Skip to main content
Log in

Gait phase recognition of multi-mode locomotion based on multi-layer perceptron for the plantar pressure measurement system

  • Regular Paper
  • Published:
International Journal of Intelligent Robotics and Applications Aims and scope Submit manuscript

Abstract

The gait phase recognition has some broad application prospects, such as lower limb exoskeleton (LLE). To accurately identify the gait phase in different locomotion modes according to gait patterns and corresponding gait characteristics, we define sets of gait phases and propose a gait phase recognition model using plantar pressure sensing signals. The gait phase recognition algorithm based on the multi-layer perceptron (MLP) is used to study the gait phase recognition in walking and running modes. The experimental results show that the gait phase recognition model can recognize the gait phase in different motion modes based on the plantar pressure sensing information. The gait phase recognition of multi-mode locomotion can provide sufficient control logic reference for the powered exoskeleton robot. Through the data of 1052 gait cycles of 4 participants in the experiment, the accuracy of gait recognition for walking mode is 93.9%, and the accuracy of gait recognition for flying state is 76.5%.

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

Access this article

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

Similar content being viewed by others

Data availability

The datasets used and analyzed during the current study are available from the corresponding author on reasonably request.

References

  • Abaid, N., Cappa, P., Palermo, E., Petrarca, M., Porfiri, M.: Gait detection in children with and without hemiplegia using single-axis wearable gyroscopes. PLoS ONE 8(9), e73152 (2013)

    Article  Google Scholar 

  • Chen, K., Trkov, M., Yi, J., Zhang, Y., Liu, T., Song, D.: A robotic bipedal model for human walking with slips. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 6301–6306. (2015)

  • Dollar, A.M., Herr, H.: Lower extremity exoskeletons and active orthoses: challenges and state-of-the-art. IEEE Trans. Rob. 24(1), 144–158 (2008)

    Article  Google Scholar 

  • Evans, R.L., Arvind, D.: Detection of gait phases using orient specks for mobile clinical gait analysis. In: 2014 11th International Conference on Wearable and Implantable Body Sensor Networks, pp. 149–154. IEEE (2014)

  • Farris, R.J., Quintero, H.A., Murray, S.A., Ha, K.H., Hartigan, C., Goldfarb, M.: A preliminary assessment of legged mobility provided by a lower limb exoskeleton for persons with paraplegia. IEEE Trans. Neural Syst. Rehabil. Eng. 22(3), 482–490 (2013)

    Article  Google Scholar 

  • Joshi, C.D., Lahiri, U., Thakor, N.V.: Classification of gait phases from lower limb EMG: Application to exoskeleton orthosis. In: 2013 IEEE Point-of-Care Healthcare Technologies (PHT), pp. 228–231. IEEE (2013)

  • Jung, J.-Y., Heo, W., Yang, H., Park, H.: A neural network-based gait phase classification method using sensors equipped on lower limb exoskeleton robots. Sensors 15(11), 27738–27759 (2015)

    Article  Google Scholar 

  • Kazerooni, H., Steger, R., Huang, L.: Hybrid control of the Berkeley lower extremity exoskeleton (BLEEX). Int. J. Robot. Res. 25(5–6), 561–573 (2006)

    Article  Google Scholar 

  • Lewis, C.L., Ferris, D.P.: Invariant hip moment pattern while walking with a robotic hip exoskeleton. J. Biomech. 44(5), 789–793 (2011)

    Article  Google Scholar 

  • Li, J., Shen, B., Chew, C.-M., Teo, C.L., Poo, A.N.: Novel functional task-based gait assistance control of lower extremity assistive device for level walking. IEEE Trans. Industr. Electron. 63(2), 1096–1106 (2015)

    Article  Google Scholar 

  • Luu, T.P., Low, K.H., Qu, X., Lim, H.B., Hoon, K.H.: Hardware development and locomotion control strategy for an over-ground gait trainer: NaTUre-Gaits. IEEE J. Transl. Eng. Health Med. 2, 1–9 (2014)

