  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.
