Investigating the impact of a motion capture system on Microsoft Kinect v2 recordings: A caution for using the technologies together

Naeemabadi, Mohammadreza; Dinesen, Birthe; Andersen, Ole Kæseler; Hansen, John H. L.

doi:10.1371/journal.pone.0204052

Cited by 26 publications

(13 citation statements)

References 38 publications

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“…As mentioned by Naeemabadi et al [ 28 ], retro-reflective markers, like those of the Vicon system, appear as noise in the IR images, so their 3D position is unknown to the Kinect sensors. To determine the 3D position of the Vicon markers, nevertheless, the intermediate intersection points on the chessboard were identified in the 2D color images using the OpenCV library, followed by the coordinate mapper functions provided by the Kinect SDKs to obtain the actual 3D positions from the depth camera space, denoted as points

in the point set P .…”

Section: Materials and Methodsmentioning

confidence: 99%

Evaluation of the Pose Tracking Performance of the Azure Kinect and Kinect v2 for Gait Analysis in Comparison with a Gold Standard: A Pilot Study

Albert

Owolabi

Gebel

et al. 2020

Sensors

197

120

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Gait analysis is an important tool for the early detection of neurological diseases and for the assessment of risk of falling in elderly people. The availability of low-cost camera hardware on the market today and recent advances in Machine Learning enable a wide range of clinical and health-related applications, such as patient monitoring or exercise recognition at home. In this study, we evaluated the motion tracking performance of the latest generation of the Microsoft Kinect camera, Azure Kinect, compared to its predecessor Kinect v2 in terms of treadmill walking using a gold standard Vicon multi-camera motion capturing system and the 39 marker Plug-in Gait model. Five young and healthy subjects walked on a treadmill at three different velocities while data were recorded simultaneously with all three camera systems. An easy-to-administer camera calibration method developed here was used to spatially align the 3D skeleton data from both Kinect cameras and the Vicon system. With this calibration, the spatial agreement of joint positions between the two Kinect cameras and the reference system was evaluated. In addition, we compared the accuracy of certain spatio-temporal gait parameters, i.e., step length, step time, step width, and stride time calculated from the Kinect data, with the gold standard system. Our results showed that the improved hardware and the motion tracking algorithm of the Azure Kinect camera led to a significantly higher accuracy of the spatial gait parameters than the predecessor Kinect v2, while no significant differences were found between the temporal parameters. Furthermore, we explain in detail how this experimental setup could be used to continuously monitor the progress during gait rehabilitation in older people.

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in the point set P .…”

Section: Materials and Methodsmentioning

confidence: 99%

Evaluation of the Pose Tracking Performance of the Azure Kinect and Kinect v2 for Gait Analysis in Comparison with a Gold Standard: A Pilot Study

Albert

Owolabi

Gebel

et al. 2020

Sensors

197

120

View full text Add to dashboard Cite

show abstract

“…In our preliminary recordings and a previous study (Naeemabadi et al, 2018), it was observed that infrared markers placed on the ankle and knee for optical motion capture could hinder the recording of depth images by the Azure Kinect sensor. Instead of placing a marker on the knee and ankle, we created a rigid body model for the shank segment before the recording session.…”

Section: Methodsmentioning

confidence: 75%

Use of the Azure Kinect to measure foot clearance during obstacle crossing

Yoshimoto

Shinya

2021

Preprint

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Obstacle crossing is a typical adaptive locomotion known to be related to the risk of falls. Previous conventional studies have used elaborate and costly optical motion capture systems, which not only represent a considerable expense but also require participants to visit a laboratory. To overcome these shortcomings, we aimed to develop a practical and inexpensive solution for measuring obstacle-crossing behavior by using the Microsoft Azure Kinect, one of the most promising markerless motion capture systems. We validated the Azure Kinect as a tool to measure foot clearance and compared its performance to that of an optical motion capture system (Qualisys). We also determined the effect of the Kinect sensor placement on measurement performance. Sixteen healthy young men crossed obstacles of different heights (50, 150, and 250 mm). Kinect sensors were placed in front of and beside the obstacle as well as diagonally between those positions. As indices of measurement quality, we counted the number of measurement failures and calculated the systematic and random errors between the foot clearance measured by the Kinect and Qualisys. We also calculated the Pearson correlation coefficients between the Kinect and Qualisys measurements. The number of measurement failures and the systematic and random error were minimized when the Kinect was placed diagonally in front of the obstacle on the same side as the trail limb. The high correlation coefficient (r > 0.890) observed between the Kinect and Qualisys measurements suggests that the Azure Kinect has excellent potential for measuring foot clearance during obstacle-crossing tasks.

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“…In addition, the synchronization between the sensors constrains the Qualisys system to work at 30 Hz instead of 300 Hz. Naembadi et al studied this question with Kinect 2, but Kinect 3 seems to be much more sensitive to interference [21]. Another point to highlight is the computational cost for running Kinect 3.…”

Section: Discussion and Limitsmentioning

confidence: 99%

Markerless 3D Human Pose Tracking in the Wild with Fusion of Multiple Depth Cameras: Comparative Experimental Study with Kinect 2 and 3

Colombel

Daney

Bonnet

et al. 2020

Smart Innovation, Systems and Technologies

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Human-robot interaction requires a robust estimate of human motion in real-time. This work presents a fusion algorithm for joint center positions tracking from multiple depth cameras to improve human motion analysis accuracy. The main contribution is the proposed algorithm based on body tracking measurements fusion with an extended Kalman filter and anthropomorphic constraints, independent of sensors. As an illustration of the use of this algorithm, this paper presents the direct comparison of joint center positions estimated with a reference stereophotogrammetric system and the ones estimated with the new Kinect 3 (Azure Kinect) sensor and its older version the Kinect 2 (Kinect for Windows). The experiment was made in two parts, one for each model of Kinect, by comparing raw and merging body tracking data of two sided Kinect with the proposed algorithm. The proposed approach improves body tracker data for Kinect 3 which has not the same characteristics as Kinect 2. This study shows also the importance of defining good heuristics to merge data depending on how the body tracking works. Thus, with proper heuristics, the joint center position estimates are improved by at least 14.6 %. Finally, we propose an additional comparison between Kinect 2 and Kinect 3 exhibiting the pros and cons of the two sensors.

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Investigating the impact of a motion capture system on Microsoft Kinect v2 recordings: A caution for using the technologies together

Cited by 26 publications

References 38 publications

Evaluation of the Pose Tracking Performance of the Azure Kinect and Kinect v2 for Gait Analysis in Comparison with a Gold Standard: A Pilot Study

Evaluation of the Pose Tracking Performance of the Azure Kinect and Kinect v2 for Gait Analysis in Comparison with a Gold Standard: A Pilot Study

Use of the Azure Kinect to measure foot clearance during obstacle crossing

Markerless 3D Human Pose Tracking in the Wild with Fusion of Multiple Depth Cameras: Comparative Experimental Study with Kinect 2 and 3

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