NRI: Novel Prosthetic Arm Control Based on a Low-Dimensional Internal Musculoskeletal Biomechanical (LIMB) Model

NRI：基于低维内部肌肉骨骼生物力学 (LIMB) 模型的新型假肢控制

• 批准号: 1527202
• 负责人: He Huang
• 金额: $ 87.94万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1527202&HistoricalAwards=false
• 关键词: NRI; Novel; Prosthetic; Arm; Control

Upper limb amputation is a major cause of disability for nearly 160,000 Americans, many of whom could benefit from emergent sophisticated robotic, multifunctional prosthetic arms/hands. In these advanced prostheses, movements are typically controlled by interpreting the user's electromyographic (EMG) signals from residual or reinnervated muscles. State-of-the-art pattern recognition (PR) has been the most promising EMG control interface for multifunctional artificial arms. However, EMG PR-based control algorithms often require lengthy and frequent algorithm training and lack reliability when the external loading or arm posture changes. This is partly because EMG PR is data-driven and does not account for the behavior of the underlying neural or biomechanical system from which the EMG signals are sourced. The objective of this project is to develop a novel EMG control of multifunctional transradial (TR) prostheses based on a systematic study of neuromuscular control and biomechanical roles of residual muscles in TR amputees. This research can potentially enhance the health, function, and quality of life of upper limb amputees. This project's concept, methods, and frameworks for enhancing EMG-based prosthesis control may be extended to other assistive robotics to benefit other patient populations such as stroke survivors. This project will impact STEM education by promoting project-based cross-training among K-12, undergraduate, and graduate students in underrepresented groups including females, minorities, and students with disabilities. The research may also impact the neuroscience and movement science communities by elucidating the control mechanism of the arm/hand and unveiling new knowledge of neuroplasticity and the internal model in upper limb amputees. At the core of the multifunctional prosthesis control is a musculoskeletal model of the missing limb that will be used to interpret intended joint motions from EMG signals. The intellectual merit of this project includes a new concept for the control of robotic, multifunctional prosthetic arms/hands. The PIs' musculoskeletal model-based interface is fundamentally different from existing data-driven, EMG PR-based control because it interprets EMG signals and decodes user movement intent in a more biological way. Additionally, this effort will result in new knowledge regarding the neuroplasticity, neuromuscular control, and perceived biomechanical roles of residual muscles in upper limb amputees, which has not been systematically investigated based on the investigators' knowledge. Ultimately, the project will result in a new prosthesis control that, compared to state-of-the-art EMG PR-based control, may require significantly fewer and shorter calibrations, provide more intuitive, robust control (against posture changes, external loading, etc.), and enable multi-joint coordinated prosthesis operations. The investigators expect that the research may transform the way in which upper limb amputees operate multifunctional prostheses in daily life.

上肢截肢是近 16 万美国人残疾的主要原因，其中许多人可以从新兴的复杂机器人、多功能假肢/手中受益。在这些先进的假肢中，通常通过解释来自残余或重新神经支配的肌肉的用户肌电图 (EMG) 信号来控制运动。最先进的模式识别（PR）已成为多功能人工手臂最有前途的肌电图控制接口。然而，基于EMG PR的控制算法往往需要长时间且频繁的算法训练，并且在外部负载或手臂姿势变化时缺乏可靠性。部分原因是 EMG PR 是数据驱动的，没有考虑 EMG 信号来源的基础神经或生物力学系统的行为。该项目的目标是基于对TR截肢者残余肌肉的神经肌肉控制和生物力学作用的系统研究，开发一种新型的多功能经桡动脉（TR）假肢的肌电图控制。这项研究有可能增强上肢截肢者的健康、功能和生活质量。该项目用于增强基于肌电图的假肢控制的概念、方法和框架可以扩展到其他辅助机器人技术，以造福其他患者群体，例如中风幸存者。该项目将通过促进 K-12、本科生和研究生（包括女性、少数族裔和残疾学生）中代表性不足的群体中基于项目的交叉培训来影响 STEM 教育。该研究还可能通过阐明手臂/手的控制机制并揭示上肢截肢者的神经可塑性和内部模型的新知识来影响神经科学和运动科学界。多功能假肢控制的核心是缺失肢体的肌肉骨骼模型，该模型将用于解释肌电图信号的预期关节运动。该项目的智力优势包括控制机器人、多功能假肢手臂/手的新概念。 PI 基于肌肉骨骼模型的界面与现有的数据驱动、基于 EMG PR 的控制有着根本的不同，因为它以更生物学的方式解释 EMG 信号并解码用户的运动意图。此外，这项工作还将产生关于上肢截肢者残余肌肉的神经可塑性、神经肌肉控制和感知生物力学作用的新知识，这些知识尚未根据研究人员的知识进行系统研究。最终，该项目将产生一种新的假肢控制，与最先进的基于 EMG PR 的控制相比，可能需要更少和更短的校准，提供更直观、更稳健的控制（针对姿势变化、外部负载、等），并实现多关节协调假肢操作。研究人员预计，这项研究可能会改变上肢截肢者在日常生活中操作多功能假肢的方式。

项目成果

Reliable Vision-Based Grasping Target Recognition for Upper Limb Prostheses

• DOI: 10.1109/tcyb.2020.2996960
• 发表时间: 2022-03-01
• 期刊: IEEE TRANSACTIONS ON CYBERNETICS
• 影响因子: 11.8
• 作者: Zhong, Boxuan;Huang, He;Lobaton, Edgar
• 通讯作者: Lobaton, Edgar