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，其定义是一种中枢神经系统疾病，导致上肢和/或下肢活动困难或无法移动。许多瘫痪患者认为，恢复失去的基本运动功能，如抓握和行走，是提高他们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，使四肢瘫痪患者能够控制具有异种肌肉(即通常不涉及肢体运动的肌肉，例如颈部肌肉)的机械手。为了支持这项研究，预期的算法将首先在正常启用的个体中进行评估。最有希望的算法随后将在四肢瘫痪患者中进行评估。这项研究是通过为功能性运动假体系统的开发和商业化创造途径，为瘫痪社区提供好处的第一步。瘫痪的人可以通过在大脑中规划动作来训练他们使用该系统，就像移动他们自然的四肢一样。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一多学科项目的培训。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，以及基于估计的运动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提出的具体次级目标包括：一个协作式脑-机器学习系统，训练人脑将肢体运动控制重新映射到不同肌肉，同时训练机器从不同肌肉的表面肌电解释运动意图；利用生物和辅助传感器信号提取更高级别的运动目标的算法；使用提取的目标和解码的运动意图实现机械手的共享脑机控制器；对这些方法在上肢瘫痪患者身上的能力进行实验评估。除了为肢体瘫痪患者开发运动假体方面的创新外，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-机器学习以及运动假体的共享脑机控制算法提供新的见解。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.