This article explores natural interaction modalities for human-cyber-physical systems (CPS) interaction in construction. CPS has been applied in construction for many purposes with the promise of improving the safety and productivity of construction operations. However, there is little research on human-CPS interaction in construction. This study proposes two methodologies for human-CPS interactions for construction progress monitoring - a) hand gesture interaction using transfer learning, and b) voice command interaction using natural language processing. User studies with thirty-two users validated the generalizability of the proposed methodologies. The proposed hand gesture recognition method achieved higher accuracy (99.69% vs 87.72%) and speed (36.05ms vs 578.91ms) than the proposed voice command recognition method, though users performed the progress monitoring task more correctly with voice commands than hand gestures (88% vs 66.1%). The main contribution of the study is the development of an ML pipeline and computational framework to recognize hand gestures and voice commands without the need for a large training dataset for human-CPS interaction.

翻译：本文探讨了建筑施工中人-信息-物理系统（CPS）的自然交互模式。CPS在建筑领域已被广泛应用于多种场景，旨在提升施工作业的安全性与效率。然而，关于建筑施工中人-CPS交互的研究尚属鲜见。本研究提出了两种用于施工进度监控的人-CPS交互方法：a) 基于迁移学习的手势交互方法，以及b) 基于自然语言处理的语音指令交互方法。通过32名用户的实验验证，所提方法具有良好的泛化能力。尽管手势识别方法在准确率（99.69% 对 87.72%）与处理速度（36.05ms 对 578.91ms）上均优于语音指令识别方法，但用户在使用语音指令执行进度监控任务时的正确率（88%）高于手势交互（66.1%）。本研究的主要贡献在于开发了一套机器学习流水线与计算框架，无需大规模训练数据集即可实现人-CPS交互中的手势与语音指令识别。