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

Abstract

Background: Markerless (ML) motion capture systems have recently become available for biomechanics applications. Evidence has indicated the potential feasibility of using an ML system to analyze lower extremity kinematics. However, no research has examined ML systems’ estimation of the lower extremity joint moments and powers. This study aimed to compare lower extremity joint moments and powers estimated by marker-based (MB) and ML motion capture systems. Methods: Sixteen volunteers ran on a treadmill for 120 s at 3.58 m/s. The kinematic data were simultaneously recorded by 8 infrared cameras and 8 high-resolution video cameras. The force data were recorded via an instrumented treadmill. Results: Greater peak magnitudes for hip extension and flexion moments, knee flexion moment, and ankle plantarflexion moment, along with their joint powers, were observed in the ML system compared to an MB system (p < 0.0001). For example, greater hip extension (MB: 1.42 ± 0.29 vs. ML: 2.27 ± 0.45) and knee flexion (MB: −0.74 vs. ML: −1.17 nm/kg) moments were observed in the late swing phase. Additionally, the ML system’s estimations resulted in significantly smaller peak magnitudes for knee extension moment, along with the knee production power (p < 0.0001). Conclusions: These observations indicate that inconsistent estimates of joint center position and segment center of mass between the two systems may cause differences in the lower extremity joint moments and powers. However, with the progression of pose estimation in the markerless system, future applications can be promising.

Comparison of concurrent and asynchronous running kinematics and kinetics from marker-based motion capture and markerless motion capture under two clothing conditions

“…Joint position differences between marker-based and markerless concurrent data were found to be 3 cm or smaller for all joints, which is consistent with previous comparisons of these systems during treadmill walking (Kanko et al, 2021a). Joint position component differences were also mostly consistent with those of Tang et al, however they did not report 3D Euclidean distances (Tang et al, 2022).…”

“…Segment and joint angles demonstrated similar agreement between motion capture systems as in our previous study for treadmill walking gait (Kanko et al, 2021a), and excellent agreement across asynchronous markerless clothing conditions. Sagittal plane joint angles between motion capture systems had mean RMSD of 5.2° or less for all three joints, consistent with the findings of Tang et al (Tang et al, 2022). The differences between systems measured as mean RMSD over the entire gait cycle are comparable to reported minimal detectable change (MDC) values for marker-based motion capture of treadmill running at initial contact (Bramah et al, 2021).…”

“…However, the ankle inversion/eversion moments displayed different patterns, with the marker-based moments being mostly inversion moments while the markerless moments were mostly eversion moments. Differences in sagittal plane joint moment peak magnitudes between marker-based and markerless joint moments were mostly consistent with the findings of Tang et al, but they did not report frontal or transverse plane joint moments for either system (Tang et al, 2022). The differences in ankle inversion/eversion and toe-in/toe-out moments between systems can be explained by differences in the foot and shank segment orientations, as the selection and orientation of the coordinate system in which joint moments are resolved can entirely change their direction (O’Connor and Hamill, 2005).…”

“…Markerless motion capture systems such as Theia3D (Theia Markerless Inc., Kingston, Ontario, Canada) can measure kinematics using multiple synchronized video cameras with few restrictions on the data collection environment, subject attire, and a significantly reduced collection time compared to marker-based motion capture. Markerless systems have demonstrated success in measuring comparable kinematic signals to those from marker-based motion capture during walking (Kanko et al, 2021a; Riazati et al, 2022), boxing (Lahkar et al, 2022), and running (Tang et al, 2022). While marker-based motion capture may not provide a true gold standard for biomechanical data collection due to issues with soft tissue artefact and marker placement variation, it is a historically accepted technology and forms the basis of much of the running biomechanics literature.…”

Markerless motion capture estimates of lower extremity kinematics and kinetics are comparable to marker-based across 8 movements

“…However, the fidelity of markerless motion capture in quantifying 3D joint kinematics and kinetics has not been fully verified against standard marker-based techniques. To our knowledge, only three recent studies compared lower extremity 3D kinematics or kinetics from concurrent marker-based and markerless motion capture (Ito et al, 2022; Kanko et al, 2021a; Tang et al, 2022). Kanko et al (2021a) found comparable kinematics between the two systems during treadmill walking.…”