3D Tracking of Human Motion Using Visual Skeletonization and Stereoscopic Vision

• Published in: Frontiers in bioengineering and biotechnology Vol. 8; p. 181
• Main Authors: Zago, Matteo, Luzzago, Matteo, Marangoni, Tommaso, De Cecco, Mariolino, Tarabini, Marco, Galli, Manuela
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 05.03.2020
• Subjects: artificial intelligence; Bioengineering and Biotechnology; computer vision; gait analysis; markerless motion capture; movement measurement; movement measurement; markerless motion capture; artificial intelligence; gait analysis; computer vision
• ISSN: 2296-4185; 2296-4185
• DOI: 10.3389/fbioe.2020.00181

The design of markerless systems to reconstruct human motion in a timely, unobtrusive and externally valid manner is still an open challenge. Artificial intelligence algorithms based on automatic landmarks identification on video images opened to a new approach, potentially e-viable with low-cost hardware. OpenPose is a library that t using a two-branch convolutional neural network allows for the recognition of skeletons in the scene. Although OpenPose-based solutions are spreading, their metrological performances relative to video setup are still largely unexplored. This paper aimed at validating a two-cameras OpenPose-based markerless system for gait analysis, considering its accuracy relative to three factors: cameras' relative distance, gait direction and video resolution. Two volunteers performed a walking test within a gait analysis laboratory. A marker-based optical motion capture system was taken as a reference. Procedures involved: calibration of the stereoscopic system; acquisition of video recordings, simultaneously with the reference marker-based system; video processing within OpenPose to extract the subject's skeleton; videos synchronization; triangulation of the skeletons in the two videos to obtain the 3D coordinates of the joints. Two set of parameters were considered for the accuracy assessment: errors in trajectory reconstruction and error in selected gait space-temporal parameters (step length, swing and stance time). The lowest error in trajectories (~20 mm) was obtained with cameras 1.8 m apart, highest resolution and straight gait, and the highest (~60 mm) with the 1.0 m, low resolution and diagonal gait configuration. The OpenPose-based system tended to underestimate step length of about 1.5 cm, while no systematic biases were found for swing/stance time. Step length significantly changed according to gait direction ( = 0.008), camera distance ( = 0.020), and resolution ( < 0.001). Among stance and swing times, the lowest errors (0.02 and 0.05 s for stance and swing, respectively) were obtained with the 1 m, highest resolution and straight gait configuration. These findings confirm the feasibility of tracking kinematics and gait parameters of a single subject in a 3D space using two low-cost webcams and the OpenPose engine. In particular, the maximization of cameras distance and video resolution enabled to achieve the highest metrological performances.