Advertisement

Journal of Intelligent & Robotic Systems

, Volume 71, Issue 2, pp 143–157 | Cite as

A Methodology for the Performance Evaluation of Inertial Measurement Units

  • Salvatore Sessa
  • Massimiliano Zecca
  • Zhuohua Lin
  • Luca Bartolomeo
  • Hiroyuki Ishii
  • Atsuo Takanishi
Article

Abstract

This paper presents a methodology for a reliable comparison among Inertial Measurement Units or attitude estimation devices in a Vicon environment. The misalignment among the reference systems and the lack of synchronization among the devices are the main problems for the correct performance evaluation using Vicon as reference measurement system. We propose a genetic algorithm coupled with Dynamic Time Warping (DTW) to solve these issues. To validate the efficacy of the methodology, a performance comparison is implemented between the WB-3 ultra-miniaturized Inertial Measurement Unit (IMU), developed by our group, with the commercial IMU InertiaCube3™ by InterSense.

Keywords

Performance evaluation Inertial Measurement Units Motion capture systems Motion sensors calibration 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Masuda, Y., Sekimoto, M., Nambu, M., Higashi, Y., Fujimoto, T., Chihara, K., Tamura, Y.: An unconstrained monitoring system for home rehabilitation. IEEE Eng. Med. Biol. Mag. 24, 43–47 (2005)CrossRefGoogle Scholar
  2. 2.
    Jarochowski, B.P., Shin, S.-J., Ryu, D.-H., Kim, H.-J.: Ubiquitous rehabilitation center: an implementation of a wireless sensor network based rehabilitation management system. In: International Conference on Convergence Information Technology, pp. 2349–2358 (2007)Google Scholar
  3. 3.
    Bhardwaj, S., Lee, D.-S., Mukhopadhyay, S.C., Chung, W.-Y.: Ubiquitous healthcare data analysis and monitoring using multiple wireless sensors for Elderly Person. Sensor & Transducer Journal 90, 87–99 (2008)Google Scholar
  4. 4.
    Lee, H., Kim, Y.-T., Jung, J.-W., Park, K.-H., Kim, D.-J., Bien, Z.Z.: A 24-hour health monitoring system in a smart house. Gerontechnology 7, 22–35 (2008)Google Scholar
  5. 5.
    Nintendo, “Wii” [Online]. Available: http://wii.com
  6. 6.
    Microsoft, “Kinect - Xbox.com” [Online]. Available: http://www.xbox.com/en-US/kinect
  7. 7.
    Antonio Benitez, R., Guillermo de los Santos, T., Daniel Vallejo, R.: Forward kinematics for virtual agents. Eng. Lett. 15(2), 225–233 (2007)Google Scholar
  8. 8.
    Chen, Y., Lee, J., Parent, R., Machiraju, R.: Markerless monocular motion capture using image features and physical constraints. In: Computer Graphics International, pp. 36–43 (2005)Google Scholar
  9. 9.
    Zecca, M., Cavallo, F., Saito, M., Endo, N., Mizoguchi, Y., Sinigaglia, S., Itoh, K., Takanobu, H., Megali, G., Tonet, O., Dario, P., Pietrabissa, A., Takanishi, A.: Analysis of the surgeon’s performance during laparoscopy by using the bioinstrumentation system WB-1R—towards the development of a global performance index. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), vol. Analysis and Control of Medical Robots I—TuD7, pp. 1272–1277 (2007)Google Scholar
  10. 10.
    Aurora Electromagnetic Measurement System [Online]. Available: http://www.ndigital.com/medical/.php
  11. 11.
    Roetenberg, D., Slycke, P., Ventevogel, A., Veltink, P.H.: A portable magnetic position and orientation tracker. Sensor Actuator Phys. 135, 426–432 (2007)CrossRefGoogle Scholar
  12. 12.
    Vlasic, D., Adelsberger, R., Vannucci, G., Barnwell, J., Gross, M., Matusik, W., Popović, J.: Practical motion capture in everyday surroundings. ACM Trans. Graph. 26(3), 35 (2007)CrossRefGoogle Scholar
