Influence of a Marker-Based Motion Capture System on the Performance of Microsoft Kinect v2 Skeleton Algorithm

Microsoft Kinect sensors are being widely used as low-cost marker-less motion capture systems in various kinematic studies. Previous studies investigated the reliability and validity of Microsoft Kinect sensors by employing marker-based motion capture systems. Both systems employ infrared emitters and detectors to track human posture and physical activities. This paper hypothesizes that the motion capture systems may interfere with Microsoft Kinect one sensor and may influence the sensor’s performance in tracking the skeleton. Hence, this paper investigated the impact of a motion capture system on the Microsoft Kinect v2 skeleton algorithm using a mannequin in the presence of eight Qualisys Oqus 300/310 cameras and retroreflective markers. It was found that the motion capture system introduced a destructive impact on the Microsoft Kinect v2 skeleton tracking algorithm. In addition, it was observed that retroreflective markers placed near the joints caused the Microsoft Kinect v2 to give an incorrect reading of estimate the joint position. The motion capture cameras thus caused a time-varying distortion of the Microsoft Kinect estimate of the joint position. It is believed that the inference can be reduced by decreasing the number of markers and avoiding facing the motion capture cameras in sight of Microsoft Kinect v2.

[1]  Moataz Eltoukhy,et al.  Validity of the Microsoft Kinect™ in assessing spatiotemporal and lower extremity kinematics during stair ascent and descent in healthy young individuals. , 2018, Medical engineering & physics.

[2]  Kelly J. Bower,et al.  Clinical feasibility of interactive motion-controlled games for stroke rehabilitation , 2015, Journal of NeuroEngineering and Rehabilitation.

[3]  Marjorie Skubic,et al.  Development and preliminary validation of an interactive remote physical therapy system , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[4]  George Azzari,et al.  Rapid Characterization of Vegetation Structure with a Microsoft Kinect Sensor , 2013, Sensors.

[5]  J P Cobb,et al.  Validity and sensitivity of the longitudinal asymmetry index to detect gait asymmetry using Microsoft Kinect data. , 2017, Gait & posture.

[6]  John Sell,et al.  The Xbox One System on a Chip and Kinect Sensor , 2014, IEEE Micro.

[7]  John Hansen,et al.  Investigating the impact of a motion capture system on Microsoft Kinect v2 recordings: A caution for using the technologies together , 2018, PloS one.

[8]  Andrew W. Fitzgibbon,et al.  Efficient regression of general-activity human poses from depth images , 2011, 2011 International Conference on Computer Vision.

[9]  Martin Kampel,et al.  Performance evaluation of joint angles obtained by the Kinect v2 , 2015 .

[10]  Marjorie Skubic,et al.  Validation of a Kinect V2 based rehabilitation game , 2018, PloS one.

[11]  Maysam Ghovanloo,et al.  A Smart Wirelessly Powered Homecage for Long-Term High-Throughput Behavioral Experiments , 2015, IEEE Sensors Journal.

[12]  Kirk Woolford,et al.  Defining accuracy in the use of Kinect v2 for exercise monitoring , 2015, MOCO.

[13]  Ryo Kurazume,et al.  Categorization of Indoor Places Using the Kinect Sensor , 2012, Sensors.

[14]  BlakeAndrew,et al.  Real-time human pose recognition in parts from single depth images , 2013 .

[15]  Bernd Heinlein,et al.  Accuracy of KinectOne to quantify kinematics of the upper body. , 2016, Gait & posture.

[16]  John Darby,et al.  An evaluation of 3D head pose estimation using the Microsoft Kinect v2. , 2016, Gait & posture.

[17]  Yao-Jen Chang,et al.  A Kinect-based system for physical rehabilitation: a pilot study for young adults with motor disabilities. , 2011, Research in developmental disabilities.

[18]  Marjorie Skubic,et al.  Evaluation of the microsoft kinect skeletal versus depth data analysis for timed-up and go and figure of 8 walk tests , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[19]  Guanghua Xu,et al.  Pre-Impact Fall Detection Based on a Modified Zero Moment Point Criterion Using Data From Kinect Sensors , 2018, IEEE Sensors Journal.

