scholarly journals Marker-based motion tracking using Microsoft Kinect

2018 ◽  
Vol 51 (22) ◽  
pp. 399-404 ◽  
Author(s):  
Alireza Bilesan ◽  
Mohammadhasan Owlia ◽  
Saeed Behzadipour ◽  
Shuhei Ogawa ◽  
Teppei Tsujita ◽  
...  
Keyword(s):  
Motion Tracking ◽  
Microsoft Kinect

scholarly journals Kinect Fusion improvement using depth camera calibration

2014 ◽  
Vol XL-5 ◽  
pp. 479-485 ◽  
Author(s):  
D. Pagliari ◽  
F. Menna ◽  
R. Roncella ◽  
F. Remondino ◽  
L. Pinto
Keyword(s):  
Video Game ◽  
Motion Tracking ◽  
Microsoft Kinect ◽  
Third Party ◽  
Depth Sensor ◽  
Ir Camera ◽  
3D Data ◽  
Reconstruction Software ◽  
Remote Controller ◽  
Accuracy And Repeatability

Scene's 3D modelling, gesture recognition and motion tracking are fields in rapid and continuous development which have caused growing demand on interactivity in video-game and e-entertainment market. Starting from the idea of creating a sensor that allows users to play without having to hold any remote controller, the Microsoft Kinect device was created. The Kinect has always attract researchers in different fields, from robotics to Computer Vision (CV) and biomedical engineering as well as third-party communities that have released several Software Development Kit (SDK) versions for Kinect in order to use it not only as a game device but as measurement system. Microsoft Kinect Fusion control libraries (firstly released in March 2013) allow using the device as a 3D scanning and produce meshed polygonal of a static scene just moving the Kinect around. A drawback of this sensor is the geometric quality of the delivered data and the low repeatability. For this reason the authors carried out some investigation in order to evaluate the accuracy and repeatability of the depth measured delivered by the Kinect. The paper will present a throughout calibration analysis of the Kinect imaging sensor, with the aim of establishing the accuracy and precision of the delivered information: a straightforward calibration of the depth sensor in presented and then the 3D data are correct accordingly. Integrating the depth correction algorithm and correcting the IR camera interior and exterior orientation parameters, the Fusion Libraries are corrected and a new reconstruction software is created to produce more accurate models.

Feasibility study of using a Microsoft Kinect for virtual coaching of wheelchair transfer techniques

2017 ◽  
Vol 62 (3) ◽  
Author(s):  
Seonhong Hwang ◽  
Chung-Ying Tsai ◽  
Alicia M. Koontz
Keyword(s):  
Motion Tracking ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Transfer Task ◽  
Angle Measurement ◽  
Microsoft Kinect ◽  
Joint Angles ◽  
Wheelchair Users ◽  
Small Standard Error ◽  
Mean Square Errors

AbstractThe purpose of this study was to test the concurrent validity and test-retest reliability of the Kinect skeleton tracking algorithm for measurement of trunk, shoulder, and elbow joint angle measurement during a wheelchair transfer task. Eight wheelchair users were recruited for this study. Joint positions were recorded simultaneously by the Kinect and Vicon motion capture systems while subjects transferred from their wheelchairs to a level bench. Shoulder, elbow, and trunk angles recorded with the Kinect system followed a similar trajectory as the angles recorded with the Vicon system with correlation coefficients that are larger than 0.71 on both sides (leading arm and trailing arm). The root mean square errors (RMSEs) ranged from 5.18 to 22.46 for the shoulder, elbow, and trunk angles. The 95% limits of agreement (LOA) for the discrepancy between the two systems exceeded the clinical significant level of 5°. For the trunk, shoulder, and elbow angles, the Kinect had very good relative reliability for the measurement of sagittal, frontal and horizontal trunk angles, as indicated by the high intraclass correlation coefficient (ICC) values (>0.90). Small standard error of the measure (SEM) values, indicating good absolute reliability, were observed for all joints except for the leading arm’s shoulder joint. Relatively large minimal detectable changes (MDCs) were observed in all joint angles. The Kinect motion tracking has promising performance levels for some upper limb joints. However, more accurate measurement of the joint angles may be required. Therefore, understanding the limitations in precision and accuracy of Kinect is imperative before utilization of Kinect.

