Obtaining lower-body Euler angle time series in an accurate way using depth camera relying on Optimized Kinect CNN

Guo, Jiamin; Zhang, Qin; Chai, Hui; Li, Yibin

doi:10.1016/j.measurement.2021.110461

Cited by 13 publications

(10 citation statements)

References 25 publications

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“…Therefore, several markerless technologies are being examined to solve these issues. The main candidates are either based on Inertial Measurement Units (IMUs) [ 2 , 3 ], depth cameras [ 4 , 5 , 6 ], or a network of RGB cameras [ 7 , 8 , 9 ]. IMUs avoid all camera-related issues such as complex setup and calibration, potential self- and gear obstructions, and can operate in real time; however, they need to be worn by the athlete and are sensitive to drift over time, and to ferromagnetic disturbances.…”

Section: Introductionmentioning

confidence: 99%

Pose2Sim: An End-to-End Workflow for 3D Markerless Sports Kinematics—Part 2: Accuracy

Pagnon

Domalain

Revéret

2022

Sensors

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Two-dimensional deep-learning pose estimation algorithms can suffer from biases in joint pose localizations, which are reflected in triangulated coordinates, and then in 3D joint angle estimation. Pose2Sim, our robust markerless kinematics workflow, comes with a physically consistent OpenSim skeletal model, meant to mitigate these errors. Its accuracy was concurrently validated against a reference marker-based method. Lower-limb joint angles were estimated over three tasks (walking, running, and cycling) performed multiple times by one participant. When averaged over all joint angles, the coefficient of multiple correlation (CMC) remained above 0.9 in the sagittal plane, except for the hip in running, which suffered from a systematic 15° offset (CMC = 0.65), and for the ankle in cycling, which was partially occluded (CMC = 0.75). When averaged over all joint angles and all degrees of freedom, mean errors were 3.0°, 4.1°, and 4.0°, in walking, running, and cycling, respectively; and range of motion errors were 2.7°, 2.3°, and 4.3°, respectively. Given the magnitude of error traditionally reported in joint angles computed from a marker-based optoelectronic system, Pose2Sim is deemed accurate enough for the analysis of lower-body kinematics in walking, cycling, and running.

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Section: Introductionmentioning

confidence: 99%

Pose2Sim: An End-to-End Workflow for 3D Markerless Sports Kinematics—Part 2: Accuracy

Pagnon

Domalain

Revéret

2022

Sensors

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“…Anchor boxes FCM (6, 8), (11,9), (10,20), (16,13), (27,14), (15,27), (42,25), (29,43), (67, 47), (49,108), (128, 62), (128, 175) K-means (5,6), (12,9), (8,18), (22,13), (13,25), (23,29), (46,27), (31,48), (68, 48), (49, 106), (124, 60), (120,163) K-means++ (5,12), (12,16), (9,35), (15,24), (24,20), (15,49), (29,33), (31,71) On the contrary, if there is a collision, the sampling radius increases to R + k � ε. k is the coefficient used to change the range of the sampling domain, and ε is the step size of the extension. δ is the extended state value of the node.…”

Section: Algorithmmentioning

confidence: 99%

“…In the new century, deep learning has made amazing breakthroughs in the field of detection [18][19][20]. Some excellent object recognition algorithms based on the convolutional neural network have emerged [21][22][23]. Classic algorithms are Region-based Convolutional Neural Network (R-CNN) [24], Fast Regionbased Convolutional Neural Network (Fast R-CNN) [25], Faster Region-based Convolutional Neural Network (Faster R-CNN) [26] etc.…”

Section: Introductionmentioning

confidence: 99%

Autonomous live working robot navigation with real‐time detection and motion planning system on distribution line

et al. 2022

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In this study, an autonomous robot navigation system is designed for live working on distribution line. The developed system features a real‐time detection and motion planning system, incorporating a manipulator capable of grasping power components. In order to accurately identify targets, the authors propose an object detection method based on the Larger Scale ‘You Only Look Once’ Version 4 (LS‐YOLOv4) algorithm for detecting the insulators and drop fuses. The LS‐YOLOv4 extracts features of power components by Convolutional Neural Network (CNN), and then performs feature fusion. Then the authors develop a motion planning method based on the Node Control Optimal Rapidly Exploring Random Trees (NC‐RRT*), which can drive the robot to realise the autonomous robot motion planning and obstacle avoidance. On the grasping function, the authors present a reliable Lightweight‐based Convolutional Neural Network (L‐CNN) grasping point detection method. Finally, the authors evaluate fully autonomous robotic system in both simulated and real‐world experiments. The experimental results demonstrate that the proposed system can effectively identify the target and complete the grasping task in an efficient way. Notably, the proposed motion planning method can take into account both planning efficiency and accuracy to manipulation tasks.

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“…Only the forward movement and rotation of the mobile robot are used to reach the target points. The motion capture system OptiTrack is commonly used to record dynamic motions [ 28 , 29 ]. The motion capture system records the detailed motion of all movements.…”

Section: Introductionmentioning

confidence: 99%

Precision-Driven Multi-Target Path Planning and Fine Position Error Estimation on a Dual-Movement-Mode Mobile Robot Using a Three-Parameter Error Model

Zhao

Misyurin

et al. 2023

Sensors

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The multi-target path planning problem is a universal problem to mobile robots and mobile manipulators. The two movement modes of forward movement and rotation are universally implemented in integrated, commercially accessible mobile platforms used in logistics robots, construction robots, etc. Localization error in multi-target path tracking is one of the crucial measures in mobile robot applications. In this article, a precision-driven multi-target path planning is first proposed. According to the path’s odometry error evaluation function, the precision-optimized path can be discovered. Then, a three-parameter odometry error model is proposed based on the dual movement mode. The error model describes localization errors in terms of the theoretical motion command values issued to the mobile robot, the forward moving distances, and the rotation angles. It appears that the three error parameters follow the normal distribution. The error model is finally validated using a mobile robot prototype. The error parameters can be identified by analyzing the actual moving trajectory of arbitrary movements. The experimental localization error is compared to the simulated localization error in order to validate the proposed error model and the precision-driven path planning method. The OptiTrack motion capture device was used to capture the prototype mobile robot’s pose and position data.

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Obtaining lower-body Euler angle time series in an accurate way using depth camera relying on Optimized Kinect CNN

Cited by 13 publications

References 25 publications

Pose2Sim: An End-to-End Workflow for 3D Markerless Sports Kinematics—Part 2: Accuracy

Pose2Sim: An End-to-End Workflow for 3D Markerless Sports Kinematics—Part 2: Accuracy

Autonomous live working robot navigation with real‐time detection and motion planning system on distribution line

Precision-Driven Multi-Target Path Planning and Fine Position Error Estimation on a Dual-Movement-Mode Mobile Robot Using a Three-Parameter Error Model

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