[20]  Philippe Fraisse,et al.  Adaptive Interface for Personalized Center of Mass Self-Identification in Home Rehabilitation , 2015, IEEE Sensors Journal.

[21]  Jeffrey M. Hausdorff,et al.  Gait tracking for virtual reality clinical applications: A low cost solution , 2013 .

[22]  Nathan Intrator,et al.  A Real-Time Kinect Signature-Based Patient Home Monitoring System , 2016, Sensors.

[23]  Keum-Bae Cho,et al.  Intelligent Lead: A Novel HRI Sensor for Guide Robots , 2012, Sensors.

[24]  M. R. Kharazi,et al.  Validity of microsoft kinectTM for measuring gait parameters , 2015, 2015 22nd Iranian Conference on Biomedical Engineering (ICBME).

[25]  Julius Verrel,et al.  Accuracy and Reliability of the Kinect Version 2 for Clinical Measurement of Motor Function , 2016, PloS one.

[26]  Thomas D. Collins,et al.  A six degrees-of-freedom marker set for gait analysis: repeatability and comparison with a modified Helen Hayes set. , 2009, Gait & posture.

[27]  Andrew W. Fitzgibbon,et al.  Real-time human pose recognition in parts from single depth images , 2011, CVPR 2011.

[28]  Carole Tucker,et al.  Performance analysis of a generalized motion capture system using microsoft kinect 2.0 , 2017, Biomed. Signal Process. Control..

[29]  Ross A Clark,et al.  Reliability and concurrent validity of the Microsoft Xbox One Kinect for assessment of standing balance and postural control. , 2015, Gait & posture.

[30]  Andrew D. Payne,et al.  A 0.13 μm CMOS System-on-Chip for a 512 × 424 Time-of-Flight Image Sensor With Multi-Frequency Photo-Demodulation up to 130 MHz and 2 GS/s ADC , 2015, IEEE Journal of Solid-State Circuits.

[31]  Ross A Clark,et al.  Gait assessment using the Microsoft Xbox One Kinect: Concurrent validity and inter-day reliability of spatiotemporal and kinematic variables. , 2015, Journal of biomechanics.

[32]  Andreas Kolb,et al.  Kinect range sensing: Structured-light versus Time-of-Flight Kinect , 2015, Comput. Vis. Image Underst..

[33]  Fillia Makedon,et al.  Quantitative analysis of the human upper-limp kinematic model for robot-based rehabilitation applications , 2016, 2016 IEEE International Conference on Automation Science and Engineering (CASE).

[34]  Iyad Obeid,et al.  The Automated Assessment of Postural Stability: Balance Detection Algorithm , 2017, Annals of Biomedical Engineering.

[35]  Mark McGroarty,et al.  Bone length calibration can significantly improve the measurement accuracy of knee flexion angle when using a marker-less system to capture the motion of countermovement jump , 2016, 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI).

[36]  Baining Guo,et al.  Exemplar-Based Human Action Pose Correction , 2014, IEEE Transactions on Cybernetics.

[37]  S. Jan.  Color Atlas of Skeletal landmark definitions. Guidelines for reproducible manual and virtual palpations. , 2007 .

[38]  Hee Chan Kim,et al.  Measurement of Shoulder Range of Motion in Patients with Adhesive Capsulitis Using a Kinect , 2015, PloS one.

[39]  M P Kadaba,et al.  Measurement of lower extremity kinematics during level walking , 1990, Journal of orthopaedic research : official publication of the Orthopaedic Research Society.

[40]  Winfried Ilg,et al.  Validation of enhanced kinect sensor based motion capturing for gait assessment , 2017, PloS one.

[41]  C. Cobelli,et al.  Comparison of Markerless and Marker-Based Motion Capture Technologies through Simultaneous Data Collection during Gait: Proof of Concept , 2014, PloS one.

[42]  Mansib Rahman,et al.  Beginning Microsoft Kinect for Windows SDK 2.0 , 2017, Apress.

[43]  Moataz Eltoukhy,et al.  Improved kinect-based spatiotemporal and kinematic treadmill gait assessment. , 2017, Gait & posture.