    Article  Google Scholar 

  • Mannini, A., Sabatini, A.M.: Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors 10(2), 1154–1175 (2010)

    Article  Google Scholar 

  • Mohammed, S., Same, A., Oukhellou, L., Kong, K., Huo, W., Amirat, Y.: Recognition of gait cycle phases using wearable sensors. Robot. Auton. Syst. 75, 50–59 (2016)

    Article  Google Scholar 

  • Murray, S.A., Ha, K.H., Hartigan, C., Goldfarb, M.: An assistive control approach for a lower-limb exoskeleton to facilitate recovery of walking following stroke. IEEE Trans. Neural Syst. Rehabil. Eng. 23(3), 441–449 (2014)

    Article  Google Scholar 

  • Oh, S., Baek, E., Song, S.-K., Mohammed, S., Jeon, D., Kong, K.: A generalized control framework of assistive controllers and its application to lower limb exoskeletons. Robot. Auton. Syst. 73, 68–77 (2015)

    Article  Google Scholar 

  • Pappas, I.P., Popovic, M.R., Keller, T., Dietz, V., Morari, M.: A reliable gait phase detection system. IEEE Trans. Neural Syst. Rehabil. Eng. 9(2), 113–125 (2001)

    Article  Google Scholar 

  • Qi, Y., Soh, C.B., Gunawan, E., Low, K.-S., Thomas, R.: Assessment of foot trajectory for human gait phase detection using wireless ultrasonic sensor network. IEEE Trans. Neural Syst. Rehabil. Eng. 24(1), 88–97 (2015)

    Article  Google Scholar 

  • St-Onge, N., Feldman, A.G.: Interjoint coordination in lower limbs during different movements in humans. Exp. Brain Res. 148(2), 139–149 (2003)

    Article  Google Scholar 

  • Taborri, J., Rossi, S., Palermo, E., Patanè, F., Cappa, P.: A novel HMM distributed classifier for the detection of gait phases by means of a wearable inertial sensor network. Sensors 14(9), 16212–16234 (2014)

    Article  Google Scholar 

  • Taborri, J., Scalona, E., Palermo, E., Rossi, S., Cappa, P.: Validation of inter-subject training for hidden Markov models applied to gait phase detection in children with cerebral palsy. Sensors 15(9), 24514–24529 (2015)

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Tran, H.T., Cheng, H., Rui, H., Lin, X., Duong, M.K., Chen, Q.: Evaluation of a fuzzy-based impedance control strategy on a powered lower exoskeleton. Int. J. Soc. Robot. 8(1), 103–123 (2016)

    Article  Google Scholar 

  • Tucker, M.R., et al.: Control strategies for active lower extremity prosthetics and orthotics: a review. J. Neuroeng. Rehabil. 12(1), 1–30 (2015)

    Article  Google Scholar 

  • Wang, P., Low, K., McGregor, A.: A subject-based motion generation model with adjustable walking pattern for a gait robotic trainer: NaTUre-gaits. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1743–1748. IEEE (2011)

  • Yan, T., Cempini, M., Oddo, C.M., Vitiello, N.: Review of assistive strategies in powered lower-limb orthoses and exoskeletons. Robot. Auton. Syst. 64, 120–136 (2015)

    Article  Google Scholar 

Download references

Acknowledgements

This research was funded by the National Natural Science Foundation of China (Grant No. U20A20282; 51775325); National Key Research and Development Program of China (Grant No. 2018YFB1309200); Young Eastern Scholars Program of Shanghai (Grant No. QD2016033). The authors would like to thank the anonymous reviewers for their constructive comments that will help us to improve the quality of this manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bin Ren.

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

Ren, B., Liu, J., Guan, W. et al. Gait phase recognition of multi-mode locomotion based on multi-layer perceptron for the plantar pressure measurement system. Int J Intell Robot Appl 7, 602–614 (2023). https://doi.org/10.1007/s41315-023-00283-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41315-023-00283-1

Keywords

Navigation