SCH: INT: Pediatric motor rehabilitation via socially interacting robot swarms

SCH：INT：通过社交互动机器人群进行小儿运动康复

• 批准号: 2014264
• 负责人: Herbert Tanner
• 金额: $ 109.93万
• 依托单位: University of Delaware
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2014264&HistoricalAwards=false
• 关键词: SCH; INT; Pediatric; motor; rehabilitation

Each year, 10,000 infants in the U.S. alone develop cerebral palsy and 4,000 infants have birth defects of the spine and the brain. The motor delays for these infants have lifelong social and economic consequences, not only for the families, but also for the society as a whole. Currently, infants with mobility impairments do not have access to the type of adaptive, dynamic, and autonomous play-based activity that mobile robots can provide. Our current scientific understanding of the significant neuroplastic and neuromuscular changes in developing children is not at pace with the state-of-the-art in pediatric rehabilitation, which still cannot meet the needs of millions of school-aged children with special needs. By the age of 4 or 5, most of these children have permanently lost the daily rich motor and cognitive stimulation of the early years. In addition, the vast majority of therapy activities are initiated and directed by adult caregivers; yet exclusive and continuous adult control can be exhausting and deprive the child from the chance to freely explore her dynamic environment. This project’s research activities will introduce the scientific innovations necessary for robotic toy swarm-enabled high-dosage pediatric rehabilitation. The robotic toy swarms will be designed to be responsive to children’s brain activity so they can be both physically and intellectually engaged. The envisioned innovations will not only disrupt the status quo in infant motor rehabilitation, but can also alleviate current stressors to special need children caregivers. In this way, this project will promote directly one of the key desired societal outcomes identified by NSF, which is the improvement of well-being of individuals in society, and indirectly that of increasing the economic competitiveness of the U.S. primarily through the expected increase in productivity for individuals with motor disabilities over the course of their life.This proposal puts forth a research plan that enables the development of an enriched infant motor rehabilitation environment, which involves stimulation by swarms of robotic toys that are socially interacting with infants in play-based activities. Our hypothesis is that appropriately designed swarm motion behaviors can keep infants engaged in active game-play, and that child-swarm interaction can be coordinated in real-time based on non-invasive imagery and electroencephalograpy (EEG) measurements. The research plan expands along four key axes that aim at the following specific goals: (i) the design of collective motion behaviors specifically for social child-robot interaction; (ii) the development of resource-aware perception algorithms for real-time monitoring and tracking of this dynamic interaction; (iii) the production of EEG data processing methods to provide real-time feedback on subject engagement; and finally (iv) the integration of this technology into an adaptive and reactive enriched environment that delivers intervention and assesses motor rehabilitation outcomes. This proposal pushes the envelope in pediatric motor rehabilitation by introducing coordinated socially-interacting smart toys as an adaptive human-machine interface within an enriched environment. The intellectual contributions include (a) new intervention pathways to neuroplastic and neuromuscular changes in developing children that utilize directed play-based interaction; (b) a new mathematical architecture for regulating human-swarm interaction that is not based on direct human input or explicit commands; (c) quantitative neurophysiological metrics of infant engagement with highly mobile and interactive smart toys; and (d) new resource-aware, robust and consistent multi-target tracking algorithms that leverage deep networks. These contributions will be significant because they will identify specific fundamental relationships between coordinated object motion behavior and infant perception and action, and exploit them to drive therapeutic interventions.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.