  13. 13.
    Venture, G., Yamane, K., Nakamura, Y., Yamamoto, T.: Identification of human limb viscoelasticity using robotics methods to support the diagnosis of neuromuscular diseases. Int. J. Rob. Res. 28(10), 1322–1333 (2009)CrossRefGoogle Scholar
  14. 14.
    Nakamura, Y., Yamane, K., Fujita, Y., Suzuki, I.: Somatosensory computation for man–machine interface from motion-capture data and musculoskeletal human model. IEEE Trans. Robot. 21(1), 58–66 (2005)CrossRefGoogle Scholar
  15. 15.
    Vicon Systems [Online]. Available: http://www.vivometrics.com/ (2009)
  16. 16.
    Scapellato, S., Cavallo, F., Martelloni, C., Sabatini, A.M.: In-use calibration of body-mounted gyroscopes for applications in gait analysis. Sensor Actuator 123, 418–422 (2005)CrossRefGoogle Scholar
  17. 17.
    Harada, T., Gyota, T., Kuniyoshi, Y., Sato, T.: Development of wireless networked tiny orientation device for wearable motion capture and measurement of walking around, walking up and down, and jumping tasks. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4135–4140 (2007)Google Scholar
  18. 18.
    Bachmann, E.R., Yun, X., McKinney, D., McGhee, R.B., Zyda, M.J.: Design and implementation of MARG sensors for 3-DOF orientation measurement of rigid bodies. IEEE Int. Conf. Robot. Autom. 1, 1171–1178 (2003)Google Scholar
  19. 19.
    Mizoguchi, Y., Itoh, K., Saito, M., Endo, N., Zecca, M., Takanobu, H., Takanishi, A.: Development of a bioinstrumentation system for interaction with a robot—motion capture system of upper body using small attitude sensor modules. In: 25th Annual Conference of the Robotics Society of Japan (RSJ2007), p. 2012 (2007)Google Scholar
  20. 20.
    Foxlin, E.: Inertial head-tracker sensor fusion by a complementary separate-bias Kalman filter. In: Proceedings of the IEEE Virtual Reality Annual International Symposium, vol. 267, pp. 185–194 (1996)Google Scholar
  21. 21.
    Foxlin, E.: Motion tracking requirements and technologies. In: Handbook of Virtual Environment Technologies, pp. 163–210. Lawrence Erlbaum, Hillsdale (2002)Google Scholar
  22. 22.
    Roetenberg, D., Slycke, P.J., Veltink, P.H.: Ambulatory position and orientation tracking fusing magnetic and inertial sensing. IEEE Trans. Biomed. Eng. 54(5), 883–890 (2007)CrossRefGoogle Scholar
  23. 23.
    You, S., Neumann, U.: Fusion of vision and gyro tracking for robust augmented reality registration. In: Proceedings Virtual Reality, pp. 71–78 (2001)Google Scholar
  24. 24.
    Hightower, J., Borriello, G.: Particle filters for location estimation in ubiquitous computing: a case study. In: Proceedings of international Conference on Ubiquitous Computing (UBICOMP), pp. 88–106 (2004)Google Scholar
  25. 25.
    LaViola, J.J.: A comparison of unscented and extended Kalman filtering for estimating quaternion motion. Am. Contr. Conf. 3, 2435–2440 (2003)Google Scholar
  26. 26.
    Kingston, D.B., Beard, R.W.: Real-time attitude and position estimation for small UAVs using low-cost sensors. In: AIAA 3rd Unmanned Unlimited Systems Conference and Workshop (2004)Google Scholar
  27. 27.
    Lee, G.H., Achtelik, M., Fraundorfer, F., Pollefeys, M., Siegwart, R.: A benchmarking tool for MAV visual pose estimation. In: 11th International Conference on Control Automation Robotics & Vision (ICARCV), pp. 1541–1546 (2010)Google Scholar
  28. 28.
