B2: Learning Environments with Augmentation and Robotics for Next-gen Emergency Responders (LEARNER)

B2：为下一代应急响应人员提供增强和机器人技术的学习环境（学习者）

• 批准号: 2033592
• 负责人: Ranjana Mehta
• 金额: $ 499.83万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2033592&HistoricalAwards=false
• 关键词: B2; Learning; Environments; Augmentation; Robotics

The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future.The broader impact and potential societal benefits of this Convergence Accelerator Phase II project will be to generate technology-based learning solutions that can support and augment the performance and safety of emergency response (ER) personnel. Academic researchers, core-technology developers, stakeholders, and an advisory board constituted of leaders from industry and government will come together to assess opportunities and challenges related to the use of human augmentation technologies (HATs) that can transform the process of foundational, use-inspired solution-finding for ER work, and in a way that is transferable to other work contexts as well. This will involve the development and evaluation of LEARNER (Learning Environments with Augmentation and Robotics for Next-gen Emergency Responders), a mixed-reality learning environment with physical, augmented, and virtual reality components, for users to learn to work effectively with two HAT classes: powered exoskeletons (EXO) and head-worn AR interfaces (AR). Our effort will contribute to better conceptualize convergence work that can foster the understanding of reciprocal human-technology interactions; contribute to systems that are tailored, optimized, and continuously adapted for humans and their environments; and education and lifelong learning to create the requisite workforce. Our effort will also serve as a model for other research communities that can benefit from working across traditional disciplinary boundaries in engineering, computer science, learning sciences, and human resource development. We will share our methods, learnings and findings with the ER community and the wider world by leading a National Talent Ecosystem Council, a collaborative think-tank organization, to support scientific research activities on workforce learning with advanced technologies and organizing Learn-X symposiums on the topic of technology-driven advances in learning-sciences and educational/human resource development.We will develop and evaluate a functional prototype of LEARNER – an innovative accessible, modular, personalized, and scalable learning platform to accelerate skilling and reskilling of ER workers, particularly on nascent augmentation technologies that have significant potential to change the very nature of work and improve efficiency, health, and well-being. LEARNER will provide a unique training paradigm by incorporating physiological, neurological, and behavioral markers of learning into real-time scenario evolution. The proposed virtual and physical user interfaces and interaction techniques will advance the human-computer interaction field by providing a multisensory approach for ER simulation and synchronized virtual interactions with physical environments and work artifacts. Furthermore, our plan to field these HATs and develop an effective learning platform has significant transformative potential as EXOs and AR will enable users to formulate new work strategies at the individual and team levels enabled by their newly extended physical and perceptual capabilities. Finally, our work will advance learning by creating a scalable and replicable platform that will increase the speed of integration and adoption of innovative and emerging HATs that benefit the future workforce across diverse industrial sectors. Our transdisciplinary approach converges and enhances the existing knowledge from the disciplines of learning science, computer science, virtual and augmented realities, human factors, cognitive psychology, and systems engineering to create the LEARNER platform that integrates training course design, innovative and emerging technology implementation, and new techniques of work.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.

