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，其中许多人可以从紧急复杂的机器人多功能假肢/手中受益。在这些先进的假肢中，通常通过解释来自残余或神经再支配肌肉的用户的肌电图（EMG）信号来控制运动。最先进的模式识别（PR）已成为最有前途的肌电信号控制接口的多功能人工手臂。然而，基于EMG PR的控制算法通常需要冗长且频繁的算法训练，并且在外部负载或手臂姿势改变时缺乏可靠性。这部分是因为EMG PR是数据驱动的，并且不考虑EMG信号所来源的底层神经或生物力学系统的行为。本项目的目的是开发一种新的肌电控制的多功能经桡动脉（TR）假肢的神经肌肉控制和生物力学作用的TR截肢者的系统研究的基础上。这项研究可能会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，以提高基于EMG的假肢控制可能会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。该项目将通过促进K-12，本科生和研究生中代表性不足的群体（包括女性，少数民族和残疾学生）基于项目的交叉培训来影响STEM教育。这项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，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，该模型将用于从EMG信号解释预期的关节运动。该项目的智力价值包括控制机器人多功能假肢/手的新概念。PI的基于肌肉骨骼模型的界面与现有的数据驱动的基于EMG PR的控制有着根本的不同，因为它以更生物的方式解释EMG信号并解码用户运动意图。此外，这一努力将导致关于上肢截肢者中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，这些知识尚未基于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，可能需要显著更少和更短的校准，提供更直观，更强大的控制（针对姿势变化，外部负载等），并且能够进行多关节协调的假体操作。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。

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.