Model Pre-Training Process In the training of end-to-end networks, two data sources are adopted in the training process to get the parameterized SMPL model. The end-to-end 3D human-body pose estimation based on single-frame images. Experimental results show that our low-cost method based on RGB video data can achieve similar results to commercial motion capture platform with RGB-D video data. Then we defined several different tasks, including the speed of the movements, the position of the subject, the orientation of the subject, and the complexity of the movements.
To make a contrast, we first built a motion capture platform through two Kinect (V2) devices and iPi Soft series software to obtain depth-camera video sequences and monocular-camera video sequences respectively. In addition, we compared the proposed 3D human pose recovery method with the commercial motion capture platform to prove the effectiveness of the proposed method. Combined with the correlation of video sequences, a 3D human pose recovery method based on video streams is proposed, which uses the correlation between videos to generate a smoother 3D pose. The model is pre-trained using open-source human pose datasets (2) Human-body pose generation based on video streams. We use 2D/3D skeleton point constraints, human height constraints, and generative adversarial network constraints to obtain a more accurate human-body model. The method is divided into two main stages: (1) 3D human pose estimation based on a single frame image. This paper proposes a novel approach to estimate 3D human action via end-to-end learning of deep convolutional neural network to calculate the parameters of the parameterized skinned multi-person linear model. 10.Using video sequences to restore 3D human poses is of great significance in the field of motion capture. Knee Surg Sports Traumatol Arthrosc 2006 14:778–88. A test battery for evaluating hop performance in patients with an ACL injury and patients who have undergone ACL reconstruction. Identification of athletes at future risk of anterior cruciate ligament ruptures by neuromuscular screening. Injury mechanisms for anterior cruciate ligament injuries in team handball: a systematic video analysis. Olsen OE, Myklebust G, Engebretsen L, et al. Trunk, pelvis, hip, and knee kinematics, hip strength, and gluteal muscle activation during a single-leg squat in males and females with and without patellofemoral pain syndrome. Nakagawa TH, Moriya ET, Maciel CD, et al.
Can serious injury in professional football be predicted by a preseason functional movement screen? N Am J Sports Phys Ther 2007 2:147–58. This study supports the use of dual Kinect v2 configuration with the iPi software as a valid tool for assessment of sagittal and frontal plane hip and knee kinematic parameters but not axial rotation in athletes.ģD marker based system Kinect kinematics markerless motion capture. Poor correlation was seen for the rotation movements. For peak angles, results showed excellent agreement for knee flexion. The tests were simultaneously recorded using both a marker-based motion capture system and two Kinect v2 cameras using iPi Mocap Studio software.Įxcellent agreement between systems for the flexion/extension range of motion of the shin during all tests and for the thigh abduction/adduction during SLS were seen. Three dimensional lower limb kinematics during three functional tests: Single Leg Squat (SLS), Single Leg Jump, Modified Counter-movement Jump. To determine whether a dual-camera markerless motion capture system can be used for lower limb kinematic evaluation in athletes in a preseason screening setting.