Applications and limitations of current markerless motion capture methods for clinical gait biomechanics

Abstract

Background Markerless motion capture has the potential to perform movement analysis with reduced data collection and processing time compared to marker-based methods. This technology is now starting to be applied for clinical and rehabilitation applications and therefore it is crucial that users of these systems understand both their potential and limitations. This literature review aims to provide a comprehensive overview of the current state of markerless motion capture for both single camera and multi-camera systems. Additionally, this review explores how practical applications of markerless technology are being used in clinical and rehabilitation settings, and examines the future challenges and directions markerless research must explore to facilitate full integration of this technology within clinical biomechanics. Methodology A scoping review is needed to examine this emerging broad body of literature and determine where gaps in knowledge exist, this is key to developing motion capture methods that are cost effective and practically relevant to clinicians, coaches and researchers around the world. Literature searches were performed to examine studies that report accuracy of markerless motion capture methods, explore current practical applications of markerless motion capture methods in clinical biomechanics and identify gaps in our knowledge that are relevant to future developments in this area. Results Markerless methods increase motion capture data versatility, enabling datasets to be re-analyzed using updated pose estimation algorithms and may even provide clinicians with the capability to collect data while patients are wearing normal clothing. While markerless temporospatial measures generally appear to be equivalent to marker-based motion capture, joint center locations and joint angles are not yet sufficiently accurate for clinical applications. Pose estimation algorithms are approaching similar error rates of marker-based motion capture, however, without comparison to a gold standard, such as bi-planar videoradiography, the true accuracy of markerless systems remains unknown. Conclusions Current open-source pose estimation algorithms were never designed for biomechanical applications, therefore, datasets on which they have been trained are inconsistently and inaccurately labelled. Improvements to labelling of open-source training data, as well as assessment of markerless accuracy against gold standard methods will be vital next steps in the development of this technology.

Comparison of Lower Extremity Joint Moment and Power Estimated by Markerless and Marker-Based Systems during Treadmill Running

“…This process is repeated with the entire training data until improvements between each iteration become negligible. The pose estimation algorithm is then tested on the new image and compared to the training dataset [ 38 ]. However, any omissions or biases implicit within training datasets could propagate to situations where the training was weak [ 19 ].…”

Movement and hardware content selection for exergame development

“…Investigators performed validation studies mainly in the field of walking and body posture, while fewer studies have assessed accuracy during more complex multi-joint movements (Ma et al., 2018). Although the time-spatial variables of motion capture systems without markers appear to be equivalent to systems with markers, the movement accuracy of some joints is not good enough (Wade et al, 2022). After we reviewed several possibilities, we decided to use Kinect V2 sensor to track movement in our exergame.…”

Healthcare applications of single camera markerless motion capture: a scoping review

“…Recent improvements to portable and affordable motion capture technology such as the Microsoft Kinect and Leap Motion, which include depth sensors in addition to conventional RGB video cameras, as well as the increased availability of high-resolution smartphone cameras, has inspired an increase in the investigation of SCMoCap for healthcare. Recent reviews of markerless motion capture have been conducted on gait ( Wade et al, 2022 ) and 3D pose estimation ( Desmarais et al, 2021 ). However, they focus mainly on the processing methods and accuracy evaluation of multi-camera motion capture systems with limited discussion on SCMoCap and clinical applications.…”