A comparative study of in-field motion capture approaches for body kinematics measurement in construction

Abstract

SummaryDue to physically demanding tasks in construction, workers are exposed to significant safety and health risks. Measuring and evaluating body kinematics while performing tasks helps to identify the fundamental causes of excessive physical demands, enabling practitioners to implement appropriate interventions to reduce them. Recently, non-invasive or minimally invasive motion capture approaches such as vision-based motion capture systems and angular measurement sensors have emerged, which can be used for in-field kinematics measurements, minimally interfering with on-going work. Given that these approaches have pros and cons for kinematic measurement due to adopted sensors and algorithms, an in-depth understanding of the performance of each approach will support better decisions for their adoption in construction. With this background, the authors evaluate the performance of vision-based (RGB-D sensor-, stereovision camera-, and multiple camera-based) and an angular measurement sensor-based (i.e., an optical encoder) approach to measure body angles through experimental testing. Specifically, measured body angles from these approaches were compared with the ones obtained from a marker-based motion capture system that has less than 0.1 mm of errors. The results showed that vision-based approaches have about 5–10 degrees of error in body angles, while an angular measurement sensor-based approach measured body angles with about 3 degrees of error during diverse tasks. The results indicate that, in general, these approaches can be applicable for diverse ergonomic methods to identify potential safety and health risks, such as rough postural assessment, time and motion study or trajectory analysis where some errors in motion data would not significantly sacrifice their reliability. Combined with relatively accurate angular measurement sensors, vision-based motion capture approaches also have great potential to enable us to perform in-depth physical demand analysis such as biomechanical analysis that requires full-body motion data, even though further improvement of accuracy is necessary. Additionally, understanding of body kinematics of workers would enable ergonomic mechanical design for automated machines and assistive robots that helps to reduce physical demands while supporting workers' capabilities.

Motion Capture Technology in Industrial Applications: A Systematic Review

“…Data synchronisation was occasionally reported as part of the pre-processing stage and included in the data fusion algorithm [ 24 , 34 , 36 ], but technical details were frequently omitted in the reviewed studies [ 27 , 28 ]; yet, when the synchronization strategy was reported, a master control unit [ 36 , 50 , 51 , 54 ] or a common communication network [ 15 , 31 , 67 ] were used. Different sampling rates of data streams were addressed by linear interpolation and cross-correlation [ 73 ] techniques, or by introducing a known event that triggers all the sensors [ 29 , 47 , 49 , 55 ].…”

“…Two works also used the Oculus Rift virtual reality headset to remotely assess industrials locations and control robotic elements [ 39 , 43 ]. The tracking accuracy of the developed systems was directly assessed against gold-standard MoCap systems (e.g., Vicon or Optotrack; Table 8 , in bold) in six works [ 14 , 15 , 55 , 59 , 73 , 77 ], while the classification or identification accuracy of a process was frequently evaluated with visual inspection of video or phone cameras [ 15 , 29 , 36 , 44 , 60 , 63 , 69 ]. A thorough diagram showing the connections between type of industry, application and MoCap system, for each considered study is also presented on Figure 5 .…”

“…The use of MoCap technologies in industry has been steadily increasing over the years, enabling the development of smart solutions that can provide advanced position estimation, aid in automated decision-making processes, improve infrastructure inspection, enable teleoperation, and increase the safety of human workers. The majority of the MoCap systems that were used in industry were IMU-based (in 70% of the studies, Table 3 ), whilst camera-based sensors were employed less frequently (40%), most likely due to their increased operational and processing cost, and other functional limitations, such as camera obstructions by workers and machinery which were reported as the most challenging issues [ 25 , 45 , 55 ]. Findings suggest that the selection of the optimal MoCap system to adopt was primarily driven by the type of application ( Figure 5 ); for instance, monitoring and quality control was mainly achieved via IMUs sensors, while productivity improvement via camera-based (marker-less) systems.…”

“…Direct evaluation of the accuracy and tracking performance of a developed MoCap system [ 14 , 55 ] was generally achieved through comparisons with a high accuracy camera-based system. This is so far the most reliable process, as it guarantees an appropriate ground truth reference.…”

Modeling and Simulation of Athlete’s Error Motion Recognition Based on Computer Vision

“…As shown in Figure 3, most of the three-dimensional scanned models of the human body are expensive and require a long time to do postprocessing, and the initial model obtained from the scan because there is no joint structure information, in the posture estimation before the need to set the skeletal structure, for its skin, etc. is increases the initial model to a certain extent [17].…”