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%.
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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)
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)
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)
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)
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)
Lewis, C.L., Ferris, D.P.: Invariant hip moment pattern while walking with a robotic hip exoskeleton. J. Biomech. 44(5), 789–793 (2011)
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)
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)
Mannini, A., Sabatini, A.M.: Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors 10(2), 1154–1175 (2010)
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)
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)
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)
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)
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)
St-Onge, N., Feldman, A.G.: Interjoint coordination in lower limbs during different movements in humans. Exp. Brain Res. 148(2), 139–149 (2003)
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)
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)
Taborri, J., Palermo, E., Rossi, S., Cappa, P.: Gait partitioning methods: A systematic review. Sensors 16(1), 66 (2016)
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)
Tucker, M.R., et al.: Control strategies for active lower extremity prosthetics and orthotics: a review. J. Neuroeng. Rehabil. 12(1), 1–30 (2015)
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)
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.
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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
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DOI: https://doi.org/10.1007/s41315-023-00283-1