权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

HCC: Medium: A novel neural interface for user-driven control of rehabilitation of finger individuation

HCC：中：一种新颖的神经接口，用于用户驱动的手指个性化康复控制

基本信息

批准号：
2330862
负责人：
Xiaogang Hu
金额：
$ 80万
依托单位：
Pennsylvania State Univ University Park
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-10-01 至 2025-11-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2330862&HistoricalAwards=false
关键词：
HCC Medium novel neural interface

项目摘要

Following a stroke incident, a majority of stroke survivors lose the ability to use their hand to perform a variety of tasks despite months of therapy. In an effort to restore hand dexterity, advanced assistive devices (e.g., exoskeletons) have been developed. Unfortunately, only few of these novel devices have been used effectively by stroke survivors. One critical factor limiting user acceptance is the lack of reliable method that allows stroke survivors to intuitively control the device. The overarching objective of the project is to combine novel decoding of neurological signals that drive the muscles with a personalized musculoskeletal model of the upper limb to provide intuitive control of an assistive hand exoskeleton. The control strategy will be robust in handling different arm postures and movements. This personalized approach will improve hand functional performance in stroke survivors, with the overall goal of improving their ability to live independently. The computational approaches employed here will also produce a research tool to study human-robot interactions. The researchers will make the computational model available over online repository system, SimTK.org, as a simulation platform for other researcher working on hand function and control of rehabilitative devices. The project will provide educational and training opportunities. The research concepts will be integrated into existing courses. Summer projects incorporating the techniques will be offered to undergraduate and high school students and local school and community college instructors. Outreach programs will be developed to disseminate the proposed research outcomes to underrepresented students.The goal of this project is to develop a personalized hybrid (neural data-based and model-based) interface that combines the decoded neural command with a musculoskeletal model. The developed interface will be used to control a soft-hard hybrid exoskeleton to enable dexterous finger movements in stroke survivors. The research team will first develop a real-time neural decoding algorithm based on populational firing probability of the motoneurons, extracted from motor unit decomposition of high-density electromyographic (HD-EMG) signals. Through incorporation of binary neuron discharge events, the decoded neural drive signals will be robust to changes in muscle activity features, background noise, and motion artifact. The research team will then employ a personalized musculoskeletal model of the limb, which will be calibrated to the unique musculoskeletal structure and activation parameters of stroke survivors. The model-based controller will be able to compensate for limb posture, movement dynamics, and subject-specific impairments that could otherwise disturb the mapping between user input and desired output. Finally, the research team will evaluate the developed interface for control of an advanced hand exoskeleton, allowing users to control flexion or extension assistance independently for each digit. The assistive forces will reinforce beneficial muscle activation while compensating for abnormal activation patterns. Collectively, the outcomes will restore hand dexterity in stroke survivors, thereby enabling them to perform daily activities and live independently.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.

在中风事件发生后，尽管经过数月的治疗，大多数中风幸存者仍然失去了使用他们的手执行各种任务的能力。为了恢复手的灵活性，先进的辅助装置（例如，外骨骼）已经被开发。不幸的是，这些新型设备中只有少数被中风幸存者有效地使用。限制用户接受的一个关键因素是缺乏允许中风幸存者直观地控制设备的可靠方法。该项目的总体目标是将驱动肌肉的神经信号的新颖解码与上肢的个性化肌肉骨骼模型结合联合收割机，以提供对辅助手外骨骼的直观控制。控制策略在处理不同的手臂姿势和运动时将是鲁棒的。这种个性化的方法将改善中风幸存者的手功能表现，总体目标是提高他们独立生活的能力。这里采用的计算方法也将产生一个研究工具来研究人机交互。研究人员将通过在线存储库系统SimTK.org提供计算模型，作为其他研究手部功能和康复设备控制的研究人员的模拟平台。该项目将提供教育和培训机会。研究概念将被纳入现有的课程。将为本科生和高中生以及当地学校和社区大学的讲师提供包含这些技术的暑期项目。本项目的目标是开发一个个性化的混合（基于神经数据和基于模型）界面，将解码的神经命令与肌肉骨骼模型相结合。开发的界面将用于控制软硬混合外骨骼，使中风幸存者的手指能够灵活运动。研究小组将首先开发一种基于运动神经元群体放电概率的实时神经解码算法，该算法从高密度肌电图（HD-EMG）信号的运动单位分解中提取。通过结合二进制神经元放电事件，解码的神经驱动信号将对肌肉活动特征、背景噪声和运动伪影的变化具有鲁棒性。然后，研究小组将采用个性化的肢体肌肉骨骼模型，该模型将根据中风幸存者独特的肌肉骨骼结构和激活参数进行校准。基于模型的控制器将能够补偿肢体姿势、运动动力学和对象特定的损伤，否则这些损伤可能会干扰用户输入和期望输出之间的映射。最后，研究小组将评估用于控制高级手部外骨骼的开发界面，允许用户独立控制每个手指的屈曲或伸展辅助。辅助力量将加强有益的肌肉激活，同时补偿异常激活模式。总的来说，这些成果将恢复中风幸存者的手部灵活性，从而使他们能够进行日常活动和独立生活。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和更广泛的影响审查标准进行评估，被认为值得支持。