Skip to main content

Advertisement

Log in

Robust Gait Event Detection Based on the Kinematic Characteristics of a Single Lower Extremity

  • Regular Paper
  • Published:
International Journal of Precision Engineering and Manufacturing Aims and scope Submit manuscript

Abstract

The observation of gait phases provides essential information for controller design and performance evaluation of lower extremity wearable robots. Specifically, gait events are often defined and detected to distinguish the transition of gait phases. To achieve this, rule-based gait event detection algorithms detect gait events by utilizing the repetitive features in human walking with very few sensors and simple logic. Besides, many of these algorithms define gait events as characteristic features that are detectable from the sensor measurements. However, conventional methods have not fully considered the correlation between the sensor measurement and characteristics of the human motion. Moreover, these methods were only accurate for a limited condition of human motion, for example, walking only or running without sensor noise. Therefore, in this paper, we propose a gait event detection algorithm considering the full kinematic characteristics of the lower extremity under various gait conditions. The proposed algorithm demonstrates robust performance for both walking and running. Besides, to minimize the time delay and the false information in the detected gait events, this paper also proposes a robust signal dithering algorithm that reduces the sensor noise with a limited phase delay. Overall, the performances of the proposed methods are verified through gait experiments with human subjects.

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

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Lin, F., Wang, A., Zhuang, Y., Tomita, M. R., & Xu, W. (2016). Smart insole: A wearable sensor device for unobtrusive gait monitoring in daily life. IEEE Transactions on Industrial Informatics, 12(6), 2281–2291.

    Article  Google Scholar 

  2. Gong, Y., & Park, K. (2021). Bilateral gait asymmetry in patients with hallux valgus using normalized cross-correlation function. International Journal of Precision Engineering and Manufacturing, 22, 373–382.

    Article  Google Scholar 

  3. Park, K.-W., Choi, J., & Kong, K. (2023). Hybrid filtered disturbance observer for precise motion generation of a powered exoskeleton. IEEE Transactions on Industrial Electronics, 70(1), 646–656.

    Article  Google Scholar 

  4. Kong, K., Choi, J., Park, K.-W., Park, J., Lee, D.-H., Song, E., Na, B., Jeon, S., Kim, T., Choi, H., Woo, H., Lee, J.-H., Kim, B., & Rha, D.-W. (2021). The history and future of the walkon suit: A powered exoskeleton for people with disabilities. IEEE Industrial Electronics Magazine, 16, 16.

    Google Scholar 

  5. Kim, T., Jeong, M., & Kong, K. (2022). Bioinspired knee joint of a lower-limb exoskeleton for misalignment reduction. IEEE/ASME Transactions on Mechatronics, 27(3), 1223–1232.

    Article  Google Scholar 

  6. Sawicki, G. S., Beck, O. N., Kang, I., & Young, A. J. (2020). The exoskeleton expansion: Improving walking and running economy. Journal of neuroengineering and rehabilitation, 17(1), 1–9.

    Article  Google Scholar 

  7. Huo, W., Mohammed, S., Amirat, Y., & Kong, K. (2018). Fast gait mode detection and assistive torque control of an exoskeletal robotic orthosis for walking assistance. IEEE Transactions on Robotics, 34(4), 1035–1052.

    Google Scholar 

  8. Levine, D., Richards, J., & Whittle, M.W. (2012). Whittle’s gait analysis. Elsevier health sciences, 5th Ed.

  9. Taborri, J., Palermo, E., Rossi, S., & Cappa, P. (2016). Gait partitioning methods: A systematic review. Sensors, 16(1), 66.

    Article  Google Scholar 

  10. Lim, D.-H., Kim, W.-S., Kim, H.-J., & Han, C.-S. (2017). Development of real-time gait phase detection system for a lower extremity exoskeleton robot. International Journal of Precision Engineering and Manufacturing, 18(5), 681–687.

    Article  Google Scholar 

  11. Kong, K., & Tomizuka, M. (2009). A gait monitoring system based on air pressure sensors embedded in a shoe. IEEE/ASME Transactions on mechatronics, 14(3), 358–370.

    Article  Google Scholar 

  12. Jung, S. Y., Fekiri, C., Kim, H.-C., & Lee, I. H. (2022). Development of plantar pressure distribution measurement shoe insole with built-in printed curved sensor structure. International Journal of Precision Engineering and Manufacturing, 23(5), 565–572.

    Article  Google Scholar 

  13. Pérez-Ibarra, J. C., Siqueira, A. A. G., & Krebs, H. I. (2020). Real-time identification of gait events in impaired subjects using a single-imu foot-mounted device. IEEE Sensors Journal, 20(5), 2616–2624.

    Article  Google Scholar 

  14. Nazarahari, M., Khandan, A., Khan, A., & Rouhani, H. (2022). Foot angular kinematics measured with inertial measurement units: A reliable criterion for real-time gait event detection. Journal of Biomechanics, 130, 1–8.

