假手握住，这使得握住和运输易碎物品变得困难。 该项目的总体目标是开发一种新颖的控制方案，允许同时控制 经桡动脉假体的多个自由度的位置以及物体移动时的握力控制 为实现这一目标，本提案包括以下2点。 目标：1）开发一种新颖的共享控制框架，用于实时上肢假肢控制和抓取 2）评估共享控制框架的性能和认知工作量。 使用人工神经网络 (ANN) 将 EMG 信号的特征映射到关节扭矩和前向动力学 将从受试者和强化中收集计算关节运动学和关节运动数据的模型。 学习算法将用于训练人工神经网络，以最小化估计和测量的关节之间的误差 连接到假手指尖的力传感器将检测何时与物体接触。 已制作并测量掌指（MCP）关节的估计扭矩。 用于估计所需的握力，PID 控制器会将测量到的握力驱动至所需的握力 为了评估该框架，将使用虚拟任务来测试受试者控制握力的能力。 然后，受试者将使用两者。 共享控制器和 EMG PR 完成涉及运输易碎/可变形的 2 项功能任务 任务将在有和没有脑力要求较高的双重任务的情况下完成，以及差异。 表现将用于估计认知负荷。 这项拟议的工作预计将引入一种控制经桡动脉假体的方法，该方法提供可靠的 基于位置的多自由度控制以及精确控制假手对物体施加的握力 这种方法可以减少抓握物体的难度和心理需求。 任务并导致动力上肢假肢的接受率更高。