Skip to main content

Development of High Rate Wearable MIMU Tracking System Robust to Magnetic Disturbances and Body Acceleration

  • Conference paper
  • First Online:
Intelligent Systems and Applications (IntelliSys 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1037))

Included in the following conference series:

Abstract

In this paper we present a wearable high rate MIMU (magnetic-inertial measurement unit) based body tracking system. It is designed using low cost state-of-the-art hardware and MEMS sensors to reduce errors and improve computational latency. Our system allows for high rate data acquisition and sensor fusion at low power budget. It can be used for range of applications from extreme activity capture and biomechanical analysis to clinical evaluation and ambulatory health monitoring/rehabilitation. The package size of sensing nodes is small, and we use textile wires which make it very flexible. Thus entire system can be easily integrated with body worn suit/pants. Up to 7x nodes can be connected without compromising the maximum sampling frequency (1 kHz), with the possibility to add more nodes using additional bridge stations between nodes. The acquisition rate can be preset from 1 kHz to 100 Hz to suit the application or accuracy requirements. To the best of our knowledge, our inertial motion capture system is the first to offer such high rate output at 1 kHz for multiple nodes. The high rate of inertial data provides intrinsic accuracy to sensor fusion as well as capture high frequency features for clinical diagnostics and biomechanical analysis in ambient settings. The system also runs an embedded sensor fusion algorithm for accurate orientation estimation. We introduce a novel accelerometer and magnetometer measurement correction with adaptive sensor covariance approach in EKF, which makes it robust to both magnetic disturbances and body accelerations. Thus it is well suited for indoor human motion analysis and monitoring highly dynamic motion.

H. T. Butt, M. Pancholi, M. Musahl and M. A. Sanchez—Equal contribution in paper.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Xsens Homepage, https://www.xsens.com/products/mtw-awinda/, last accessed 2019/3/19.

  2. 2.

    Available: https://www.xsens.com/products/mtw-awinda/.

  3. 3.

    Available: https://www.xsens.com/products/mtw-awinda/.

  4. 4.

    Available: https://www.xsens.com/products/mtw-awinda/.

References

  1. Robert-Lachaine, X., et al.: Effect of local magnetic field disturbances on inertial measurement units accuracy. Appl. Ergon. 63, 123–132 (2017)

    Article  Google Scholar 

  2. Yun, X., Bachmann, E.R.: Design, implementation, and experimental results of a quaternion-based Kalman filter for human body motion tracking. IEEE Trans. Robot. 22, 1216–1227 (2006)

    Article  Google Scholar 

  3. Hu, J.-S., Sun, K.-C.: A robust orientation estimation algorithm using MARG sensors. IEEE Trans. Instrum. Meas. 64, 815–822 (2014)

    Google Scholar 

  4. Daponte, P., et al.: Compensating magnetic disturbances on MARG units by means of a low complexity data fusion algorithm. In: IEEE International Symposium on Medical Measurements and Applications (MeMeA), pp. 157–162 (2015)

    Google Scholar 

  5. Seel, T., et al.: Joint axis and position estimation from inertial measurement data by exploiting kinematic constraints. In: IEEE International Conference on Control Applications (CCA), pp. 45–49 (2012)

    Google Scholar 

  6. Miezal, M., et al.: A generic approach to inertial tracking of arbitrary kinematic chains. In: Proceedings of the 8th International Conference on Body Area Networks, pp. 189–192 (2013)

    Google Scholar 

  7. Poddar, S., et al.: A comprehensive overview of inertial sensor calibration techniques. J. Dyn. Syst. Meas. Control 139, 011006 (2017)

    Article  Google Scholar 

  8. Wu, Y., et al.: Dynamic magnetometer calibration and alignment to inertial sensors by Kalman filtering. IEEE Trans. Control Syst. Technol. 26, 716–723 (2018)

    Article  Google Scholar 

  9. Fan, B., et al.: An adaptive orientation estimation method for magnetic and inertial sensors in the presence of magnetic disturbances. Sensors 17, 1161 (2017)

    Article  Google Scholar 

  10. Chesneau, C.-I., et al.: Improving magneto-inertial attitude and position estimation by means of a magnetic heading observer. In: International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–8 (2017)

    Google Scholar 

  11. Pancholi, M., et al.: Relative translation and rotation calibration between optical target and inertial measurement unit. In: International Conference on Sensor Systems and Software, pp. 175–186 (2016)

    Google Scholar 

  12. Ricci, L., et al.: On the orientation error of IMU: investigating static and dynamic accuracy targeting human motion. PLoS ONE 11, e0161940 (2016)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hammad Tanveer Butt .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Butt, H.T., Pancholi, M., Musahl, M., Sanchez, M.A., Stricker, D. (2020). Development of High Rate Wearable MIMU Tracking System Robust to Magnetic Disturbances and Body Acceleration. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1037. Springer, Cham. https://doi.org/10.1007/978-3-030-29516-5_87

Download citation

Publish with us

Policies and ethics