Development of a Self-Calibrated Motion Capture System by Nonlinear Trilateration of Multiple Kinects v2

Yang, Bowen; Dong, Haiwei; Saddik, Abdulmotaleb El

doi:10.1109/jsen.2017.2671420

Cited by 17 publications

(12 citation statements)

References 37 publications

(41 reference statements)

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“…For 3D data registration, there are also specially designed calibration-assisting devices other than the checker board. For example, in [20], a wand was used to register depth data from multiple RGBD cameras as a calibration-assisting device. Salau et al [21] used the calibration device with three sticks perpendicular to each other, which were fixed at the center and six pieces of wood ball were placed at the end.…”

Section: Related Workmentioning

confidence: 99%

Omnidirectional 3D Point Clouds Using Dual Kinect Sensors

2019

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This paper proposes a registration method for two sets of point clouds obtained from dual Kinect V2 sensors, which are facing each other to capture omnidirectional 3D data of the objects located in between the two sensors. Our approach aims at achieving a handy registration without the calibration-assisting devices such as the checker board. Therefore, it is suitable in portable camera setting environments with frequent relocations. The basic idea of the proposed registration method is to exploit the skeleton information of the human body provided by the two Kinect V2 sensors. That is, a set of correspondence pairs in skeleton joints of human body detected by Kinect V2 sensors is used to determine the calibration matrices, then Iterative Closest Point (ICP) algorithm is adopted for finely tuning the calibration parameters. The performance of the proposed method is evaluated by constructing 3D point clouds for human bodies and by making geometric measurements for cylindrical testing objects.

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Section: Related Workmentioning

confidence: 99%

Omnidirectional 3D Point Clouds Using Dual Kinect Sensors

2019

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“…Using a multi-Kinect system is one of the solutions to improve the kinematic accuracy in dynamic motion analysis. Yang et al [29] developed a multiple-Kinect system by combining results from multiple sensors with a nonlinear least square method. The system showed better performance in dealing with body occlusion and had better average accuracy of tracking human joints.…”

Section: Introductionmentioning

confidence: 99%

Placement Recommendations for Single Kinect-Based Motion Capture System in Unilateral Dynamic Motion Analysis

Cai

Liu

2021

Healthcare

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Low-cost, portable, and easy-to-use Kinect-based systems achieved great popularity in out-of-the-lab motion analysis. The placement of a Kinect sensor significantly influences the accuracy in measuring kinematic parameters for dynamics tasks. We conducted an experiment to investigate the impact of sensor placement on the accuracy of upper limb kinematics during a typical upper limb functional task, the drinking task. Using a 3D motion capture system as the golden standard, we tested twenty-one Kinect positions with three different distances and seven orientations. Upper limb joint angles, including shoulder flexion/extension, shoulder adduction/abduction, shoulder internal/external rotation, and elbow flexion/extension angles, are calculated via our developed Kinect kinematic model and the UWA kinematic model for both the Kinect-based system and the 3D motion capture system. We extracted the angles at the point of the target achieved (PTA). The mean-absolute-error (MEA) with the standard represents the Kinect-based system’s performance. We conducted a two-way repeated measure ANOVA to explore the impacts of distance and orientation on the MEAs for all upper limb angles. There is a significant main effect for orientation. The main effects for distance and the interaction effects do not reach statistical significance. The post hoc test using LSD test for orientation shows that the effect of orientation is joint-dependent and plane-dependent. For a complex task (e.g., drinking), which involves body occlusions, placing a Kinect sensor right in front of a subject is not a good choice. We suggest that place a Kinect sensor at the contralateral side of a subject with the orientation around 30∘ to 45∘ for upper limb functional tasks. For all kinds of dynamic tasks, we put forward the following recommendations for the placement of a Kinect sensor. First, set an optimal sensor position for capture, making sure that all investigated joints are visible during the whole task. Second, sensor placement should avoid body occlusion at the maximum extension. Third, if an optimal location cannot be achieved in an out-of-the-lab environment, researchers could put the Kinect sensor at an optimal orientation by trading off the factor of distance. Last, for those need to assess functions of both limbs, the users can relocate the sensor and re-evaluate the functions of the other side once they finish evaluating functions of one side of a subject.

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“…In the convolution process, the feature map is connected with the data in several consecutive frames [16]. The literature uses single frame data and optical flow data to capture motion information [17, 18]. In this paper, a 3D CNN is used in a fixed‐length video block.…”

Section: Introductionmentioning

confidence: 99%