    Article  Google Scholar 

  15. Ding, S., Ouyang, X., Liu, T., Li, Z., & Yang, H. (2018). Gait event detection of a lower extremity exoskeleton robot by an intelligent imu. IEEE Sensors Journal, 18(23), 9728–9735.

    Article  Google Scholar 

  16. Sánchez Manchola, M. D., Bernal, M. J. P., Munera, M., & Cifuentes, C. A. (2019). Gait phase detection for lower-limb exoskeletons using foot motion data from a single inertial measurement unit in hemiparetic individuals. Sensors, 19(13), 2988.

    Article  Google Scholar 

  17. Bejarano, N. C., Ambrosini, E., Pedrocchi, A., Ferrigno, G., Monticone, M., & Ferrante, S. (2014). A novel adaptive, real-time algorithm to detect gait events from wearable sensors. IEEE Transactions on Neural systems and Rehabilitation Engineering, 23(3), 413–422.

    Article  Google Scholar 

  18. Catalfamo, P., Ghoussayni, S., & Ewins, D. (2010). Gait event detection on level ground and incline walking using a rate gyroscope. Sensors, 10(6), 5683–5702.

    Article  Google Scholar 

  19. Kim, J., Lee, G., Heimgartner, R., Arumukhom Revi, D., Karavas, N., Nathanson, D., Galiana, I., Eckert-Erdheim, A., Murphy, P., Perry, D., et al. (2019). Reducing the metabolic rate of walking and running with a versatile, portable exosuit. Science, 365(6454), 668–672.

    Article  Google Scholar 

  20. Kolaghassi, R., Al-Hares, M. K., & Sirlantzis, K. (2021). Systematic review of intelligent algorithms in gait analysis and prediction for lower limb robotic systems. IEEE Access, 9, 113788.

    Article  Google Scholar 

  21. Chen, C., Wu, X., Liu, D.-X., Feng, W., & Wang, C. (2017). Design and voluntary motion intention estimation of a novel wearable full-body flexible exoskeleton robot. Mobile Information Systems. https://doi.org/10.1155/2017/8682168

    Article  Google Scholar 

  22. Perry, J., & Burnfield, J.M. (2010). Gait analysis. Normal and pathological function 2nd Ed.

  23. Godiyal, A. K., Verma, V., Khanna, N., & Joshi, D. (2020). Force myography and its application to human locomotion. Algorithms and ApplicationsBiomedical Signal Processing: Advances in Theory (pp. 49–70). Singapore: Springer.

  24. Hodges, P. W., & Bui, B. H. (1996). A comparison of computer-based methods for the determination of onset of muscle contraction using electromyography. Electroencephalography and Clinical Neurophysiology/Electromyography and Motor Control, 101(6), 511–519.

    Article  Google Scholar 

  25. Nene, A., Mayagoitia, R., & Veltink, P. (1999). Assessment of rectus femoris function during initial swing phase. Gait & posture, 9(1), 1–9.

    Article  Google Scholar 

  26. Lee, H., Kim, W., Han, J., & Han, C. (2012). The technical trend of the exoskeleton robot system for human power assistance. International Journal of Precision Engineering and Manufacturing, 13(8), 1491–1497.

    Article  Google Scholar 

  27. Bayón, C., Keemink, A. Q., van Mierlo, M., Rampeltshammer, W., van der Kooij, H., & van Asseldonk, E. H. (2022). Cooperative ankle-exoskeleton control can reduce effort to recover balance after unexpected disturbances during walking. Journal of Neuroengineering and Rehabilitation, 19(1), 1–16.

    Article  Google Scholar 

  28. Lee, M., & Park, S. (2020). Estimation of three-dimensional lower limb kinetics data during walking using machine learning from a single imu attached to the sacrum. Sensors, 20(21), 6277.

    Article  Google Scholar 

  29. Sinclair, J., Greenhalgh, A., Edmundson, C. J., Brooks, D., & Hobbs, S. J. (2012). Gender differences in the kinetics and kinematics of distance running: Implications for footwear design. International Journal of Sports Science and Engineering, 6(2), 118–128.

    Google Scholar 

Download references

Acknowledgements

This research was financially supported by the Institute of Civil Military Technology Cooperation funded by the Defense Acquisition Program Administration and Ministry of Trade, Industry and Energy of Korean government Under Grant No. 19-CM-GU-01.

Author information

Authors and Affiliations

Authors

Contributions

All authors read and approved the final manuscript.

Corresponding author

Correspondence to Kyoungchul Kong.

Ethics declarations

Competing interests

The authors declares that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, G.T., Lee, M., Kim, Y. et al. Robust Gait Event Detection Based on the Kinematic Characteristics of a Single Lower Extremity. Int. J. Precis. Eng. Manuf. 24, 987–1000 (2023). https://doi.org/10.1007/s12541-023-00807-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12541-023-00807-6

Keywords

Navigation