CHS: Medium: Collaborative Research: Collaborative Online Learning and Control for Motor Prosthesis

据估计，美国约有540万人患有某种形式的瘫痪，定义为导致上肢和/或下肢移动困难或无法移动的中枢神经系统疾病。许多瘫痪的人认为恢复失去的基本运动功能，如抓握和行走，是提高生活质量的重要能力。该项目的目标是开发和评估先进的机器学习算法，使四肢瘫痪的人能够用异源肌肉（即通常不参与肢体运动的肌肉;例如颈部肌肉）控制机器人手。为了实现这项研究，前瞻性算法将在正常启用的个体中进行初步评估。最有前途的算法将随后在四肢瘫痪的个人进行评估。这项研究是为瘫痪社区提供福利的第一步，为功能性运动假肢系统的开发和商业化创造了途径。瘫痪的人可以通过在他们的头脑中规划运动来训练使用该系统，就像移动他们的自然肢体一样。这项研究的成功可能会在改善中风或脊髓损伤患者的功能和生活质量方面取得重大进展。高中生以及本科生和研究生将接受这项多学科项目的培训。这项研究涉及从生物信号中学习人类意图，使用患者体内的传感器提取更高级别的目标，并根据估计的运动意图和更高级别的目标开发运动操纵控制器。为实现该项目的总体目标而提出的具体子目标包括：一个协作的大脑-机器学习系统，该系统训练人脑将肢体运动控制重新映射到异源肌肉，同时训练机器从异源肌肉的表面肌电图中解释运动意图;使用生物和辅助传感器信号提取更高级别运动目标的算法;使用提取的目标和解码的运动意图来共享机器人手的脑机控制器;以及实验评估这些方法对上肢瘫痪的个人的能力。除了为肢体瘫痪的人开发运动假肢的创新之外，拟议的研究还将为非线性和时变环境中的在线学习，协作脑机学习，分享大脑该奖项反映了NSF的法定使命，并通过使用基金会的智力价值进行评估，被认为值得支持和更广泛的影响审查标准。

It is estimated that approximately 5.4 million people in the United States live with some form of paralysis, defined as a central nervous system disorder resulting in difficulty or inability to move the upper and/or lower extremities. Many paralyzed individuals consider restoration of lost basic motor functions such as grasping and walking as important abilities that could improve their quality of life. The goal of this project is to develop and evaluate advanced machine learning algorithms that enable quadriplegic individuals to control a robotic hand with heterologous muscles (that is, muscles not typically involved in moving the limbs; for example, muscles of the neck). To enable this research, prospective algorithms will be initially evaluated in normally-enabled individuals. The most promising algorithms will subsequently be evaluated in quadriplegic individuals. This research is a first step toward providing benefit to the paralyzed community by creating pathways toward the development and commercialization of functional motor prosthetic systems. Paralyzed individuals can be trained to use the system by planning the movements in their minds, much like moving their natural limbs. Success of this research could lead to a significant advance in improving function and quality of life for individuals affected by stroke or spinal cord injury. High-School students as well as undergraduate and graduate students will be trained on this multi-disciplinary project.This research involves learning human intent from biological signals, extracting higher-level goals using sensors embodied in the patient, and developing controllers for motor manipulation based on estimated motor movement intent and higher-level goals. Specific sub-goals proposed to achieve the overall goal of the project include: a collaborative brain-machine learning system that trains the human brain to remap limb movement control to heterologous muscles while simultaneously training the machine to interpret the movement intent from surface electromyograms of the heterologous muscles; algorithms to extract higher-level movement goals using biologic and auxiliary sensor signals; shared brain-machine controllers of robotic hands using the extracted goal and decoded movement intent; and experimental assessment of the capabilities of the methods on individuals with paralysis of the upper limbs. In addition to the innovations in the development of motor prostheses for people with paralysis of the limbs, the proposed research will provide new insights into online learning in nonlinear and time-varying environments, collaborative brain-machine learning, and shared brain-machine control algorithms for motor prostheses.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.