Motion Capturing with Inertial Measurement Units and Kinect - Tracking of Limb Movement using Optical and Orientation Information

Christoph Kalkbrenner, Steffen Hacker, Maria-Elena Algorri, Ronald Blechschmidt-Trapp

2014

Abstract

This paper presents an approach for the tracking of limb movements using orientation information acquired from Inertial Measurement Units (IMUs) and optical information from a Kinect sensor. A new algorithm that uses a Kalman filter to fuse the Kinect and IMU data is presented. By fusing optical and orientation information we are able to track the movement of limb joints precisely, and almost drift-free. First, the IMU data is processed using the gradient descent algorithm proposed in (Madgwick et al., 2011) which calculates the orientation information of the IMU using acceleration and velocity data. Measurements made with IMUs tend to drift over time, so in a second stage we compensate for the drift using absolute position information obtained from a Microsoft Kinect sensor. The fusion of sensor data also allows to compensate for faulty or missing measurements. We have carried out some initial experiments on arm tracking. The first results show that our technique for data fusion has the potential to be used to record common medical exercises for clinical movement analysis.

References

  1. Bo, A., Hayashibe, M., and Poignet, P. (2011). Joint angle estimation in rehabilitation with inertial sensors and its integration with kinect. In Engineering in Medicine and Biology Society,EMBC, 2011 Annual International Conference of the IEEE, pages 3479- 3483.
  2. Claasen, G., Martin, P., and Picard, F. (2011). Highbandwidth low-latency tracking using optical and inertial sensors. In Automation, Robotics and Applications (ICARA), 2011 5th International Conference on, pages 366-371.
  3. Cloete, T. and Scheffer, C. (2008). Benchmarking of a fullbody inertial motion capture system for clinical gait analysis. In Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE, pages 4579-4582.
  4. Cordella, F., Di Corato, F., Zollo, L., Siciliano, B., and Van Der Smagt, P. (2012). Patient performance evaluation using kinect and monte carlo-based finger tracking. In Biomedical Robotics and Biomechatronics (BioRob), 2012 4th IEEE RAS EMBS International Conference on, pages 1967-1972.
  5. El-laithy, R., Huang, J., and Yeh, M. (2012). Study on the use of microsoft kinect for robotics applications. In Position Location and Navigation Symposium (PLANS), 2012 IEEE/ION, pages 1280-1288.
  6. Fischer, C., Talkad Sukumar, P., and Hazas, M. (2012). Tutorial: implementation of a pedestrian tracker using foot-mounted inertial sensors. Pervasive Computing, IEEE, PP(99):1-1.
  7. Glaz, Y. L. (2011). Smartphonebasierte bewegungsanalyse zur therapiekontrolle bei morbus parkinson. Master's thesis, University of Applied Sciences Hochschule Ulm.
  8. Jekeli, C. (2001). Inertial Navigation Systems With Geodetic Applications. Walter de Gruyter.
  9. Jung, Y., Kang, D., and Kim, J. (2010). Upper body motion tracking with inertial sensors. In Robotics and Biomimetics (ROBIO), 2010 IEEE International Conference on, pages 1746-1751.
  10. Koehler, B.-U. (2005). Konzepte der statistischen Signalverarbeitung. Springer, Berlin [u.a.].
  11. Liguo, H., Yanfeng, Z., and Lingyun, Z. (2011). Body motion recognition based on acceleration sensor. In Electronic Measurement Instruments (ICEMI), 2011 10th International Conference on, volume 1, pages 142- 145.
  12. Madgwick, S., Harrison, A. J. L., and Vaidyanathan, R. (2011). Estimation of imu and marg orientation using a gradient descent algorithm. In Rehabilitation Robotics (ICORR), 2011 IEEE International Conference on, pages 1-7.
  13. Maybeck, P. (1982). Stochastic Models, Estimation, and Control. Mathematics in science and engineering. Academic Press.
  14. Nischwitz, A., Fischer, M., and Haberäcker, P. (2007). Computergrafik und Bildverarbeitung. Vieweg, Wiesbaden, 2 edition.
  15. Obdrzalek, S., Kurillo, G., Ofli, F., Bajcsy, R., Seto, E., Jimison, H., and Pavel, M. (2012). Accuracy and robustness of kinect pose estimation in the context of coaching of elderly population. In Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE, pages 1188- 1193.
  16. Patsadu, O., Nukoolkit, C., and Watanapa, B. (2012). Human gesture recognition using kinect camera. In Computer Science and Software Engineering (JCSSE), 2012 International Joint Conference on, pages 28-32.
  17. Rodriguez-Angeles, A., Morales-Diaz, A., Bernabe, J.- C., and Arechavaleta, G. (2010). An online inertial sensor-guided motion control for tracking human arm movements by robots. In Biomedical Robotics and Biomechatronics (BioRob), 2010 3rd IEEE RAS and EMBS International Conference on, pages 319-324.
  18. Taffoni, F., Piervirgili, G., Formica, D., and Guglielmelli, E. (2011). An alignment procedure for ambulatory measurements of lower limb kinematic using magnetoinertial sensors. In Engineering in Medicine and Biology Society,EMBC, 2011 Annual International Conference of the IEEE, pages 1197-1200.
  19. Walter, B. (2012). Smartphonebasierte bewegungsanalyse zur therapiekontrolle bei morbus parkinson. Master's thesis, University of Applied Sciences Hochschule Ulm.
  20. Wendel, J. (2007). Integrierte Navigationssysteme : Sensordatenfusion, GPS und Inertiale Navigation. Oldenbourg, Muenchen [u.a.].
  21. Xiao, Z., Mengyin, F., Yi, Y., and Ningyi, L. (2012). 3d human postures recognition using kinect. In Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2012 4th International Conference on, volume 1, pages 344-347.
  22. Zeng, M., Liu, Z., Meng, Q., Bai, Z., and Jia, H. (2012). Motion capture and reconstruction based on depth information using kinect. In Image and Signal Processing (CISP), 2012 5th International Congress on, pages 1381-1385.
  23. Zhang, Z., Fang, Q., and Ferry, F. (2011). Upper limb motion capturing and classification for unsupervised stroke rehabilitation. In IECON 2011 - 37th Annual Conference on IEEE Industrial Electronics Society, pages 3832-3836.
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Paper Citation