    Lin, Z., Zecca, M., Sessa, S., Bartolomeo, L., Ishii, H., Itoh, K., Takanishi, A.: Development of an ultra-miniaturized inertial measurement unit WB-3 for human body motion tracking. In: IEEE/SICE International Symposium on System Integration (SII), pp. 414–419 (2010)Google Scholar
  29. 29.
    Foxlin, E., Altshuler, Y.: Motion-tracking. U.S. Patent 647415905 (2002)Google Scholar
  30. 30.
    Sessa, S., Zecca, M., Lin, Z., Bartolomeo, L., Itoh, K., Ishii, H., Mukaeda, Y., Suzuki, Y., Takanishi, A.: Ultra-miniaturized WB-3 Inertial Measurement Unit: performance evaluation of the attitude estimation. In: IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 998–1003 (2010)Google Scholar
  31. 31.
    Windolf, M., Götzen, N., Morlock, M.: Systematic accuracy and precision analysis of video motion capturing systems exemplified on the Vicon-460 system. J. Biomech. 41(12), 2776–2780 (2008)CrossRefGoogle Scholar
  32. 32.
    Craig, J.J.: Introduction to Robotics: Mechanics and Control, 3rd edn. Prentice Hall (2004)Google Scholar
  33. 33.
    Zhou, H., Hu, H., Tao, Y.: Inertial measurements of upper limb motion. Med. Biol. Eng. Comput. 44(6), 479–487 (2006)CrossRefGoogle Scholar
  34. 34.
    Lloyd, D.G., Alderson, J., Elliott, B.C.: An upper limb kinematic model for the examination of cricket bowling: a case study of Mutiah Muralitharan. J. Sports Sci. 18(12), 975–982 (2000)CrossRefGoogle Scholar
  35. 35.
    Alan, H.: Determinants of the gait transition speed during human locomotion: kinematic factors. J. Biomech. 28(6), 669–677 (1995)CrossRefGoogle Scholar
  36. 36.
    Tong, K., Granat, M.H.: A practical gait analysis system using gyroscopes. Med. Eng. Phys. 21(2), 87–94 (1999)CrossRefGoogle Scholar
  37. 37.
    Zecca, M., Sessa, S., Lin, Z., Sasaki, T., Suzuki, T., Itoh, K., Iseki, H., Takanishi, A.: Development of an ultra-miniaturized inertial measurement unit for objective skill analysis and assessment in neurosurgery: preliminary results. In: MICCAI 2009, Part I, Lecture Notes in Computer Science, vol. 5671, pp. 443–500 (2009)Google Scholar
  38. 38.
    Sessa, S., Zecca, M., Lin, Z., Sasaki, T., Itoh, K., Takanishi, A.: Waseda Bioinstrumentation System 3 as a tool for objective rehabilitation measurement and assessment—development of the inertial measurement unit. In: IEEE International Conference on Rehabilitation Robotics, pp. 115–120 (2009)Google Scholar
  39. 39.
    Lin, Z., Zecca, M., Sessa, S., Ishii, H., Takanishi, A.: Development of an ultra-miniaturized inertial measurement unit for jaw movement analysis during free chewing. J. Comput. Sci. 6(8), 896–903 (2010b)CrossRefGoogle Scholar
  40. 40.
    Sessa, S., Zecca, M., Lin, Z., Sasaki, T., Suzuki, T., Itoh, K., Iseki, H., Takanishi, A.: Objective skill analysis and assessment of neurosurgery by using the waseda bioinstrumentation system WB-3. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4086–4091 (2009)Google Scholar
  41. 41.
    Welch, G., Bishop, G.: An Introduction to the Kalman Filter. University of North Carolina, Chapel Hill (1995)Google Scholar
  42. 42.
    Kourepenis, A., Borenstein, J., Connelly, J., Elliott, R., Ward, P., Weinberg, M.: Performance of MEMS inertial sensors. In: Position Location and Navigation Symposium, pp. 1–8 (1998)Google Scholar
  43. 43.
    Sabatini, A.M.: Quaternion-based extended Kalman filter for determining orientation by inertial and magnetic sensing. IEEE Trans. Biomed. Eng. 53(7), 1346–1356 (2006)CrossRefGoogle Scholar
  44. 44.
