Convergence Accelerator Phase I(RAISE): Smart Platform of Personalized Learning, Assessment and Prediction for Future Career Training of Skilled Workers

NSF融合加速器支持以团队为基础的多学科努力，解决国家重要性的挑战，并在不久的将来显示出可交付成果的潜力。这个“融合加速器”第一阶段项目的更广泛的影响/潜在利益是解决发展和维持熟练技术劳动力的关键需求，这是美国经济的关键组成部分。本项目拟通过数字化培训流程，嵌入大数据、（人工智能）AI、智能传感、混合现实、运动机能学和消防工程等先进技术，开发智能培训平台。其结果可能会加速传统培训项目向与最新技术进步相关的新型培训和认证的转变。具体来说，该项目将为消防员提供一个新的培训平台，减少伤害，缩短培训过程，并通过虚拟培训计划为更多的人准备未来的消防员。这将挽救生命，减少火灾中财产损失和人员伤亡的成本。通过开发的公开平台，消防员将学习STEM技能，并在职业生涯后期轻松过渡到需要类似技能的新职位。该平台可以扩展到各种技术工人职业，如医疗保健和智能制造。该项目还将加强大学和在线消防安全课程，并在两所参与大学培训少数民族学生。“融合加速器”第一阶段项目建议通过一个智能、个性化和增强的培训平台来创新技术工人的培训，该平台可协调各组织的培训。该平台将集成以数据为中心的技术，以服务于不同参与者的多种目的，并提供一套全面的培训功能。具体来说，该项目将研究几个研究任务：用于同时跟踪用户、动作识别和用户识别的智能无线传感系统；智能和自适应传感，用于全面评估用户行为和对环境的影响；新的数据驱动的损伤评估和预测方法；基于深度学习的个性化培训活动和未来工作推荐模型；以及用于创建各种培训环境的协作增强方法，以及用于分析大规模数据来源的沉浸式分析。这些方法为未来劳动力的适应性培训形成了一个整体的环境。作为特殊案例，调查人员将通过大学消防工程专业、消防部门和培训学院以及全国消防协会的密切合作，评估和演示开发的系统在消防员培训方面的应用。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。

The NSF Convergence Accelerator supports team-based, multidisciplinary efforts that address challenges of national importance and show potential for deliverables in the near future. The broader impact/potential benefit of this Convergence Accelerator Phase I project is to address the critical needs for developing and sustaining skilled technical workforce, which are a key component of the U.S. economy. This project proposes to develop a smart training platform through digitalizing the training processes and embedding advanced techniques of big data, (Artificial Intelligence) AI, smart sensing, mixed reality, kinesiology and fire engineering. The results could accelerate the changes of traditional training programs to new types of training and certification related to the latest technology advances. Specifically, this project will benefit firefighters with a new training platform that reduces injuries, shortens training process, and prepares more people as future firefighters with virtual training programs. This will save lives and reduce costs of both property damage and human casualty in fires. With the developed platform that will be made publicly available, firefighters will learn STEM skills and achieve an easy transition into new positions that require similar skills in later in their careers. The platform could be extended to a variety of skilled worker occupations such as health care and smart manufacturing. This project will also strengthen college and online programs of fire safety and train minority students at two participating universities.This Convergence Accelerator Phase I project proposes to innovate the training of skilled workers through a smart, personalized and augmented training platform that coordinates training across organizations. The platform will integrate data-centric techniques to serve multiple purposes of various participants and provide a comprehensive suite of training functions. Specifically, this project will investigate several research tasks: intelligent wireless sensing system for simultaneous user tracking, action recognition, and user identification; smart and adaptive sensing for comprehensive evaluation of user actions and impacts on environments; new data-driven methods for injury assessment and prediction; deep learning based recommendation models for personalized training activity and future jobs; and collaborative augmenting methods for creating various training environments and immersive analytics for analyzing large-scale data provenance. These methods form a holistic environment for adaptive training of the future workforce. As a special case, the investigators will evaluate and demonstrate the application of the developed system on training of firefighters through close collaborations among university fire engineering programs, fire departments and a training academy, and a nationwide firefighter association.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.