Productivity assessment helps contractors estimate labor cost and activity duration. Some methods such as work sampling or Data Envelope Analysis can be used to assess productivity. However, they are post-analyzed based on recorded video of construction activities instead of real-time assessment. Their base upon human judgment also limits the feasible sampling rate of the video. This research uses depth cameras to capture joints of human skeleton and builds a system to automatically determine whether a subject's posture is a productive or nonproductive in a real time fashion. When the target activity (e.g., formwork) is known, the system may further categorize the subject's posture into the associated sub-activities (e.g., formwork assembly, formwork nailing). Experiments, which targeted on common construction activities including rebar assembly, formwork assembly, moving materials, reading blueprints, laying bricks, and tiling, were conducted to evaluate the identification accuracy. The results show that accuracies are 92.23%, 80.19%, 90.82%, 90.65%, 62.24%, and 94.40%, respectively.
|Translated title of the contribution||Motion-Sensing Identification System for Construction Operation|
|Original language||Chinese (Traditional)|
|Number of pages||16|
|Journal||Journal of the Chinese Institute of Civil and Hydraulic Engineering|
|State||Published - 1 Mar 2020|