    Sabatini, A.M.: A wavelet-based bootstrap method applied to inertial sensor stochastic error modelling using the Allan variance. Meas. Sci. Technol. 17, 2980–2988 (2006)CrossRefGoogle Scholar
  45. 45.
    Alonso, R., Shuster, M.D.: Attitude-independent magnetometer-bias determination: a survey. J. Astronaut. Sci. 50(4), 453–475 (2002)Google Scholar
  46. 46.
    Gebre-egziabher, D., Elkaim, G.H., Powell, J.D., Parkinson, B.W.: A non-linear, two-step estimation algorithm for calibrating solid-state strapdown magnetometers. In: 8th International St. Petersburg confernce on Navigation Systems, pp. 28–30 (2001)Google Scholar
  47. 47.
    Gebre-Egziabher, D., Elkaim, G.H., Powell, J.D., Parkinson, B.W.: Calibration of strapdown magnetometers in magnetic field domain. J. Aerosp. Eng. 19(2), 87–102 (2006)CrossRefGoogle Scholar
  48. 48.
    Campolo, D., Fabris, M., Cavallo, G., Accoto, D., Keller, F., Guglielmelli, E.: A novel procedure for in-field calibration of sourceless inertial/magnetic orientation tracking wearable devices. In: The First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, pp. 471–476 (2006)Google Scholar
  49. 49.
    Mayagoitia, R.E., Nene, A.V., Veltink, P.H.: Accelerometer and rate gyroscope measurement of kinematics: an inexpensive alternative to optical motion analysis systems. J. Biomech. 35(4), 537–542 (2002)CrossRefGoogle Scholar
  50. 50.
    Ojeda, L., Borenstein, J.: Non-GPS navigation for security personnel and first responders. J. Navig. 60(03), 391–407 (2007)CrossRefGoogle Scholar
  51. 51.
    Kavanagh, J.J., Menz, H.B.: Accelerometry: a technique for quantifying movement patterns during walking. Gait Posture 28(1), 1–15 (2008)CrossRefGoogle Scholar
  52. 52.
    Sivrikaya, F., Yener, B.: Time synchronization in sensor networks: a survey. IEEE Netw. 18(4), 45–50 (2004)CrossRefGoogle Scholar
  53. 53.
    Sankoff, D., Kruskal, J.B.: Time Warps, String Edits, and Macromolecules: The Theory and Practice of Sequence Comparison. Addison-Wesley (1983)Google Scholar
  54. 54.
    Salvador, S., Chan, P.: Toward accurate dynamic time warping in linear time and space. Intell. Data Anal. 11(5), 561–580 (2007)Google Scholar
  55. 55.
    Shen, S., Michael, N., Kumar, V.: Autonomous multi-floor indoor navigation with a computationally constrained MAV. In: IEEE International Conference on Robotics and Automation, pp. 20–25 (2011)Google Scholar
  56. 56.
    Heng, L., Meier, L., Tanskanen, P., Fraundorfer, F., Pollefeys, M.: Autonomous obstacle avoidance and maneuvering on a vision-guided MAV using on-board processing. IEEE International Conference on Robotics and Automation, pp. 2472–2477 (2011)Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Salvatore Sessa
    • 1
  • Massimiliano Zecca
    • 3
    • 5
    • 6
  • Zhuohua Lin
    • 1
    • 7
  • Luca Bartolomeo
    • 2
    • 7
  • Hiroyuki Ishii
    • 8
  • Atsuo Takanishi
    • 4
    • 5
    • 6
    • 7
  1. 1.Graduate School of Creative Science and EngineeringWaseda UniversityTokyoJapan
  2. 2.Graduate School of Advanced Science and EngineeringWaseda UniversityTokyoJapan
  3. 3.School of Creative Science and EngineeringWaseda UniversityTokyoJapan
  4. 4.Department of Modern Mechanical EngineeringWaseda UniversityTokyoJapan
  5. 5.HRI - Humanoid Robotics InstituteWaseda UniversityTokyoJapan
  6. 6.Italy-Japan Joint Laboratory on Humanoid and Personal Robotics “RoboCasa”Waseda UniversityTokyoJapan
  7. 7.Global Robot AcademiaWaseda UniversityTokyoJapan
  8. 8.Waseda Research Institute for Science and EngineeringWaseda UniversityTokyoJapan

Personalised recommendations