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Planning Grant: NSF Engineering Research Center for Smart Personalized Assistive Devices and Enabling Systems (SPADES)

Planning Grant: NSF Engineering Research Center for Smart Personalized Assistive Devices and Enabling Systems (SPADES)
规划拨款:NSF 智能个性化辅助设备和支持系统工程研究中心 (SPADES)
批准号:
1936949
负责人:
Albert Shih
金额:
$10.0万
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
项目状态:
已结题
起止时间:
2019-09-01 至 2022-08-31

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中文摘要
翻译
工程研究中心规划拨款竞赛是ERC项目的试点征集。规划补助金不需要作为ERC竞赛的一部分,但它旨在在团队之间建立能力,以规划聚合的、中心规模的工程研究。辅助装置为克服身体限制、预防伤害和改善健康工人和残疾人的安全提供了必要的支持。糖尿病、痴呆和肥胖的流行、儿童残疾的增加以及人口老龄化增加了辅助设备的使用,使它们成为我们社会护理系统的重要组成部分。虽然许多用户对辅助设备的需求增加,但由于自尊心、不适合、高成本和不断变化的需求,他们可能会降低使用这些设备的意愿。智能个性化辅助设备和使能系统(SPADES)工程研究中心(ERC)旨在通过创建个性化辅助设备(PADs)和智能护理系统(SCS)来满足这一社会需求。科学和工程的进步使pad的材料、设计、制造、学习和控制成为可能。将建立pad的分布式制造和服务网络,作为SCS的基础。SPADES的ERC旨在创建一个创新生态系统,并在我们未来的社会中通过多元化和包容女性、少数族裔(URM)和退伍军人的文化,改造pad和SCS。这项计划拨款将建立一个融合工程、心理学、社会学、运动机能学、康复学和工业(知识的广度)以及整合基础科学(知识的深度)的研究和教育团队。将为PADs和SCS探索用于增材制造(AM)、接触力学、以人为本的设计和机器学习的聚合物软材料的新科学知识。将研究微结构和功能梯度材料的AM,以构建具有个性化属性的人体接触界面的保形形状。建立动态生物力学和接触模型,设计与用户最佳个人偏好空间相匹配的PAD接触界面,该界面基于多维展开建模和用户反馈输入以及评估过程中测量的生物特征传感器数据来确定。为解决用户需求的演变,将建立主动PAD的实时控制和基于大数据的改进PAD设计和控制的学习。大数据基于强大的提取、转换和识别能力,用于分析传感器测量和模型预测的演变,这是SCS学习的基础。多层次决策策略将利用来自传感器和用户反馈的数据,结合监督学习和多目标优化,来驱动被动和主动pad的决策策略。该奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
The Planning Grants for Engineering Research Centers competition was run as a pilot solicitation within the ERC program. Planning grants are not required as part of the full ERC competition, but intended to build capacity among teams to plan for convergent, center-scale engineering research.Assistive devices provide the critical support required to overcome physical limitations, prevent injury, and improve safety for healthy workers and people with disabilities. The epidemic of diabetes, dementia, and obesity, the rise in childhood disability, and the aging population have increased the use of assistive devices, making them an instrumental part of the care system in our society. While many users of have an increased need for assistive devices, they may have a declined willingness to use them due to self-pride, poor fit, high cost, and changing needs. The Engineering Research Center (ERC) for Smart Personalized Assistive Devices and Enabling Systems (SPADES) is proposed to meet this societal need by creating personalized assistive devices (PADs) and smart care system (SCS). Advancements in science and engineering have enabled the material, design, manufacturing, learning, and control of PADs. A distributed manufacturing and service network for PADs will be established as the foundation for SCS. The ERC for SPADES is aimed to create an innovation ecosystem and transform PADs and SCS in our future society with the diversity and culture of inclusion for women, underrepresented minority (URM), and veterans. This planning grant will establish a convergent research and education team with partnership among engineering, psychology, sociology, kinesiology, rehabilitation, and industry (the breadth of knowledge) and integration of fundamental sciences (the depth of knowledge). New scientific knowledge in polymeric soft material for additive manufacturing (AM), contact mechanics, human-centered design, and machine learning will be explored for PADs and SCS. AM of micro-architectured and functionally graded materials will be studied to construct conformal shapes for human contact interface with personalized properties. The dynamic biomechanical and contact models will be established to design a PAD contact interface that matches to the optimal personal preference space of a user, which is identified based on the multidimensional unfolding modeling and inputs from the user feedback and biometric sensor data measured during evaluation. To address the evolution of user needs, the real-time control of active PADs and the big data based learning for improved PAD design and control will be established. The big data is based on robust extraction, transformation, and identification capabilities to analyze the evolution of sensor measurements and model predictions, which are the foundation of learning in SCS. A multi-level decision making strategy will utilize data derived from sensors and user feedback, combined with supervised learning and multi-objective optimization, to drive the decision-making strategy for both passive and active PADs.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.
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