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

在中风事件发生后，尽管接受了数月的治疗，大多数中风幸存者仍失去了使用手执行各种任务的能力。为了恢复手的灵巧性，已经开发了先进的辅助设备(例如外骨骼)。不幸的是，这些新设备中只有很少一部分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可靠的方法，使中风幸存者能够直观地控制设备。该项目的主要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相结合，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动作时将是稳健的。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。这里使用的计算方法也将产生一个研究工具来研究人与机器人的互动。研究人员将通过在线存储库系统SimTK.org提供计算模型，作为其他研究人员研究手功能和康复设备控制的模拟平台。该项目将提供教育和培训机会。研究概念将融入现有课程。包含这些技术的暑期项目将提供给本科生和高中生以及当地学校和社区大学的教师。这个项目的目标是开发一种个性化的混合界面(基于神经数据和基于模型)，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移动。该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(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健壮性。研究小组随后将使用肢体的个性化肌肉骨骼模型，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、运动动力学和特定于受试者的损伤，否则可能会干扰用户输入和期望输出之间的映射。最后，研究小组将评估开发的用于控制高级手外骨骼的界面，允许用户独立控制每个手指的屈曲或伸展辅助。辅助力将加强有益的肌肉激活，同时补偿异常的激活模式。总的来说，结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优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.