Empirical assessment of a RGB-D sensor on motion capture and action recognition for construction worker monitoring

Han, SangUk; Achar, Madhav; Lee, SangHyun; Pena-Mora, Feniosky A.

Background: For construction management, data collection is a critical process for gathering and measuring information for the evaluation and control of ongoing project performances. Taking into account that construction involves a significant amount of manual work, worker monitoring can play a key role in analyzing operations and improving productivity and safety. However, time-consuming tasks involved in field observation have brought up the issue of implementing worker observation in daily management practice. Methods: In an effort to address the issue, this paper investigates the performances of a cost-effective and portable RGB-D sensor, based on recent research efforts extended from our previous study. The performance of an RGB-D sensor is evaluated in terms of (1) the 3D positions of the body parts tracked by the sensor, (2) the 3D rotation angles at joints, and (3) the impact of the RGB-D sensor’s accuracy on motion analysis. For the assessment, experimental studies were undertaken to collect motion capture datasets using an RGB-D sensor and a marker-based motion capture system, VICON, and to analyze errors as compared with the VICON used as the ground truth. As a test case, 25 trials of ascending and descending during ladder climbing were recorded simultaneously with both systems, and the resulting motion capture datasets (i.e., 3D skeleton models) were temporally and spatially synchronized for their comparison. Results: Through the comparative assessment, we found a discrepancy of 10.7 cm in the tracked locations of body parts, and a difference of 16.2 degrees in rotation angles. However, motion detection results show that the inaccuracy of an RGB-D sensor does not have a considerable effect on action recognition in the experiment. Conclusions: This paper thus provides insight into the accuracy of an RGB-D sensor on motion capture in various measures and directions of further research for the improvement of accuracy.


Also Published In

Visualization in Engineering

More About This Work

Academic Units
Civil Engineering and Engineering Mechanics
BioMed Central
Published Here
September 9, 2014


Motion capture, Action recognition, Motion classification, RGB-D sensor, Machine learning