I-Corps: Personalized AI-Driven Training for Construction Workers with Non-Intrusive Measures

I-Corps：采用非侵入性措施为建筑工人提供个性化人工智能驱动培训

• 批准号: 2330278
• 负责人: Behzad Esmaeili
• 金额: $ 5万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-15 至 2024-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2330278&HistoricalAwards=false
• 关键词: Corps; Personalized; AI; Driven; Training

The broader impact/commercial potential of this I-Corps project is the development of a software platform in combination with wearable sensors for training construction workers. Currently, construction companies rely on training programs to improve construction workers’ skills. However, traditional classroom-based training environments may not adequately prepare workers for the current challenges in the construction industry. The proposed software platform is designed to capture performance and personalize learning in non-invasive ways that may enable rethinking of the current pedagogical approach. The proposed technology provides smart training systems that observe human metrics to strengthen workplace and academic educational practices and knowledge acquisition among diverse learners. In addition, the adaptive systems may incorporate mixed reality that provide context-dependent support from multiple sources of information and include personalized tracking of the individual worker’s capabilities, work history, goals for the task, and prior performance on the task. The project is aimed at architecture, engineering, and construction worker training; however, the proposed platform may be adapted for other labor-intensive industries. This I-Corps project is based on the development of personalized artificial intelligence (AI)-driven training for construction workers that includes non-intrusive measures. The proposed technology uses the Human-Error Detection Framework that harnesses real-time psychophysiological data collected from wearable sensors (e.g., eye tracker, electroencephalogram, electrodermal activity, and photoplethysmography). The sensors are designed to measure, track, and predict workers’ performance and capabilities using the multimodal heterogeneous sensor data. Predictive models resulting from this study may contribute to significant accident reduction as well as provide a critical validation measure to confirm the effectiveness of training programs on enhancing workers' risk-analysis skills. Validated at real construction jobsites, the algorithms, classifiers, and predictive models developed by the research reveal which physiological metrics characterize training effectiveness. Since the results of this study link training proficiency to direct measures of cognitive load and attentional demands, it lays the foundation for developing personalized training environments that provide the optimum amount of challenge for each user in dynamic and hazardous workplaces such as construction. These results challenge the passivity paradigm of construction training by creating methods to boost workers’ cognitive abilities by considering their individual differences to overcome challenges on work sites.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.

这个I-Corps项目的更广泛的影响/商业潜力是，与可穿戴的传感器结合使用用于培训建筑工人的软件平台。目前，建筑公司依靠培训计划来提高建筑工人的技能。但是，传统的基于课堂的培训环境可能无法充分为工人准备建筑行业的挑战。提出的软件平台旨在以非侵入性方式捕获性能和个性化学习，从而可以重新思考当前的教学方法。拟议的技术提供了智能培训系统，可观察人类指标，以加强不同学习者之间的工作场所和学术教育实践和知识的获取。此外，自适应系统可能会结合混合现实，该现实可提供来自多个信息来源的上下文支持，并包括对单个工人能力的个性化跟踪，工作历史记录，任务目标以及对任务的先前绩效。该项目针对建筑，工程和建筑工人培训；但是，建议的平台可能适用于其他劳动密集型行业。这个I-Corps项目基于针对包括非侵入措施的建筑工人的个性化人工智能（AI）驱动的培训。提出的技术使用了人类纠正框架，该框架利用从可穿戴传感器（例如眼镜跟踪器，脑电图，电透明度，电胚层活动和光插图学）收集的实时心理生理数据。这些传感器旨在使用多模式异质传感器数据来测量，跟踪和预测工人的性能和功能。这项研究产生的预测模型可能有助于大大减少事故，并提供一项关键的验证措施，以确认培训计划对增强工人的风险分析技能的有效性。在实际的建筑工作地点，算法，分类器和预测模型中得到了验证，该研究表明，哪些物理指标表征了培训有效性。由于这项研究的结果将培训能力链接到直接衡量认知负荷和注意力需求的措施，因此为开发个性化培训环境奠定了基础，这些培训环境为每个用户提供了最佳的挑战，例如施工等动态和危险工作。这些结果通过创建方法来提高工人的认知能力，考虑他们的个体差异来克服工作地点上的挑战，从而挑战了建筑训练的消极范式。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审查标准来评估来通过评估来获得的支持。