scholarly journals Evaluation of real time motion tracking accuracy of customised IMU sensor for application in a mobile badminton virtual reality training system

2018 ◽  
Vol 7 (1) ◽  
Author(s):  
Zahari Taha ◽  
Mohd Yashim Wong ◽  
Hwa Jen Yap ◽  
Amirul Abdullah ◽  
Wee Kian Yeo
Keyword(s):  
Virtual Reality ◽  
Real Time ◽  
Motion Tracking ◽  
Training System ◽  
Microsoft Kinect ◽  
Measurement Unit ◽  
Tracking Accuracy ◽  
Improve Performance ◽  
Motion Data ◽  
Time Motion

Immersion is one of the most important aspects in ensuring the applicability of Virtual Reality systems to training regimes aiming to improve performance. To ensure that this key aspect is met, the registration of motion between the real world and virtual environment must be made as accurate and as low latency as possible. Thus, an in-house developed Inertial Measurement Unit (IMU) system is developed for use in tracking the movement of the player’s racquet. This IMU tracks 6 DOF motion data and transmits it to the mobile training system for processing. Physically, the custom motion is built into the shape of a racquet grip to give a more natural sensation when swinging the racquet. In addition to that, an adaptive filter framework is also established to cope with different racquet movements automatically, enabling real-time 6 DOF tracking by balancing the jitter and latency. Experiments are performed to compare the efficacy of our approach with other conventional tracking methods such as the using Microsoft Kinect. The results obtained demonstrated noticeable accuracy and lower latency when compared with the aforementioned methods.

scholarly journals Towards a Live Feedback Training System: Interchangeability of Orbbec Persee and Microsoft Kinect for Exercise Monitoring

Designs ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 30
Author(s):  
Verena Venek ◽  
Wolfgang Kremser ◽  
Thomas Stöggl
Keyword(s):  
Monitoring System ◽  
Motion Tracking ◽  
Tracking System ◽  
Training System ◽  
Microsoft Kinect ◽  
Motion Sensing ◽  
Feedback Training ◽  
Sensing Applications ◽  
Motion Tracking System ◽  
Training Exercises

Many existing motion sensing applications in research, entertainment and exercise monitoring are based on the Microsoft Kinect and its skeleton tracking functionality. With the Kinect’s development and production halted, researchers and system designers are in need of a suitable replacement. We investigated the interchangeability of the discontinued Kinect v2 and the all-in-one, image-based motion tracking system Orbbec Persee for the use in an exercise monitoring system prototype called ILSE. Nine functional training exercises were performed by six healthy subjects in front of both systems simultaneously. Comparing the systems’ internal tracking states from ’not tracked’ to ‘tracked’ showed that the Persee system is more confident during motion sequences, while the Kinect is more confident for hip and trunk joint positions. Assessing the skeleton tracking robustness, the Persee’s tracking of body segment lengths was more consistent. Furthermore, we used both skeleton datasets as input for the ILSE exercise monitoring including posture recognition and repetition-counting. Persee data from exercises with lateral movement and in uncovered full-body frontal view provided the same results as Kinect data. The Persee further preferred tracking of quasi-static lower limb motions and tight-fitting clothes. With these limitations in mind, we find that the Orbbec Persee is a suitable replacement for the Microsoft Kinect for motion sensing within the ILSE exercise monitoring system.

Kinect-Based Universal Range Sensor for Laboratory Experiments

2013 ◽  
Author(s):  
Mingshao Zhang ◽  
Zhou Zhang ◽  
El-Sayed Aziz ◽  
Sven K. Esche ◽  
Constantin Chassapis
Keyword(s):  
Object Recognition ◽  
Motion Tracking ◽  
Laboratory Experiments ◽  
Low Cost ◽  
Object Motion ◽  
Microsoft Kinect ◽  
Prototype System ◽  
High Quality ◽  
Range Sensor ◽  
Depth Data

