Abstract
Abstract 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.
Highlights
During a construction project, data collection is critical to the evaluation and control of ongoing project performances
Fernandez-Baena et al (2012) conduct an experiment associated with rehabilitation treatments to compare the accuracy between a Kinect—combined with Natural Interaction Technology for End-user (NITE)—and a VICON in terms of the rotation angles of knee, hip, and shoulder joints, defined as angles between two vectors of body parts; the results show that the differences in rotation angles range from 6.78 to 8.98 degrees for a knee, from 5.53 to 9.92 degrees for a hip, and from 7 to 13 degrees for a shoulder
To compute the tracking errors, a VICON is used as the ground truth for motion tracking, and the iPi Motion Capture solution is used with Kinect sensors to track the 3D positions of a human subject and extract 3D skeletons; the iPi Motion capture system estimates human poses mainly based on the depth measurements of a human body, and is less affected by a performer’s appearance
Summary
Data collection is critical to the evaluation and control of ongoing project performances. On the other hand, Ray and Teizer (2012) utilize a Kinect for the pose analysis of construction workers to classify awkward postures based on ergonomic rules during safety and health training, and Han et al (2013) study the unsafe action detection of workers for safety behavior monitoring with motion capture data from a Kinect. Though limited to indoor applications, the Kinect still has the following notable advantages for motion sensing: (1) it requires no additional body attachment (e.g., markers, a special suit), which allows for worker observation without the interference of ongoing work; (2) the cost of a sensor (e.g., approximately 150– 250 USD) is quite competitive, compared with other motion capture systems (e.g., approximately 96–120K USD for a marker-based VICON system) (Han et al 2013); (3) the minimum number of sensors for motion tracking is only one Kinect; and (4) it provides an easyto-use and easy-to-carry means for data collection in a field setting
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