Collaborative Research: HEBB: Human-Robot Enabled System to Induce Brain Behavior Adaptations

合作研究：HEBB：诱导大脑行为适应的人机驱动系统

• 批准号: 1935500
• 负责人: Laurel Riek
• 金额: $ 45万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1935500&HistoricalAwards=false
• 关键词: Collaborative; Research; HEBB; Human; Robot

The overall research objective of this collaborative project is to create an embodied, intelligent robotic system that can induce meaningful long-term change in human motor function by providing personalized, adaptive feedback and noninvasive neural stimulation designed to induce desirable neuromotor plasticity. The motor behavior targeted for enhancement is plantarflexor power during the push-off phase of gait; stroke survivors often produce diminished plantarflexor power and rely instead on an inappropriate hip flexion "pull-off" compensation, thereby limiting the quality of their gait and quality of life. Personalized learning methods will be employed to model and optimize behavioral responses to changes in performance feedback provided by an intelligent mobile robotic coach, which will guide gait training. The project will lay the foundation to determine whether training based solely on principles of motor learning suffice to induce meaningful increases in plantarflexor power that are retained over time, or whether simultaneous targeted changes in brain excitability are required. This project advances the NSF mission to promote the progress of science and advance the national health by developing an adaptive motor learning algorithm embedded within an interactive mobile robot to induce meaningful long-term changes in human motor function through human-robot interaction. Broader impacts of the project include efforts to enhance research reproducibility and rigor, and to broaden participation in STEM for women, minorities, and persons with disabilities. The overall objective of this research is to create an embodied, intelligent system that provides personalized, adaptive feedback to induce neuromotor plasticity, mediate motor adaptation, and promote meaningful, lasting increases in plantarflexor power, which is diminished during walking in many stroke survivors. Three sets of human subject experiments are researched. The first will identify critical parameters of performance feedback that facilitate the desired behavioral change. The second will use a novel learning paradigm to model and optimize behavioral responses to changes in performance feedback provided by an intelligent robotic coach. The third will use single-pulse transcranial magnetic stimulation (TMS) and paired associative stimulation (PAS) to harness neuroplastic effects in humans such that desired behavioral changes induced by optimized feedback training are made persistent through Hebbian learning mechanisms. The envisioned system will involve bi-directional learning between the human and machine intelligences to determine how to control important, but subject-specific, variables critical for maintaining and promoting motor function across the life and health span. Understanding these bi-directional relationships within the context of neurorehabilitation may provide insights that can further advance human-robot teaming in a range of application domains, including healthcare, manufacturing, and personal transportation.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.

该合作项目的总体研究目标是创建一个具体的智能机器人系统，该系统可以通过提供个性化的自适应反馈和非侵入性神经刺激来诱导人类运动功能发生有意义的长期变化，旨在诱导理想的神经运动可塑性。增强的目标运动行为是步态推出阶段的跖屈肌力量；中风幸存者通常会产生跖屈肌力量减弱的情况，而是依赖于不适当的髋部屈曲“拉力”补偿，从而限制了他们的步态质量和生活质量。将采用个性化学习方法来建模和优化对智能移动机器人教练提供的表现反馈变化的行为反应，这将指导步态训练。该项目将为确定仅基于运动学习原理的训练是否足以引起随时间保留的跖屈肌力量有意义的增加，或者是否需要同时有针对性地改变大脑兴奋性奠定基础。该项目通过开发嵌入交互式移动机器人中的自适应运动学习算法，通过人机交互诱导人类运动功能发生有意义的长期变化，从而推进 NSF 促进科学进步和促进国民健康的使命。该项目更广泛的影响包括努力提高研究的可重复性和严谨性，并扩大妇女、少数族裔和残疾人对 STEM 的参与。这项研究的总体目标是创建一个具体的智能系统，提供个性化的自适应反馈，以诱导神经运动可塑性，调节运动适应，并促进跖屈肌力量的有意义、持久的增加，而在许多中风幸存者中，跖屈肌力量在行走过程中会减弱。研究了三组人体实验。第一个将确定促进所需行为改变的绩效反馈的关键参数。第二个将使用一种新颖的学习范式来建模和优化对智能机器人教练提供的表现反馈变化的行为反应。第三种方法将使用单脉冲经颅磁刺激（TMS）和配对联想刺激（PAS）来利用人类的神经可塑性效应，从而通过赫布学习机制使优化反馈训练引起的所需行为变化持续存在。设想的系统将涉及人类和机器智能之间的双向学习，以确定如何控制重要但特定于主题的变量，这些变量对于在整个生命和健康范围内维持和促进运动功能至关重要。在神经康复的背景下理解这些双向关系可能会提供见解，从而进一步推进一系列应用领域的人机协作，包括医疗保健、制造和个人交通。该奖项反映了 NSF 的法定使命，并被认为是值得的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来提供支持。

项目成果

JESSIE: Synthesizing Social Robot Behaviors for Personalized Neurorehabilitation and Beyond

JESSIE：综合社交机器人行为以实现个性化神经康复等

• DOI: 10.1145/3319502.3374836
• 发表时间: 2020-03-06
• 期刊: 2020 15th ACM/IEEE International Conference on Human-Robot Interaction (HRI)
• 作者: A. Kubota;Emma I. C. Peterson;Vaishali Rajendren;H. Kress;L. Riek
• 通讯作者: L. Riek

Stochastic Multi-Player Bandit Learning from Player-Dependent Feedback

随机多人强盗从玩家相关的反馈中学习

• 发表时间: 2020-01
• 期刊: ICML Workshop on Real World Experiment Design and Active Learning
• 作者: Wang, Z.;Singh, M.K.;Zhang, C.;Riek, L.D.;Chaudhuri, K.
• 通讯作者: Chaudhuri, K.

Multitask Bandit Learning Through Heterogeneous Feedback Aggregation

通过异构反馈聚合进行多任务强盗学习

• 发表时间: 2021-01
• 期刊: Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS
• 作者: Wang, Z.;Chaudhuri, K.
• 通讯作者: Chaudhuri, K.

Facial Expression Modeling and Synthesis for Patient Simulator Systems: Past, Present, and Future

模拟病人系统的面部表情建模和合成：过去、现在和未来

• DOI: 10.1145/3483598
• 发表时间: 2022-03-03
• 期刊: ACM Transactions on Computing for Healthcare (HEALTH)
• 作者: Maryam Pourebadi;L. Riek
• 通讯作者: L. Riek

GARRY: The Gait Rehabilitation Robotic System

GARRY：步态康复机器人系统

• 发表时间: 2024-01
• 期刊: Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’24
• 作者: Bestmann, B.;Chow, A.;Kubota, A.;Riek, L.D.
• 通讯作者: Riek, L.D.