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

I-Corps项目更广泛的影响/商业潜力是开发一个与可穿戴传感器相结合的软件平台，用于培训建筑工人。目前，建筑公司依靠培训计划来提高建筑工人的技能。然而，传统的基于课堂的培训环境可能不足以让工人为当前建筑业的挑战做好准备。拟议的软件平台旨在以非侵入性的方式获取绩效和个性化学习，从而能够重新思考目前的教学方法。拟议的技术提供了观察人类指标的智能培训系统，以加强工作场所和学术教育实践以及不同学习者之间的知识获取。此外，自适应系统可以结合混合现实，该混合现实提供来自多个信息源的上下文相关的支持，并且包括对个体工作者的能力、工作历史、任务的目标和任务的先前表现的个性化跟踪。该项目旨在培训建筑、工程和建筑工人；然而，拟议的平台可能适用于其他劳动密集型行业。这个i-Corps项目是基于为建筑工人开发的个性化人工智能(AI)驱动的培训，其中包括非侵入性措施。建议的技术使用人为错误检测框架，该框架利用从可穿戴传感器(例如，眼睛跟踪器、脑电、皮肤电活动和光体积描记仪)收集的实时心理生理学数据。这些传感器旨在使用多模式不同类型的传感器数据来测量、跟踪和预测员工的表现和能力。这项研究产生的预测模型可能有助于显著减少事故，并提供关键的验证措施，以确认培训计划在提高工人风险分析技能方面的有效性。研究开发的算法、分类器和预测模型在实际建筑工地进行了验证，揭示了哪些生理指标表征了培训效果。由于这项研究的结果将培训熟练程度与认知负荷和注意力需求的直接测量联系起来，因此它为开发个性化培训环境奠定了基础，该环境为每个用户在动态和危险的工作场所(如建筑)提供最佳挑战。这些结果挑战了建筑培训的被动范式，通过考虑工人的个体差异来提高他们的认知能力，以克服工作现场的挑战。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 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.