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

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臂/手中受益。在这些先进的假肢中，运动通常是通过解读来自残余或再神经支配肌肉的用户肌电图（EMG）信号来控制的。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控制接口。然而，基于肌电电位的控制算法往往需要长时间和频繁的算法训练，并且当外部负载或手臂姿势发生变化时缺乏可靠性。这部分是因为肌电图PR是数据驱动的，并不能解释肌电图信号来源的潜在神经或生物力学系统的行为。本项目的目的是在系统研究经桡骨假肢残肌的神经肌肉控制和生物力学作用的基础上，开发一种新的肌电控制功能。这项研究有可能提高上肢截肢者的健康、功能和生活质量。该项目的概念、方法和框架可以用于增强基于肌电图的假肢控制，并扩展到其他辅助机器人，以造福其他患者群体，如中风幸存者。该项目将通过在女性、少数民族和残疾学生等代表性不足的群体中促进基于项目的K-12、本科生和研究生的交叉培训，影响STEM教育。该研究还可能通过阐明手臂/手的控制机制，揭示上肢截肢者神经可塑性和内部模型的新知识，对神经科学和运动科学界产生影响。多功能假肢控制的核心是缺失肢体的肌肉骨骼模型，该模型将用于从肌电图信号中解释预期的关节运动。这个项目的智力价值包括一个控制机器人的新概念，多功能假肢手臂/手。pi基于肌肉骨骼模型的接口与现有的数据驱动、基于肌电信号pr的控制有着根本的不同，因为它可以解释肌电信号，并以一种更生物的方式解码用户的运动意图。此外，这一努力将产生关于神经可塑性、神经肌肉控制和上肢截肢者残余肌肉感知生物力学作用的新知识，这些知识尚未根据研究者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），并实现多关节协调的假肢操作。研究人员期望这项研究可以改变上肢截肢者在日常生活中操作多功能假肢的方式。

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.