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

CHS：媒介：协作研究：运动假肢的协作在线学习和控制

基本信息

批准号：
1901236
负责人：
David Warren
金额：
$ 50.83万
依托单位：
University of Utah
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2023-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1901236&HistoricalAwards=false
关键词：
CHS Medium Collaborative Research Online

项目摘要

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.

据估计，美国大约有540万人患有某种形式的瘫痪，瘫痪被定义为中枢神经系统紊乱，导致上肢和/或下肢活动困难或无法活动。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，是可以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，使四肢瘫痪的人能够用异源肌肉（即通常不参与移动四肢的肌肉，例如颈部肌肉）控制机械手。为了使这项研究能够进行，前瞻性算法将在正常激活的个体中进行初步评估。最有希望的算法将随后在四肢瘫痪的个体中进行评估。这项研究是通过为功能性运动假肢系统的开发和商业化创造途径，为瘫痪社区提供利益的第一步。瘫痪的人可以通过在头脑中规划动作来训练使用该系统，就像移动他们的自然四肢一样。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个多学科项目的培训。这项研究包括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层次的目标，以及基于估计的运动意图和更高层次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：一个协同脑机学习系统，该系统训练人脑将肢体运动控制重新映射到异种肌肉，同时训练机器从异种肌肉的表面肌电图中解读运动意图；利用生物和辅助传感器信号提取高级运动目标的算法；基于目标提取和动作意图解码的机器人手共享脑机控制器并对该方法对上肢瘫痪患者的治疗效果进行了实验评估。除了在肢体瘫痪患者运动假肢开发方面的创新外，该研究还将为非线性和时变环境下的在线学习、脑机协作学习以及运动假肢的共享脑机控制算法提供新的见解。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A Bionic Hand for Semi-Autonomous Fragile Object Manipulation via Proximity and Pressure Sensors

通过接近传感器和压力传感器进行半自主易碎物体操纵的仿生手

DOI：
10.1109/embc46164.2021.9629622
发表时间：
2021
期刊：
Annual International Conference of the IEEE Engineering in Medicine and Biology Society
影响因子：
0
作者：
Hansen, Taylor C.;Trout, Marshall A.;Segil, Jacob L.;Warren, David J.;George, Jacob A.
通讯作者：
George, Jacob A.

Interpreting Volitional Movement Intent From Biological Signals: A Review

从生物信号解释意志运动意图：综述

DOI：
10.1109/msp.2021.3074778
发表时间：
2021
期刊：
IEEE Signal Processing Magazine
影响因子：
14.9
作者：
Dantas, Henrique;Hansen, Taylor C.;Warren, David J.;Mathews, V. John
通讯作者：
Mathews, V. John

Shared Prosthetic Control Based on Multiple Movement Intent Decoders

DOI：
10.1109/tbme.2020.3045351
发表时间：
2021-05-01
期刊：
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
影响因子：
4.6
作者：
Dantas, Henrique;Hansen, Taylor C.;Mathews, V. John
通讯作者：
Mathews, V. John

Shared Control Decreases the Physical and Cognitive Demands of Maintaining a Secure Grip.

共享控制降低了保持安全握持的身体和认知需求。

DOI：
发表时间：
2022
期刊：
Proceedings of the Myoelectric Controls and Upper Limb Prosthetics Symposium.
影响因子：
0
作者：
Trout, M. A.;Hansen, T. C.;Olsen, C. D.;Warren, D. J.;George, J. A.
通讯作者：
George, J. A.

Bilaterally Mirrored Movements Improve the Accuracy and Precision of Training Data for Supervised Learning of Neural or Myoelectric Prosthetic Control

双边镜像运动提高了神经或肌电假肢控制监督学习训练数据的准确性和精确度

DOI：
10.1109/embc44109.2020.9175388
发表时间：
2020
期刊：
Annual International Conference of the IEEE Engineering in Medicine and Biology Society
影响因子：
0
作者：
George, J. A.;Tully, T. N.;Colgan, P. C.;Clark, G. A.
通讯作者：
Clark, G. A.