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

这个I-Corps项目更广泛的影响/商业潜力是开发一个软件平台，结合可穿戴传感器，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，传统的课堂培训环境可能无法为建筑行业当前的挑战做好充分的准备。 拟议的软件平台的目的是捕捉性能和个性化学习的非侵入性的方式，可以使重新思考目前的教学方法。所提出的技术提供了智能培训系统，该系统观察人类指标，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支持，并且包括对个体工人的能力、工作历史、任务的目标以及任务的先前表现的个性化跟踪。该项目的目的是建筑，工程和建筑工人培训;然而，拟议的平台可以适用于其他劳动密集型行业。这个I-Corps项目的基础是为建筑工人开发个性化的人工智能（AI）驱动的培训，其中包括非侵入性措施。 所提出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收集的实时心理生理数据（例如，眼跟踪器、脑电图、皮肤电活动和光电体积描记法）。 传感器被设计成使用多模态异构传感器数据来测量、跟踪和预测工人的表现和能力。从这项研究中产生的预测模型可能有助于显着减少事故，以及提供一个关键的验证措施，以确认培训计划的有效性，提高工人的风险分析技能。在真实的建筑工地验证，算法，分类器和预测模型的研究揭示了生理指标的特点培训效果。由于这项研究的结果将训练熟练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的训练环境奠定了基础，为建筑等动态和危险工作场所的每个用户提供最佳的挑战。这些成果挑战了建筑培训的被动模式，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 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.