Identify Finger Rotation Angles With ArUco Markers and Action Cameras

Abstract

Measuring the motions of human hand joints is often a challenge due to the high number of degrees-of-freedom. In this study, we proposed a hand tracking system utilizing action cameras and ArUco markers to continuously measure the rotation angles of hand joints during motion. Three methods were developed to estimate the joint rotation angles. The pos-based method transforms marker positions to a reference coordinate system and extracts a hand skeleton to identify the rotation angles. Similarly, the orient-x-based method calculates the rotation angles from the transformed x-orientations of the detected markers in the reference coordinate system. In contrast, the orient-mat-based method first identifies the rotation angles in each camera coordinate system using the detected orientations and then synthesizes the results regarding each joint. Experiment results indicated that the repeatability errors with one camera regarding different marker sizes were around 2.64–27.56 deg and 0.60–2.36 deg using the marker positions and orientations, respectively. With multiple cameras employed, the joint rotation angles measured by using the three methods were compared with that measured by a goniometer. Comparison results indicated that the results of using the orient-mat-based method are more stable and efficient and can describe more types of movements. The effectiveness of this method was further verified by capturing hand movements of several participants. Therefore, it is recommended for measuring joint rotation angles in practical setups.