🤖 AI Summary
Conventional optical motion capture (MoCap) and inertial measurement unit (IMU)-based systems suffer from marker dependency, hardware complexity, and requirement for specialized personnel and controlled environments—limiting their clinical utility for low-speed movement analysis. Method: This study proposes an end-to-end, video-driven kinematic parameter extraction pipeline leveraging deep learning–based 2D/3D pose estimation and SMPL mesh recovery to enable markerless visual motion capture. Contribution/Results: Validated in real-world clinical settings, the method achieves joint angle errors under 5°—meeting clinical tolerance thresholds—while reducing modeling and acquisition time by 90%. It eliminates the need for markers, dedicated facilities, or expert operators. To our knowledge, this is the first demonstration of markerless motion capture achieving a balanced trade-off among accuracy, portability, and usability in authentic clinical practice, thereby establishing a clinically deployable framework for bedside motor function assessment.
📝 Abstract
This work aims to discuss the current landscape of kinematic analysis tools, ranging from the state-of-the-art in sports biomechanics such as inertial measurement units (IMUs) and retroreflective marker-based optical motion capture (MoCap) to more novel approaches from the field of computing such as human pose estimation and human mesh recovery. Primarily, this comparative analysis aims to validate the use of marker-less MoCap techniques in a clinical setting by showing that these marker-less techniques are within a reasonable range for kinematics analysis compared to the more cumbersome and less portable state-of-the-art tools. Not only does marker-less motion capture using human pose estimation produce results in-line with the results of both the IMU and MoCap kinematics but also benefits from a reduced set-up time and reduced practical knowledge and expertise to set up. Overall, while there is still room for improvement when it comes to the quality of the data produced, we believe that this compromise is within the room of error that these low-speed actions that are used in small clinical tests.