Top > JRM > Human-Scale Motion Capture with an Accelerometer-Based Gaming ...

single-rb.php

JRM Vol.25 No.3 pp. 458-465
doi: 10.20965/jrm.2013.p0458
(2013)

Paper:

Views over last 60 days: 758

Human-Scale Motion Capture with an Accelerometer-Based Gaming Controller

Sagar N. Purkayastha, Michael D. Byrne, and Marcia K. O’Malley

Rice University, 6100 Main Street, Houston, TX 77005, USA

Received:
November 14, 2012
Accepted:
March 6, 2013
Published:
June 20, 2013
Creative Commons License
Keywords:
motion capture, accelerometers, gaming controllers
Abstract
Gaming controllers are attractive devices for research due to their onboard sensing capabilities and low cost. However, a proper quantitative analysis regarding their suitability for motion capture has yet to be fully reported. In this paper, a detailed analysis of the accelerometers of the Nintendo Wiimote is presented. The gravity-compensated acceleration data from the accelerometers of theWiimote were plotted, compared and correlated with computed acceleration data derived from a six-camera motion capture system. The results show high correlation and low mean absolute error between the gravity-compensated data from the accelerometers of the controllers and computed acceleration from position data of the motion capture system. From the results obtained, it can be inferred that the Wiimote is well suited for motion capture applications where post-processing of data is practical.
Cite this article as:
S. Purkayastha, M. Byrne, and M. O’Malley, “Human-Scale Motion Capture with an Accelerometer-Based Gaming Controller,” J. Robot. Mechatron., Vol.25 No.3, pp. 458-465, 2013.
Data files:
References
  1. [1] A. Gams and P. Mudry, “Gaming controllers for research robots: controlling a humanoid robot using a WIIMOTE,” in 17th Int. Electrotechnical and Computer Science Conference (ERK08), Citeseer, 2008.
  2. [2] J. Lee, “Hacking the nintendo wii remote,” Pervasive Computing, IEEE, Vol.7, No.3, pp. 39-45, 2008.
  3. [3] D. Reinkensmeyer, C. Pang, J. Nessler, and C. Painter, “Web-based telerehabilitation for the upper extremity after stroke,” IEEE Trans. on Neural Systems and Rehabilitation Engineering, Vol.10, No.2, pp. 102-108, 2002.
  4. [4] C. Jadhav, P. Nair, and V. Krovi, “Individualized interactive homebased haptic telerehabilitation,” IEEE MULTIMEDIA, pp. 32-39, 2006.
  5. [5] T. Nakaie, T. Koyama, and M. Hirakawa, “Development of a collaborative multimodal system with a shared sound display,” in Ubi-Media Computing, First IEEE Int. Conf., IEEE, pp. 14-19, 2008.
  6. [6] T. Schlömer, B. Poppinga, N. Henze, and S. Boll, “Gesture recognition with aWii controller,” in Proc. of the 2nd Int. Conf. on Tangible and Embedded Interaction, ACM New York, NY, USA, pp. 11-14, 2008.
  7. [7] S. Castellucci and I. MacKenzie, “Unigest: text entry using three degrees of motion,” in CHI ’08 Extended Abstracts on Human Factors in Computing Systems, ACM, pp. 3549-3554, 2008.
  8. [8] L. Gallo, G. De Pietro, and I. Marra, “3D interaction with volumetric medical data: experiencing the Wiimote,” in Proc. of the 1st Int. Conf. on Ambient Media and Systems. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), pp. 1-6, 2008.
  9. [9] D. Bradshaw and K. Ng, “Tracking Conductors Hand Movements Using Multiple Wiimotes,” in Automated solutions for Cross Media Content andMulti-channel Distribution (AXMEDIS ’08) Int. Conf., IEEE, pp. 93-99, 2008.
  10. [10] S. Olufs and M. Vincze, “A simple inexpensive interface for robots using the Nintendo Wii controller,” in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems 2009 (IROS 2009), IEEE, pp. 473-479, 2009.
  11. [11] M. Lapping-Carr, O. C. Jenkins, D. H. Grollman, J. N. Schowertfeger, and T. R. Hinkle, “Wiimote interfaces for lifelong robot learning,” in Proc. of AAAI Symposium on using AI to Motivate Greater Participation in Computer Science, 2008.
  12. [12] R. Leder, G. Azcarate, R. Savage, S. Savage, L. Sucar, D. Reinkensmeyer, C. Toxtli, E. Roth, and A. Molina, “Nintendo Wii remote for computer simulated arm and wrist therapy in stroke survivors with upper extremity hemiparesis,” in Virtual Rehabilitation, IEEE, p. 74, 2008.
  13. [13] P. Wilson, J. Duckworth, N. Mumford, R. Eldridge, M. Guglielmetti, P. Thomas, D. Shum, and H. Rudolph, “A virtual tabletop workspace for the assessment of upper limb function in Traumatic Brain Injury (TBI),” in Virtual Rehabilitation, IEEE, pp. 14-19, 2007.
