A Real-Time On-Chip Algorithm for IMU-Based Gait Measurement

  • Shenggao Zhu
  • Hugh Anderson
  • Ye Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7674)

Abstract

This paper presents a real-time and on-chip gait measurement algorithm used in our Gait Measurement System (GMS). Our GMS is a small foot-mounted device based on an Inertial Measurement Unit (IMU), which contains an accelerometer and a gyroscope. The GMS can compute spatio-temporal gait parameters in real-time and transmit them to a remote receiver. Measured gait parameters include cadence, velocity, stride length, swing/stance ratio and so on. The algorithm is optimized to run in a ATmega328 microprocessor with only 2kB data memory. During a walking session, each stride is recognized instantaneously, and the stride length and other parameters are computed at the same time. Although inexpensive components are utilized, the algorithm achieves high accuracy, with an average stride length error smaller than 3%, and error in total walking distance less than 2%.

Keywords

Gait Measurement Algorithm IMU Real-Time On-Chip 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bachlin, M., Plotnik, M., Roggen, D., Maidan, I., Hausdorff, J., Giladi, N., Troster, G.: Wearable Assistant for Parkinson’s Disease Patients With the Freezing of Gait Symptom. IEEE Transactions on Information Technology in Biomedicine 14(2), 436–446 (2010)CrossRefGoogle Scholar
  2. 2.
    Lee, Y., Kim, J., Son, M., Lee, M.: Implementation of Accelerometer Sensor Module and Fall Detection Monitoring System based on Wireless Sensor Network. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2007, pp. 2315–2318 (August 2007)Google Scholar
  3. 3.
    Bamberg, S., Benbasat, A., Scarborough, D., Krebs, D., Paradiso, J.: Gait Analysis Using a Shoe-Integrated Wireless Sensor System. IEEE Transactions on Information Technology in Biomedicine 12(4), 413–423 (2008)CrossRefGoogle Scholar
  4. 4.
    Yang, C.C., Hsu, Y.L., Shih, K.S., Lu, J.M., Chan, L.: Real-time gait cycle parameters recognition using a wearable motion detector. In: 2011 International Conference on System Science and Engineering, ICSSE, pp. 498–502 (June 2011)Google Scholar
  5. 5.
    Thaut, M.H., McIntosh, G.C., Rice, R.R., Miller, R.A., Rathbun, J., Brault, J.M.: Rhythmic auditory stimulation in gait training for Parkinson’s disease patients. Movement Disorders 11(2), 193–200 (1996)CrossRefGoogle Scholar
  6. 6.
    Li, Z., Xiang, Q., Hockman, J., Yang, J., Yi, Y., Fujinaga, I., Wang, Y.: A music search engine for therapeutic gait training. In: Proceedings of the International Conference on Multimedia, MM 2010, pp. 627–630. ACM, New York (2010)CrossRefGoogle Scholar
  7. 7.
    Ojeda, L., Borenstein, J.: Non-GPS navigation for security personnel and first responders. Journal of Navigation 60(3), 391–407 (2007)CrossRefGoogle Scholar
  8. 8.
    Facchinetti, T., Savioli, A., Goldoni, E.: Design and development of a real-time embedded inertial measurement unit. In: Proceedings of the 2010 ACM Symposium on Applied Computing, SAC 2010, pp. 491–495. ACM, New York (2010)CrossRefGoogle Scholar
  9. 9.
    Li, Q., Young, M., Naing, V., Donelan, J.: Walking speed estimation using a shank-mounted inertial measurement unit. Journal of Biomechanics 43(8), 1640–1643 (2010)CrossRefGoogle Scholar
  10. 10.
    Sabatini, A., Martelloni, C., Scapellato, S., Cavallo, F.: Assessment of walking features from foot inertial sensing. IEEE Transactions on Biomedical Engineering 52(3), 486–494 (2005)CrossRefGoogle Scholar
  11. 11.
    Song, Y., Shin, S., Kim, S., Lee, D., Lee, K.: Speed Estimation From a Tri-axial Accelerometer Using Neural Networks. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2007, pp. 3224–3227 (August 2007)Google Scholar
  12. 12.
    Miyazaki, S.: Long-term unrestrained measurement of stride length and walking velocity utilizing a piezoelectric gyroscope. IEEE Transactions on Biomedical Engineering 44(8), 753–759 (1997)CrossRefGoogle Scholar
  13. 13.
    Alvarez, J., Gonzalez, R., Alvarez, D., Lopez, A., Rodriguez-Uria, J.: Multisensor Approach to Walking Distance Estimation with Foot Inertial Sensing. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2007, pp. 5719–5722 (August 2007)Google Scholar
  14. 14.
    Peruzzi, A., Croce, U.D., Cereatti, A.: Estimation of stride length in level walking using an inertial measurement unit attached to the foot: A validation of the zero velocity assumption during stance. Journal of Biomechanics 44(10), 1991–1994 (2011)CrossRefGoogle Scholar
  15. 15.
    Zhu, S., Anderson, H., Wang, Y.: Reducing the Power Consumption of an IMU-Based Gait Measurement System. In: Weisi, L., Dong, X., Anthony, H., Jianxin, W., Ying, H., Jianfei, C., Mohan, K., Ming-Ting, S. (eds.) PCM 2012. LNCS, vol. 7674, pp. 105–116. Springer, Heidelberg (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Shenggao Zhu
    • 1
  • Hugh Anderson
    • 1
  • Ye Wang
    • 1
  1. 1.School of ComputingNational University of SingaporeSingapore

Personalised recommendations