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

NSF融合加速器支持基于团队的多学科努力，以应对国家重要性的挑战，并在不久的将来显示出交付成果的潜力。这一融合加速器第一阶段项目的更广泛影响/潜在好处是满足培养和维持熟练技术劳动力的关键需求，这些劳动力是美国经济的关键组成部分。该项目提出，通过将培训流程数字化，并嵌入大数据、(人工智能)AI、智能传感、混合现实、人体工学和消防工程等先进技术，开发智能培训平台。这一结果可能会加速传统培训计划向与最新技术进步相关的新型培训和认证的转变。具体地说，该项目将通过一个新的培训平台使消防员受益，该平台可以减少伤害，缩短培训过程，并通过虚拟培训计划为更多的人培养未来的消防员。这将挽救生命，降低火灾中财产损失和人员伤亡的成本。随着开发的平台将公开提供，消防员将学习STEM技能，并实现轻松过渡到新的职位，在他们职业生涯的后期需要类似的技能。该平台可以扩展到各种熟练工人职业，如医疗保健和智能制造。该项目还将加强学院和在线消防安全项目，并在两所参与大学培训少数民族学生。这一融合加速器第一阶段项目提议通过一个智能、个性化和增强的培训平台来创新技术工人的培训，该平台协调跨组织的培训。该平台将整合以数据为中心的技术，为不同参与者的多种目的服务，并提供一整套培训功能。具体地说，这个项目将调查几项研究任务：用于同时跟踪、识别和识别用户的智能无线传感系统；用于综合评估用户行动和对环境影响的智能和自适应传感；用于伤害评估和预测的新的数据驱动方法；用于个性化培训活动和未来工作的基于深度学习的推荐模型；用于创建各种培训环境的协作增强方法和用于分析大规模数据来源的身临其境的分析。这些方法形成了对未来劳动力进行适应性培训的整体环境。作为一个特例，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员协会的密切合作，评估和展示开发的系统在消防员培训方面的应用。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。

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.