Estimating Sit-to-Stand Dynamics Using a Single Depth Camera

Abstract

Kinetic and dynamic motion analysis provides quantitative, functional assessments of human ability that are unobtainable through static imaging methods or subjective surveys. While biomechanics facilities are equipped to perform this measurement and analysis, the clinical translation of these methods is limited by the specialised skills and equipment needed. This paper presents and validates a method for estimating dynamic effects such as joint torques and body momenta using a single depth camera. An allometrically scaled, sagittal plane dynamic model is used to estimate the joint torques at the ankles, knees, hips, and low back, as well as the torso momenta, and shear and normal loads at the L5-S1 disc. These dynamic metrics are applied to the sit-to-stand motion and validated against a gold-standard biomechanical system consisting of full-body active motion-capture and force sensing systems. The metrics obtained from the proposed method were found have excellent concordance with peak metrics that are consistent with prior biomechanical studies. This suggests the feasibility of using this system for rapid clinical assessment, with applications in diagnostics, longitudinal tracking, and quantifying patient recovery.

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

“…The STS test obtained good correlation data (r = 0.59-0.73), reliability (ICC = 0.64-0.75), and responsiveness (AUC = 0.64-0.77). The STS test is one of the functional tests that evaluates the strength of the most used lower limbs [43] and it has already been used as a test to study the reliability of the depth chamber, but never in LBP patients [44]. Galna et al (2014) and Matthews et al (2019) compared a RGB-D camera with an active motion capture system with markers in patients with Parkinson's disease (r = 0.99, ICC = 0.98) [20] and healthy patients (ICC = 0.97-0.98, mean absolute error = 1.7-2.8) [44], respectively.…”

“…The STS test is one of the functional tests that evaluates the strength of the most used lower limbs [43] and it has already been used as a test to study the reliability of the depth chamber, but never in LBP patients [44]. Galna et al (2014) and Matthews et al (2019) compared a RGB-D camera with an active motion capture system with markers in patients with Parkinson's disease (r = 0.99, ICC = 0.98) [20] and healthy patients (ICC = 0.97-0.98, mean absolute error = 1.7-2.8) [44], respectively. They obtained good results but they measured the linear displacement of the head [20] and the center of mass [44], unlike this study, which took the angle formed by the trunk flexion.…”

“…Galna et al (2014) and Matthews et al (2019) compared a RGB-D camera with an active motion capture system with markers in patients with Parkinson's disease (r = 0.99, ICC = 0.98) [20] and healthy patients (ICC = 0.97-0.98, mean absolute error = 1.7-2.8) [44], respectively. They obtained good results but they measured the linear displacement of the head [20] and the center of mass [44], unlike this study, which took the angle formed by the trunk flexion. Regarding the agreement, the data were more compact around the average compared to the other tests and had lower limits (SD = 12.74, 4.21, 0.39) than the bending test.…”

Detection of sitting posture using hierarchical image composition and deep learning

“…Currently, the state of the art in non-invasive posture tracking is depth and image processing (Abobakr, Hossny & Nahavandi, 2018;Matthew et al, 2019;Camalan et al, 2018). For example, Huang & Gao (2019) reconstruct realistic 3D human poses using the 3D coordinates of joint points captured by the depth camera and employ conformal geometric algebra to improve human limb modelling.…”

Reachable Workspace and Proximal Function Measures for Quantifying Upper Limb Motion

“…This approach has been used in the collection and analysis of sit-to-stand movements [24]. The kinematics and dynamics of this motion can be recovered, providing estimates of loading in the low-back and ground reaction forces [25]. This method of combining a depth camera system with a rigid body model and a fixed clinical protocol resulted in the identification of cohort specific variables of performance [26].…”