A comparison of lower extremity squat, lunge, and hip hinge kinematics between marker based and markerless motion capture systems.

The resource-intensive nature of traditional marker-based motion capture systems limits opportunities for quantitative motion analysis. However, the advancement of markerless motion capture technology yields tremendous promise for accessible kinematic analyses beyond conventional research settings. This work compared the lower limb kinematics measured by a neural network-driven markerless motion capture system to those from a standard marker-based motion capture system during squat, hip hinge, and reverse lunge tasks. Fourteen adults performed three repetitions of each movement while being recorded simultaneously by two iPads and 17 infrared optical motion capture cameras. The mean peak cross-correlation values indicated high agreement for knee and hip flexion (>0.95) and poor agreement for hip adduction, knee varus, and internal rotation (<0.49) for all tasks; agreement for hip internal rotation and ankle flexion was task dependent (0.27-0.97). The average root mean square error indicated joint-specific offsets between systems, as values ranged from 6.43° to 12.32° for the knee, 11.25° to 17.35° for the hip, and 21.51° to 25.67° for the ankle. These findings suggest that, while the markerless system demonstrates the ability to capture gross motor patterns in the sagittal plane, further refinement of the underlying models is needed to improve the validity of the system's measurements.

Keywords

Markerless motion capture, computer vision, kinematics, movement measurement, pose estimation

Conflict of interest statement

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: At the time of data collection, Steven Hirsch was employed by Uplift Labs. Use of the Uplift Labs software was provided in kind.