The Microsoft Kinect is part of a wave of new sensing technologies. Its RGB-D camera is capable of providing high quality synchronized video of both color and depth data. Compared to traditional 3-D tracking techniques that use two separate RGB cameras’ images to calculate depth data, the Kinect is able to produce more robust and reliable results in object recognition and motion tracking. Also, due to its low cost, the Kinect provides more opportunities for use in many areas compared to traditional more expensive 3-D scanners. In order to use the Kinect as a range sensor, algorithms must be designed to first recognize objects of interest and then track their motions. Although a large number of algorithms for both 2-D and 3-D object detection have been published, reliable and efficient algorithms for 3-D object motion tracking are rare, especially using Kinect as a range sensor. In this paper, algorithms for object recognition and tracking that can make use of both RGB and depth data in different scenarios are introduced. Subsequently, efficient methods for scene segmentation including background and noise filtering are discussed. Taking advantage of those two kinds of methods, a prototype system that is capable of working efficiently and stably in various applications related to educational laboratories is presented.

scholarly journals The Reliability of the Microsoft Kinect and Ambulatory Sensor-Based Motion Tracking Devices to Measure Shoulder Range-of-Motion: A Systematic Review and Meta-Analysis

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8186
Author(s):  
Peter Beshara ◽  
David B. Anderson ◽  
Matthew Pelletier ◽  
William R. Walsh
Keyword(s):  
Range Of Motion ◽  
Motion Tracking ◽  
Human Motion ◽  
Microsoft Kinect ◽  
Smartphone Applications ◽  
Health Measurement ◽  
Rater Reliability ◽  
Shoulder Range Of Motion ◽  
Cosmin Checklist ◽  
Tracking Devices

Advancements in motion sensing technology can potentially allow clinicians to make more accurate range-of-motion (ROM) measurements and informed decisions regarding patient management. The aim of this study was to systematically review and appraise the literature on the reliability of the Kinect, inertial sensors, smartphone applications and digital inclinometers/goniometers to measure shoulder ROM. Eleven databases were screened (MEDLINE, EMBASE, EMCARE, CINAHL, SPORTSDiscus, Compendex, IEEE Xplore, Web of Science, Proquest Science and Technology, Scopus, and PubMed). The methodological quality of the studies was assessed using the consensus-based standards for the selection of health Measurement Instruments (COSMIN) checklist. Reliability assessment used intra-class correlation coefficients (ICCs) and the criteria from Swinkels et al. (2005). Thirty-two studies were included. A total of 24 studies scored “adequate” and 2 scored “very good” for the reliability standards. Only one study scored “very good” and just over half of the studies (18/32) scored “adequate” for the measurement error standards. Good intra-rater reliability (ICC > 0.85) and inter-rater reliability (ICC > 0.80) was demonstrated with the Kinect, smartphone applications and digital inclinometers. Overall, the Kinect and ambulatory sensor-based human motion tracking devices demonstrate moderate–good levels of intra- and inter-rater reliability to measure shoulder ROM. Future reliability studies should focus on improving study design with larger sample sizes and recommended time intervals between repeated measurements.

scholarly journals Inverse Kinematics and Extended Kalman Filter Based Motion Tracking of Human Limb

2014 ◽  
Author(s):  
Benson Isaac ◽  
Manish Kumar ◽  
Samuel H. Huang ◽  
Daniel Humpert
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Motion Tracking ◽  
Inertial Sensors ◽  
Rigid Bodies ◽  
Microsoft Kinect ◽  
Extended Kalman Filtering ◽  
Human Skeleton ◽  
Flow Equations ◽  
Human Limb

This paper addresses the problem of motion tracking of human skeleton system using non-invasive vision based sensors. The proposed approach combines synergistic paradigms of image processing, kinematics of rigid bodies and Extended Kalman Filtering scheme to estimate the motion of a human limb system. This approach solely depends on the measurement obtained from the vision sensors without involving any wearable or inertial sensors to measure the motion parameters. In this paper we propose fusion of two filtering schemes — the optical flow equations applied to raw images obtained from the Microsoft Kinect and extended Kalman filter for human skeleton considered as a system of kinematic linkages. The strategy proposed in this paper yields near optimal results as is demonstrated with the help of experiments performed using the Microsoft Kinect sensor and compared using accurate tracks obtained from 24-Camera Optitrack motion capture system.

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