NSF融合加速器支持以使用为灵感、以团队为基础的多学科努力，以应对国家重要性的挑战，并将在不久的将来产生对社会有价值的成果。这一融合加速器第二阶段项目的更广泛影响和潜在的社会好处将是生成基于技术的学习解决方案，这些解决方案可以支持和增强应急响应(ER)人员的绩效和安全。学术研究人员、核心技术开发人员、利益攸关方以及由行业和政府领导人组成的顾问委员会将齐聚一堂，评估与使用人类增强技术(HATS)相关的机遇和挑战，HATS可以改变ER工作的基础、受使用启发的解决方案寻找过程，并且以一种可以转移到其他工作环境的方式。这将涉及开发和评估Learner(下一代应急响应人员的增强和机器人学习环境)，这是一个包含物理、增强和虚拟现实组件的混合现实学习环境，用户可以学习如何有效地使用两个HAT类别：电动外骨骼(EXO)和头戴式AR接口(AR)。我们的努力将有助于更好地概念化融合工作，以促进对人与技术相互作用的理解；促进为人类及其环境量身定做、优化和不断适应的系统；以及教育和终身学习，以创造必要的劳动力。我们的努力也将成为其他研究社区的典范，这些社区可以从跨越工程、计算机科学、学习科学和人力资源开发的传统学科边界中受益。我们将与ER社区和更广泛的世界分享我们的方法、学习和发现，方法是领导一个协作智库组织-国家人才生态系统理事会，以支持关于使用先进技术进行劳动力学习的科学研究活动，并组织以学习科学和教育/人力资源开发中的技术驱动进步为主题的Lear-X研讨会。我们将开发和评估学习者的功能原型-一个创新的可访问、模块化、个性化和可扩展的学习平台，以加速ER员工的技能和再培训，特别是在具有巨大潜力改变工作性质、提高效率、健康和福祉的新兴增强技术方面。学习者将通过将学习的生理、神经和行为标记物整合到实时情景演变中来提供独特的训练范例。提出的虚拟和物理用户界面和交互技术将通过提供一种用于ER模拟的多感官方法以及与物理环境和工作人工制品的同步虚拟交互来推进人机交互领域。此外，我们部署这些帽子并开发有效学习平台的计划具有重大的变革潜力，因为EXOS和AR将使用户能够根据他们新扩展的身体和感知能力，在个人和团队层面制定新的工作战略。最后，我们的工作将通过创建一个可扩展和可复制的平台来推动学习，该平台将加快创新和新兴HAT的集成和采用速度，使不同行业的未来劳动力受益。我们的跨学科方法融合并增强了学习科学、计算机科学、虚拟和增强现实、人类因素、认知心理学和系统工程等学科的现有知识，创建了一个集培训课程设计、创新和新兴技术实施以及新工作技术于一体的学习者平台。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。

项目成果

Human-Centered Intelligent Training for Emergency Responders

以人为本的应急响应人员智能培训

• DOI: 10.1609/aimag.v43i1.19129
• 发表时间: 2022
• 期刊: AI Magazine
• 影响因子: 0.9
• 作者: Mehta, Ranjana;Moats, Jason;Karthikeyan, Rohith;Gabbard, Joseph;Srinivasan, Divya;Du, Eric;Leonessa, Alexander;Burks, Garret;Stephenson, Andrew;Fernandes, Ron
• 通讯作者: Fernandes, Ron

A neurophysiological approach to assess training outcome under stress: A virtual reality experiment of industrial shutdown maintenance using Functional Near-Infrared Spectroscopy (fNIRS)

• DOI: 10.1016/j.aei.2020.101153
• 发表时间: 2020-10
• 期刊: Adv. Eng. Informatics
• 作者: Yangming Shi;Yibo Zhu;Ranjana K. Mehta;E. Du

User-Centered Design and Evaluation of ARTTS: an Augmented Reality Triage Tool Suite for Mass Casualty Incidents

以用户为中心的 ARTTS 设计和评估：针对大规模伤亡事件的增强现实分诊工具套件

• 发表时间: 2022
• 期刊: IEEE International Symposium on Mixed and Augmented Reality ISMARAdjunct
• 作者: Nelson, CR;Gabbard, JL;Moats, JB;Mehta, RK
• 通讯作者: Mehta, RK

Neurobehavioral assessment of force feedback simulation in industrial robotic teleoperation

• DOI: 10.1016/j.autcon.2021.103674
• 发表时间: 2021-06
• 期刊: Automation in Construction
• 影响因子: 10.3
• 作者: Qi Zhu;Jing Du;Yangming Shi;P. Wei

Modeling Brain Dynamics During Virtual Reality-Based Emergency Response Learning Under Stress

压力下基于虚拟现实的应急响应学习期间的大脑动力学建模

• DOI: 10.1177/00187208211054894
• 发表时间: 2021
• 期刊: Human Factors: The Journal of the Human Factors and Ergonomics Society
• 作者: Tyagi, Oshin;Hopko, Sarah;Kang, John;Shi, Yangming;Du, Jing;Mehta, Ranjana K.
• 通讯作者: Mehta, Ranjana K.