Validation, Reliability, and Responsiveness Outcomes of Kinematic Assessment with an RGB-D Camera to Analyze Movement in Subacute and Chronic Low Back Pain

Abstract

Background: The RGB-D camera is an alternative to asses kinematics in order to obtain objective measurements of functional limitations. The aim of this study is to analyze the validity, reliability, and responsiveness of the motion capture depth camera in sub-acute and chronic low back pain patients. Methods: Thirty subjects (18–65 years) with non-specific lumbar pain were screened 6 weeks following an episode. RGB-D camera measurements were compared with an inertial measurement unit. Functional tests included climbing stairs, bending, reaching sock, lie-to-sit, sit-to-stand, and timed up-and-go. Subjects performed the maximum number of repetitions during 30 s. Validity was analyzed using Spearman’s correlation, reliability of repetitions was calculated by the intraclass correlation coefficient and the standard error of measurement, and receiver operating characteristic curves were calculated to assess the responsiveness. Results: The kinematic analysis obtained variable results according to the test. The time variable had good values in the validity and reliability of all tests (r = 0.93–1.00, (intraclass correlation coefficient (ICC) = 0.62–0.93). Regarding kinematics, the best results were obtained in bending test, sock test, and sit-to-stand test (r = 0.53–0.80, ICC = 0.64–0.83, area under the curve (AUC) = 0.55–84). Conclusion: Functional tasks, such as bending, sit-to-stand, reaching, and putting on sock, assessed with the RGB-D camera, revealed acceptable validity, reliability, and responsiveness in the assessment of patients with low back pain (LBP). Trial registration: ClinicalTrials.gov NCT03293095 “Functional Task Kinematic in Musculoskeletal Pathology” 26 September 2017

Human motion capture for movement limitation analysis using an RGB-D camera in spondyloarthritis: a validation study

“…This could cause errors in the data collection of the camera. These results agree with those shown in other study about the correlation and reliability in tasks with a large displacement of the trunk [11].…”

“…Bending and Chair tasks had the best results in the analysed properties among other functional tasks. Other previous studies with low back pain patients showed similar results as this study in the displacement, velocity and acceleration outcomes (r = 0.53-0.80, ICC = 0.55-0.84) [11]. Bending task showed moderate to excellent results in all the outcomes with an RGB-D camera, but the validity to analyse the linear acceleration in Chair task was low (r = 0.37).…”

“…A human motion capture system with a RGB-D camera or depth camera has a relevant application in research and industry due to its easy use [9] and could be a reliable and valid tool that helps to understand the damage that SpA can induce in these patients. A RGB-D camera has been useful in other health issues, such as Parkinson's disease or subacute and chronic low back pain [10,11]. Furthermore, the RGB-D camera has been checked by Moreno et al 2017 in Timed Up and Go (TUG) test, showing positive reliability of the camera (ICC = 0.81-0.84) and adequate correlation with an inertial measurement unit (IMU) in balance tests (r = 0.59-0.98) [12].…”

Enhancement of RGB-D Image Alignment Using Fiducial Markers

“…The optimization is performed as a bundle adjustment, meaning the objective of the optimization is to refine the camera poses and the 3D point positions. As such, the set of parameters to be optimized Φ, is defined as (4) where i refers to the i-th camera, of the set of I cameras, and j refers to the j-th aruco marker, of the set of J aruco markers. Notice that, in this vector, the rotation for one camera (r 1 , r 2 , r 3 ) is represented through the axis/angle parameterization, as opposed to the 3 × 3 rotation matrix format of the camera's extrinsic matrix.…”

“…Three-dimensional (3D) reconstruction is the creation of 3D models from the captured shape and appearance of real objects. It is a field that has its roots in several areas within computer vision and graphics, and has gained high importance in others, such as architecture [1], robotics [2], autonomous driving [3], medicine [4], agriculture [5], and archaeology [6]. Most of the current model acquisition technologies are based on LiDAR (Light Detection And Ranging) [7,8], RGB-D cameras [9,10], and image-based approaches, such as photogrammetry [11,12].…”