  14. [14] J. Szurley and C. Druzgalski, “A novel approach in remote monitoring and assessment of patient balance,” in Pan American Health Care Exchanges (PAHCE), IEEE, p. 143, 2009.
  15. [15] C. Ardito, P. Buono, M. Costabile, R. Lanzilotti, and A. Simeone, “Comparing low cost input devices for interacting with 3D Virtual Environments,” in 2nd Conf. on Human System Interactions (HSI ’09), IEEE, pp. 292-297, 2009.
  16. [16] S. Attygalle, M. Duff, T. Rikakis, and J. He, “Low-cost, at-home assessment system withWii Remote based motion capture,” in Virtual Rehabilitation, IEEE, pp. 168-174, 2008.
  17. [17] Z. Lin, Z. Massimiliano, S. Sessa, L. Bartolomeo, H. Ishii, and A. Takanishi, “Development of the Wireless Ultraminiaturized Inertial Measurement UnitWB-4: Preliminary Performance Evaluation,” in Proc. of the 33rd Annual Int. Conf. of the IEEE EMBS, pp. 6927-6930, 2011.
  18. [18] T. Watanabe and H. Saito, “Tests of wireless wearable sensor system in joint angle measurement of lower limbs,” in Proc. of the 33rd Annual Int. Conf. of the IEEE EMBS, pp. 5469-5472, 2011.
  19. [19] S. Bakhshi, M. H. Mahoor, and B. S. Davidson, “Development of a body joint angle measurement system using IMU sensors,” in Proc. of the 33rd Annual Int. Conf. of the IEEE EMBS, pp. 6923-6926, 2011.
  20. [20] M. El-Gohary, L. Holmstrom, J. Huisinga, E. King, J. McNames, and F. Horak, “Upper limb joint angle tracking with inertial sensors,” in Proc. of the 33rd Annual Int. Conf. of the IEEE EMBS, pp. 5629-5632, 2011.
  21. [21] M. Patterson, D. McGrath, and B. Caulfield, “Using a triaxial accelerometer to detect technique breakdown due to fatigue in distance runners: a preliminary perspective,” in Proc. of the 33rd Annual Int. Conf. of the IEEE EMBS, pp. 6511-6514, 2011.
  22. [22] A. K. Bourke, K. O’Donovan, A. Clifford, G. OLaighin, and J. Nelson, “Optimum gravity vector and vertical acceleration estimation using a triaxial accelerometer for falls and normal activities,” in Proc. of the 33rd Annual Int. Conf. of the IEEE EMBS, pp. 7896-7899, 2011.
  23. [23] D. Natapov, S. Castellucci, and I. MacKenzie, “ISO 9241-9 evaluation of video game controllers,” in Proc. of Graphics Interface. Canadian Information Processing Society, pp. 223-230, 2009.
  24. [24] L. Xie, M. Kumar, Y. Cao, D. Gracanin, and F. Quek, “Data-driven motion estimation with low-cost sensors,” in Visual Information Engineering (VIE) 5th Int. Conference, IET, pp. 600-605, 2009.
  25. [25] S. N. Purkayastha, N. Eckenstein, M. D. Byrne, and M. K. O’Malley, “Analysis and comparison of low cost gaming controllers for motion analysis,” in Proc. of the 2010 IEEE/ASME Int. Conf. on Advanced Intelligent Mechatronics, pp. 353-360, 2010.
  26. [26] T. Sheridan and W. Ferrell, “Man-machine systems: Information, control, and decision models of human performance,” 1981.
  27. [27] D. McRuer and H. Jex, “A review of quasi-linear pilot models,” IEEE Trans. on Human Factors in Electronics, No.3, pp. 231-249, 2006.
  28. [28] A. Champy, “Elements of motion: 3d sensors in intuitive game design,” Analog Dialogue, Vol.41, No.2, pp. 11-14, 2007.
  29. [29] J. Lee and I. Ha, “Real-time motion capture for a human body using accelerometers,” Robotica, Vol.19, No.6, pp. 601-610, 2001.
  30. [30] T. Sakaguchi, T. Kanamori, H. Katayose, K. Sato, and S. Inokuchi, “Human motion capture by integrating gyroscopes and accelerometers,” in IEEE/SICE/RSJ Int. Conf. on Multisensor Fusion and Integration for Intelligent Systems, IEEE, pp. 470-475, 1996.
  31. [31] C. Wingrave, B. Williamson, P. Varcholik, J. Rose, A. Miller, E. Charbonneau, J. Bott, and J. LaViola Jr., “The wiimote and beyond: Spatially convenient devices for 3d user interfaces,” Computer Graphics and Applications, IEEE, Vol.30, No.2, pp. 71-85, 2010.
  32. [32] T. Harada, T. Mori, and T. Sato, “Human posture probability density estimation based on actual motion measurement and eigenpostures,” J. of Robotics and Mechatronics, Vol.17, No.6, p. 664, 2005.
  33. [33]
    Supporting Online Materials:[a] “Quanser: QuaRC.”
    http://www.quanser.com/
  34. [34] [b] “Analog Devices.”
    http://www.analog.com/static/imported-files/data sheets/ADXL330.pdf
  35. [35] [c] “Wiimote-WiiBrew.”
    http://wiibrew.org/wiki
  36. [36] [d] “Optitrack motion capture system.”
    http://www.naturalpoint.com/optitrack

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

Copyright© 2013 by Fuji Technology Press Ltd. and Japan Society of Mechanical Engineers. All right reserved.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 22, 2024