每年，仅在美国就有10，000名婴儿患脑瘫，4，000名婴儿患有脊柱和大脑的先天缺陷。这些婴儿的运动迟缓不仅对家庭，而且对整个社会都有终身的社会和经济后果。目前，有行动障碍的婴儿无法获得移动的机器人可以提供的自适应、动态和自主的基于游戏的活动。我们目前对发育中儿童的重要神经可塑性和神经肌肉变化的科学理解与儿科康复的最新水平不同步，这仍然无法满足数百万有特殊需要的学龄儿童的需求。到4岁或5岁时，这些孩子中的大多数已经永久地失去了早年每天丰富的运动和认知刺激。此外，绝大多数治疗活动都是由成人照料者发起和指导的;然而，完全和持续的成人控制可能会使儿童筋疲力尽，并剥夺儿童自由探索其动态环境的机会。该项目的研究活动将引入机器人玩具群支持的高剂量儿科康复所需的科学创新。机器人玩具群将被设计成对儿童的大脑活动做出反应，这样他们就可以在身体和智力上都参与进来。设想的创新不仅将打破婴儿运动康复的现状，而且还可以减轻目前对特殊需要儿童照顾者的压力。通过这种方式，该项目将直接促进NSF确定的关键预期社会成果之一，即改善社会中个人的福祉，并间接提高美国的经济竞争力，主要是通过预期的提高生产力的个人与运动残疾人在他们的生活过程中。这一建议提出了一项研究计划，使发展的一个丰富的婴儿运动康复环境，其中包括通过成群的机器人玩具进行刺激，这些玩具在基于游戏的活动中与婴儿进行社交互动。我们的假设是，适当设计的群体运动行为可以让婴儿参与积极的游戏，并且可以根据非侵入性图像和脑电图（EEG）测量实时协调儿童群体互动。该研究计划沿着四个关键轴扩展，旨在实现以下具体目标：（i）专门用于社会儿童-机器人交互的集体运动行为的设计;（ii）用于实时监测和跟踪这种动态交互的资源感知感知算法的开发;（iii）EEG数据处理方法的产生，以提供受试者参与的实时反馈;以及最后（iv）将该技术整合到提供干预和评估运动康复结果的适应性和反应性丰富的环境中。该提案通过引入协调的社交互动智能玩具作为丰富环境中的自适应人机界面，推动了儿科运动康复的发展。智力方面的贡献包括：（a）利用基于游戏的定向互动，对发育中儿童的神经可塑性和神经肌肉变化进行干预的新途径;（B）不基于直接人类输入或明确命令的调节人类群体互动的新数学架构;（c）婴儿参与高度移动的和互动智能玩具的定量神经生理学指标;以及（d）利用深度网络的新的资源感知、稳健和一致的多目标跟踪算法。这些贡献将是重要的，因为他们将确定协调的物体运动行为和婴儿的感知和行动之间的具体的基本关系，并利用它们来推动治疗干预。这个奖项反映了NSF的法定使命，并已被认为是值得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的支持。

项目成果

Resilient Supervisory Multiagent Systems

弹性监管多代理系统

• DOI: 10.1109/tro.2021.3108074
• 发表时间: 2022
• 期刊: IEEE Transactions on Robotics
• 影响因子: 7.8
• 作者: Baxevani, Kleio;Zehfroosh, Ashkan;Tanner, Herbert G.
• 通讯作者: Tanner, Herbert G.

Bifurcating vector fields driven by time-scale separated motivational dynamics

由时间尺度分离的动机动力学驱动的分叉向量场

• 发表时间: 2023
• 期刊: IFAC World Congress 2023
• 作者: Baxevani, Kleio;Tanner, Bert
• 通讯作者: Tanner, Bert

Map-based Visual-Inertial Localization: A Numerical Study

• DOI: 10.1109/icra46639.2022.9811829
• 发表时间: 2022-05
• 期刊: 2022 International Conference on Robotics and Automation (ICRA)
• 作者: Patrick Geneva;G. Huang

Navigation functions with moving destinations and obstacles

• DOI: 10.1007/s10514-023-10088-7
• 发表时间: 2023-02
• 期刊: Autonomous Robots
• 影响因子: 3.5
• 作者: Cong Wei;Chuchu Chen;H. Tanner

Multi-behavioral Multi-Robot Systems driven by Motivation Dynamics

动机动力学驱动的多行为多机器人系统

• DOI: 10.23919/acc55779.2023.10156084
• 发表时间: 2023
• 期刊: 2023 American Control Conference (ACC
• 作者: Baxevani, Kleio;Tanner, Herbert G.
• 通讯作者: Tanner, Herbert G.