single-jc.php

JACIII Vol.29 No.1 pp. 53-63
doi: 10.20965/jaciii.2025.p0053
(2025)

Research Paper:

sEMG Signal-Based Human Lower-Limb Intention Recognition Algorithm Using Improved Extreme Learning Machine

Shengbiao Wu*,**,†, Xianpeng Cheng*, and Huaning Li*

*School of Mechanical and Electronic Engineering, East China University of Technology
418 Guanglan Avenue, Nanchang, Jiangxi 330013, China

**Jiangxi Industry Technology Research Institute of Rehabilitation Assistance
Nanchang, Jiangxi, China

Corresponding author

Received:
June 6, 2024
Accepted:
October 4, 2024
Published:
January 20, 2025
Keywords:
human lower-limbs, intention recognition, feature extraction, electromyographic signal
Abstract

To address the difficulty in identifying human lower-limb movement intentions, low accuracy of classification models, and weak generalization ability, this study proposes a motion intention recognition method that combines an improved pelican optimization algorithm (IPOA) and a hybrid kernel extreme learning machine (HKELM). First, we collect the surface electromyography (sEMG) signals of subjects in six motion modes and perform feature parameter extraction under non-ideal conditions. On this basis, we establish a dataset of the relationships between the feature parameters and gait movements. Second, we build a motion intention classification model based on relational data using the HKELM to solve the problems of low modeling accuracy and weak generalizability. Third, the IPOA is used to optimize the parameters related to the HKELM, and a differential evolution algorithm is introduced to improve the population quality and prevent the algorithm from falling into a local optimal solution. The experimental results show that the IPOA exhibits better optimization accuracy and convergence speed for four classical benchmark functions. Its average classification accuracy, average classification recall, and average F-value are 94.45%, 94.47%, and 94.46%, respectively, which are significantly higher than those of other intention recognition algorithms. Therefore, the proposed method has high classification accuracy and generalization performance.

