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名婴儿出现脑瘫，而有4,000名婴儿患有脊柱和大脑的先天缺陷。这些婴儿的电动机延误不仅对家庭，而且对整个社会都带来了终生的社会和经济后果。目前，具有移动性障碍的婴儿无法访问移动机器人可以提供的自适应，动态和自主游戏活动的类型。我们目前对发育中儿童的重要神经塑性和神经肌肉变化的科学理解并不是在小儿康复中最先进的空间，这些小儿康复的最先进仍然无法满足数百万具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失去了每天的富裕运动和早期认知刺激。此外，成人护理人员发起和指导的绝大多数治疗活动；然而，排他性和连续的成人控制可能会累人，并剥夺孩子自由探索她动态环境的机会。该项目的研究活动将介绍机器人玩具群的高剂量小儿康复所需的科学创新。机器人玩具群将设计为对儿童的大脑活动的反应，因此它们在身体和智力上都可以参与。设想的创新不仅会破坏婴儿汽车康复中的现状，而且还可以减轻当前对特殊需求儿童护理人员的压力。通过这种方式，该项目将直接促进NSF确定的关键期望社会成果之一，这是社会中个人的福祉的改善，并间接地，间接地通过增加了一项促进运动环境的运动环境的运动计划来提高美国的生产力，从而增加美国的经济竞争力，从而使运动障碍的生产力提高。在基于游戏的活动中与婴儿在社交上互动的机器人玩具。我们的假设是，适当设计的群体运动行为可以使婴儿从事积极的游戏玩法，并且可以根据非侵入性成像和脑电图（EEG）测量来实时协调儿童互动。该研究计划沿着四个关键轴扩展，旨在以以下特定目标：（i）专门针对社交儿童机器人互动的集体运动行为的设计； （ii）用于实时监视和跟踪这种动态相互作用的资源感知感知算法的开发； （iii）产生脑电图数据处理方法，以提供有关主题参与的实时反馈；最后（iv）将该技术集成到适应性和反应性丰富的环境中，可提供干预并评估运动康复结果。该提案通过将协调的社交智能玩具作为一种自适应的人机界面引入丰富的环境中，从而推动了小儿运动康复的信封。智力上的贡献包括（a）在利用基于游戏的相互作用的儿童中神经塑性和神经肌肉变化的新干预途径； （b）一种用于确定不基于直接人类输入或显式命令的人类互动的新数学体系结构； （c）使用高度移动和互动智能玩具的婴儿参与的定量神经生理学指标； （d）利用深层网络的新资源感知，强大且一致的多目标跟踪算法。这些贡献将是重要的，因为它们将确定协调的对象运动行为与婴儿感知和行动之间的特定基本关系，并探索它们以推动理论干预措施。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响标准通过评估来评估的支持。

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.