)1 Computer Vision and Image Understanding Laboratory, Department of Computer Science and Computer Engineering, University of Arkansas, Fayetteville, AR 72701, USA; [EMAIL_ADDRESS] (K.L. )1 Computer Vision and Image Understanding Laboratory, Department of Computer Science and Computer Engineering, University of Arkansas, Fayetteville, AR 72701, USA; [EMAIL_ADDRESS] (K.L. )1 Computer Vision and Image Understanding Laboratory, Department of Computer Science and Computer Engineering, University of Arkansas, Fayetteville, AR 72701, USA; [EMAIL_ADDRESS] (K.L. )1 Computer Vision and Image Understanding Laboratory, Department of Computer Science and Computer Engineering, University of Arkansas, Fayetteville, AR 72701, USA; [EMAIL_ADDRESS] (K.L. )1 Computer Vision and Image Understanding Laboratory, Department of Computer Science and Computer Engineering, University of Arkansas, Fayetteville, AR 72701, USA; tth032@uark. can be taken out of the lab to measure more naturalistic movements. However, wearable systems are still expensive and require time consuming and accurate sensor placement. Recent advances in computer vision-based tracking of videos show promise for gait analysis occurring in natural environments without requiring expensive equipment or placement of sensors. Such video data are easier to capture from patients and inexpensive to process. The potential clinical utility of video-based pose-tracking has recently been demonstrated in studies on infants [8], healthy adults [9], older adults at risk of falls [10], and children with cerebral palsy [11]. With regards to gait analysis, these studies have attempted to measure either joint kinematics [11] or spatiotemporal parameters, such as step length and cadence [9], from a single video camera and have achieved moderate accuracy. This moderate accuracy can be attributed in part to the machine learning techniques that have been employed. The purpose of our study was to use more advanced deep learning techniques to achieve better accuracy of video-based gait analysis compared to the gold-standard optical motion capture. In our perspective, two barriers have prevented wider use of video based recognition for human gait analysis. First, interest in video based recognition that uses pose estimation for keypoints extraction is relatively new and has been primarily confined to researchers in computer science and related fields. Second, there is expectation for a validation of the gait parameters as calculated by video based recognition approaches against gold standard method, i.e., optical motion capture systems. The goals of this study were two-fold: (1) extract the keypoints from a video using OpenPose [12], then predict quantitative gait metrics commonly used in clinical gait analysis; and (2) propose a novel method based on graph convolutional neural network used for the prediction of quantitative gait metrics and also can be applied for classification tasks. The proposed method were trained on 1792 videos of 1026 unique patients with cerebral palsy disorder [13]. The clinical gait analysis method was used on the optical motion capture data to calculate the gait parameter values and used as a ground truth for our proposed network. The gait parameters that were used as metrics in this study were walking speed, cadence, gait deviation index, and knee flexion angle at maximum extension.