in Harvard Style

Kalkbrenner C., Hacker S., Algorri M. and Blechschmidt-Trapp R. (2014). Motion Capturing with Inertial Measurement Units and Kinect - Tracking of Limb Movement using Optical and Orientation Information . In Proceedings of the International Conference on Biomedical Electronics and Devices - Volume 1: BIODEVICES, (BIOSTEC 2014) ISBN 978-989-758-013-0, pages 120-126. DOI: 10.5220/0004787601200126


in Bibtex Style

@conference{biodevices14,
author={Christoph Kalkbrenner and Steffen Hacker and Maria-Elena Algorri and Ronald Blechschmidt-Trapp},
title={Motion Capturing with Inertial Measurement Units and Kinect - Tracking of Limb Movement using Optical and Orientation Information},
booktitle={Proceedings of the International Conference on Biomedical Electronics and Devices - Volume 1: BIODEVICES, (BIOSTEC 2014)},
year={2014},
pages={120-126},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004787601200126},
isbn={978-989-758-013-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Biomedical Electronics and Devices - Volume 1: BIODEVICES, (BIOSTEC 2014)
TI - Motion Capturing with Inertial Measurement Units and Kinect - Tracking of Limb Movement using Optical and Orientation Information
SN - 978-989-758-013-0
AU - Kalkbrenner C.
AU - Hacker S.
AU - Algorri M.
AU - Blechschmidt-Trapp R.
PY - 2014
SP - 120
EP - 126
DO - 10.5220/0004787601200126