The study of joint kinematics and dynamics has broad clinical applications including the identification of pathological motions or compensation strategies and the analysis of dynamic stability. High-end motion capture systems, however, are expensive and require dedicated camera spaces with lengthy setup and data processing commitments. Depth cameras, such as the Microsoft Kinect, provide an inexpensive, marker-free alternative at the sacrifice of joint-position accuracy. In this work, we present a fast framework for adding biomechanical constraints to the joint estimates provided by a depth camera system. We also present a new model for the lower lumbar joint angle. We validate key joint position, angle, and velocity measurements against a gold standard active motion-capture system on ten healthy subjects performing sit-to-stand (STS). Our method showed significant improvement in Mean Absolute Error and Intraclass Correlation Coefficients for the recovered joint angles and position-based metrics. These improvements suggest that depth cameras can provide an accurate and clinically viable method of rapidly assessing the kinematics and kinetics of the STS action, providing data for further analysis using biomechanical or machine learning methods.

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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.

Study design A longitudinal cohort study. Objective To define a set of objective biomechanical metrics that are representative of adult spinal deformity (ASD) post-surgical outcomes and that may forecast post-surgical mechanical complications. Summary of background data Current outcomes for ASD surgical planning and post-surgical assessment are limited to static radiographic alignment and patient-reported questionnaires. Little is known about the compensatory biomechanical strategies for stabilizing sagittal balance during functional movements in ASD patients. Methods We collected in-clinic motion data from 15 ASD patients and 10 controls during an unassisted sit-to-stand (STS) functional maneuver. Joint motions were measured using noninvasive 3D depth mapping sensor technology. Mathematical methods were used to attain high-fidelity joint-position tracking for biomechanical modeling. This approach provided reliable measurements for biomechanical behaviors at the spine, hip, and knee. These included peak sagittal vertical axis (SVA) over the course of the STS, as well as forces and muscular moments at various joints. We compared changes in dynamic sagittal balance (DSB) metrics between pre- and post-surgery and then separately compared pre- and post-surgical data to controls. Results Standard radiographic and patient-reported outcomes significantly improved following realignment surgery. From the DSB biomechanical metrics, peak SVA and biomechanical loads and muscular forces on the lower lumbar spine significantly reduced following surgery (− 19 to − 30%, all p < 0.05). In addition, as SVA improved, hip moments decreased (− 28 to − 65%, all p < 0.05) and knee moments increased (+ 7 to + 28%, p < 0.05), indicating changes in lower limb compensatory strategies. After surgery, DSB data approached values from the controls, with some post-surgical metrics becoming statistically equivalent to controls. Conclusions Longitudinal changes in DSB following successful multi-level spinal realignment indicate reduced forces on the lower lumbar spine along with altered lower limb dynamics matching that of controls. Inadequate improvement in DSB may indicate increased risk of post-surgical mechanical failure. Graphical abstract These slides can be retrieved under Electronic Supplementary Material. Electronic supplementary material The online version of this article (10.1007/s00586-019-05925-2) contains supplementary material, which is available to authorized users.

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