Comparing the accuracy of open-source pose estimation methods for measuring gait kinematics

Abstract

BackgroundOpen-source pose estimation is rapidly reducing the costs associated with motion capture, as machine learning partially eliminates the need for specialized cameras and equipment. This technology could be particularly valuable for clinical gait analysis, which is often performed qualitatively due to the prohibitive cost and setup required for conventional, marker-based motion capture. Research QuestionHow do open-source pose estimation software packages compare in their ability to measure kinematics and spatiotemporal gait parameters for gait analysis? MethodsThis analysis used an existing dataset that contained video and synchronous motion capture data from 32 able-bodied participants while walking. Sagittal plane videos were analyzed using pre-trained algorithms from four open-source pose estimation methods—OpenPose, Tensorflow MoveNet Lightning, Tensorflow MoveNet Thunder, and DeepLabCut—to extract keypoints (i.e., landmarks) and calculate hip and knee kinematics and spatiotemporal gait parameters. The absolute error when using each markerless pose estimation method was computed against conventional marker-based optical motion capture. Errors were compared between pose estimation methods using statistical parametric mapping. ResultsPose estimation methods differed in their ability to measure kinematics. OpenPose and Tensorflow MoveNet Thunder methods were most accurate for measuring hip kinematics, averaging 3.7 ± 1.3 deg and 4.6 ± 1.8 deg (mean ± std) over the entire gait cycle, respectively. OpenPose was most accurate when measuring knee kinematics, averaging 5.1 ± 2.5 deg of error over the gait cycle. MoveNet Thunder and OpenPose had the lowest errors when measuring spatiotemporal gait parameters but were not statistically different from one another. SignificanceThe results indicate that OpenPose significantly outperforms other existing platforms for pose-estimation of healthy gait kinematics and spatiotemporal gait parameters and could serve as an alternative to conventional motion capture systems in clinical and research settings when marker-based systems are not available.