Cite this article as:
S. Wu, X. Cheng, and H. Li, “sEMG Signal-Based Human Lower-Limb Intention Recognition Algorithm Using Improved Extreme Learning Machine,” J. Adv. Comput. Intell. Intell. Inform., Vol.29 No.1, pp. 53-63, 2025.
Data files:
References
  1. [1] T. Wang, B. Zhang, C. Liu, T. Liu, Y. Han, S. Wang, J. P. Ferreira, W. Dong, and X. Zhang, “A Review on the Rehabilitation Exoskeletons for the Lower Limbs of the Elderly and the Disabled,” Electronics, Vol.11, No.3, Article No.388, 2022. https://doi.org/10.3390/electronics11030388
  2. [2] D. Zhao, T. Zhang, H. Liu, J, Yang, and H. Yokoi, “Gait rehabilitation training robot: A motion-intention recognition approach with safety and convenience,” Rob. Auton. Syst., Vol.158, Article No.104260, 2022. https://doi.org/10.1016/j.robot.2022.104260
  3. [3] R. Singh, T. Miller, J. Newn, E. Velloso, F. Vetere, and L. Sonenberg, “Combining gaze and AI planning for online human intention recognition,” Artif. Intell., Vol.284, Article No.103275, 2020. https://doi.org/10.1016/j.artint.2020.103275
  4. [4] Y. Liu, K. Liu, G. Wang, Z. Sun, and L. Jin, “Noise-tolerant zeroing neurodynamic algorithm for upper limb motion intention-based human–robot interaction control in non-ideal conditions,” Expert Systems with Applications, Vol.213, Article No.118891, 2023. https://doi.org/10.1016/j.eswa.2022.118891
  5. [5] Z. Zhang and F. Sup, “Activity recognition of the torso based on surface electromyography for exoskeleton control,” Biomedical Signal Processing and Control, Vol.10, pp. 281-288, 2014. https://doi.org/10.1016/j.bspc.2013.10.002
  6. [6] T. Baldacchino, W. R. Jacobs, S. R. Anderson, K. Worden, and J. Rowson, “Simultaneous Force Regression and Movement Classification of Fingers via Surface EMG within a Unified Bayesian Framework,” Front. Bioeng. Biotechnol., Vol.6, Article No.13, 2018. https://doi.org/10.3389/fbioe.2018.00013
  7. [7] L. Zhang, Y. Shi, W. Wang, Y. Chu, and X. Yuan, “Real-time and user-independent feature classification of forearm using EMG signals,” J. Soc. Inf. Display., Vol.27, No.2, pp. 101-107, 2019. https://doi.org/10.1002/jsid.749
  8. [8] M. Zhu, X. Guan, Z. Li, L. He, Z. Wang, and K. Cai, “sEMG-Based lower limb motion prediction using CNN-LSTM with improved PCA optimization algorithm,” J. Bionic. Eng., Vol.20, No.2, pp. 612-627, 2023. https://doi.org/10.1007/s42235-022-00280-3
  9. [9] R. Fu, B. Zhang, H. Liang, S. Wang, Y. Wang, and Z. Li, “Gesture recognition of sEMG signal based on GASF-LDA feature enhancement and adaptive ABC optimized SVM,” Biomedical Signal Processing and Control, Vol.85, Article No.105104, 2023. https://doi.org/10.1016/j.bspc.2023.105104
  10. [10] R. Fuentes-Alvarez, J. H. Hernandez, I. Matehuala-Moran, M. Alfaro-Ponce, R. Lopez-Gutierrez, S. Salazar, and R. Lozano, “Assistive robotic exoskeleton using recurrent neural networks for decision taking for the robust trajectory tracking,” Expert Syst. Appl., Vol.193, Article No.116482, 2022. https://doi.org/10.1016/j.eswa.2021.116482
  11. [11] S. Ghimire, R. C. Deo, N. J. Downs, and N. Raj, “Self-adaptive differential evolutionary extreme learning machines for long-term solar radiation prediction with remotely-sensed MODIS satellite and reanalysis atmospheric products in solar-rich cities,” Remote Sens. Environ., Vol.212, pp. 176-198, 2018. https://doi.org/10.1016/j.rse.2018.05.003
  12. [12] S. Kumar, S. K. Pal, and R. Singh, “A novel hybrid model based on particle swarm optimisation and extreme learning machine for short-term temperature prediction using ambient sensors,” Sustain. Cities Soc., Vol.49, Article No.101601, 2019. https://doi.org/10.1016/j.scs.2019.101601
  13. [13] J. Ding, G. Chen, and K. Yuan, “Short-term wind power prediction based on improved grey wolf optimization algorithm for extreme learning machine,” Processes, Vol.8, No.1, Article No.109, 2020. https://doi.org/10.3390/pr8010109
  14. [14] N. Alamir, S. Kamel, T. F. Megahed, M. Hori, and S. M. Abdelkader, “Developing Hybrid Demand Response Technique for Energy Management in Microgrid Based on Pelican Optimization Algorithm,” Electr. Power Syst. Res., Vol.214, Article No.108905, 2023.
  15. [15] R. B. Azhiri, M. Esmaeili, and M. Nourani, “EMG-Based Feature Extraction and Classification for Prosthetic Hand Control,” arXiv:2107.00733, 2021. https://doi.org/10.48550/arXiv.2107.00733
  16. [16] P. Phukpattaranont, S. Thongpanja, K. Anam, A. Al-Jumaily, and C. Limsakul, “Evaluation of feature extraction techniques and classifiers for finger movement recognition using surface electromyography signal,” Med. Biol. Eng. Comput., Vol.56, pp. 2259-2271, 2018. https://doi.org/10.1007/s11517-018-1857-5
  17. [17] S. Zhang, W. Tan, Q. Wang, and N. Wang, “A new method of online extreme learning machine based on hybrid kernel function,” Neural Comput. Appl., Vol.31, pp. 4629-4638, 2019. https://doi.org/10.1007/s00521-018-3629-4
  18. [18] J. Li, C. Hai, Z. Feng, and G. Li, “A Transformer Fault Diagnosis Method Based on Parameters Optimization of Hybrid Kernel Extreme Learning Machine,” IEEE Access, Vol.9, pp. 891-902, 2021. https://doi.org/10.1109/ACCESS.2021.3112478
  19. [19] P. Trojovský and M. Dehghani, “Pelican Optimization Algorithm: A Novel Nature-Inspired Algorithm for Engineering Applications,” Sensors, Vol.22, No.3, Article No.855, 2022. https://doi.org/10.3390/s22030855
  20. [20] W. Tuerxun, C. Xu, M. Haderbieke, L. Guo, and Z. Cheng, “A Wind Turbine Fault Classification Model Using Broad Learning System Optimized by Improved Pelican Optimization Algorithm,” Machines, Vol.10, No.5, Article No.407, 2022. https://doi.org/10.3390/machines10050407
  21. [21] G. P. Mohammed, N. Alasmari, H. Alsolai, S. S. Alotaibi, N. Alotaibi, and H. Mohsen, “Autonomous Short-Term Traffic Flow Prediction Using Pelican Optimization with Hybrid Deep Belief Network in Smart Cities,” Appl. Sci., Vol.12, No.21, Article No.10828, 2022. https://doi.org/10.3390/app122110828
  22. [22] X. Zhou, J. Lu, J. Huang, M. Zhong, and M. Wang, “Enhancing artificial bee colony algorithm with multi-elite guidance,” Inf. Sci., Vol.543, pp. 242-258, 2021. https://doi.org/10.1016/j.ins.2020.07.037

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Feb. 07, 2025