EDP Sciences logo
Submit your paper
Search
Menu
Home All issues Volume 17 (2026) Int. J. Simul. Multidisci. Des. Optim., 17 (2026) 1 References
Open Access
Issue
Int. J. Simul. Multidisci. Des. Optim.
Volume 17, 2026
Article Number 1
Number of page(s) 14
DOI https://doi.org/10.1051/smdo/2025034
Published online 03 February 2026
  1. H. Cheng, R. Huang, J. Qiu et al., Rehabilitation robots and their clinical applications: a review, Robot, 43, 606–619 (2021) [Google Scholar]
  2. T. Mao, Y. Li, M. Li et al., Survey on deep learning-based lesion segmentation and detection in acute ischemic stroke, Comput. Syst. Appl. 34, 11–25 (2025) [Google Scholar]
  3. S. Luo, L. Meng, H. Yu, An overview of the research and application of rehabilitation robot technology in China, China Acad. J. Electron. Publ. House 38, 1762–1768 (2023) [Google Scholar]
  4. C. He, C. Xiong, W. Chen. Brain injury upper limb rehabilitation robot and its clinical application research, J. Mech. Eng. 59, 65–80 (2023) [Google Scholar]
  5. L. Gao, D. Zhang, X. Zhao, Upper limb rehabilitation robot direct teaching technology adapted to individual patient differences, Chin. J. Rehabil. Theory Pract. 28, 1231–1240 (2022) [Google Scholar]
  6. L. Li, R. Zhang, G. Cheng et al., Trajectory tracking control of upper limb rehabilitation robot based on optimal discrete sliding mode control, Meas. Control 56, 1142–1155 (2023) [Google Scholar]
  7. S. Guo, Y. Song, X. Wang et al., Learning and transfer method of active rehabilitation strategy for upper limb rehabilitation robot, Robot 46, 562–575 (2024) [Google Scholar]
  8. J. Zhang, X. Fu, Y. Wang et al., Task-oriented training based on activities of daily living analysis for stroke patients, Chin. J. Phys. Med. Rehabil. 44, 595–599 (2022) [Google Scholar]
  9. Y. Yao, S. Pei, J. Guo et al., Upper limb rehabilitation robot research review, J. Mech. Eng. 60, 115–134 (2024) [Google Scholar]
  10. T. Terama, T. Matsubara, T. Noda et al., Quaternion-based trajectory optimization of human postures for inducing target muscle activation patterns, IEEE Robot. Autom. Lett. 5, 6607–6614 (2020) [Google Scholar]
  11. D. Xu, Z. Wang. Trajectory planning and optimization research of upper limb rehabilitation robot based on 5th quasi-uniform B-spline, Indust. Control Comput. 36, 59–60+63 (2023) [Google Scholar]
  12. N.B. Mohamadwasel, S. Kurnaz, Implementation of the parallel robot using FOPID with fuzzy type-2 in use social spider optimization algorithm, Appl. Nanosci. 13, (2023). https://doi.org/10.1007/s13204-021-02034-9 [Google Scholar]
  13. N. Basil, A.F. Mohammed, B.M. Sabbar et al., Performance analysis of hybrid optimization approach for UAV path planning control using FOPID-TID controller and HAOAROA algorithm, Sci. Rep. 4840, (2025). https://doi.org/10.1038/s41598-025-86803-4 [Google Scholar]
  14. N. Basil, H.M. Marhoon, D.F. Sahib et al., Accelerated black hole optimization algorithm with enhanced FOPID controller for omni-wheel drive mobile robot system, Neural Comput. Appl. 37, 16983–17014 (2025) [Google Scholar]
  15. G. Li, Q. Fang, T. Xu et al., Inverse kinematic analysis and trajectory planning of a modular upper limb rehabilitation exoskeleton, Technol. Health Care 27, 123–132 (2019) [Google Scholar]
  16. A. Lei, Y. Chen, Y. Xu, Human-like motion planning method for robot arm based on reinforcement learning, Chin. J. Sci. Instrum. 42, 136–145 (2021) [Google Scholar]
  17. Y. Wei, W. Jiang, A. Rahmani et al., Motion planning for a humanoid mobile manipulator system, Int. J. Hum. Robot. 16, 1950006 (2019) [Google Scholar]
  18. N.F. Durate, M. Raković, J. Santos-Victor, Biologically inspired controller of human action behaviour for a humanoid robot in a dyadic scenario, in: IEEE EUROCON 2019-18th International Conference on Smart Technologies, 2019, pp. 1–6 [Google Scholar]
  19. Y. Zhu, X. Tong, R. Yang et al., A survey on modeling mechanism and control strategy of rehabilitation robots: recent trends, current challenges, and future developments, Int. J. Control Autom. Syst. 20, 2724–2748 (2022) [Google Scholar]
  20. H. Zhang, T. Zhang, Research progress in stroke rehabilitation techniques, Chin. J. Rehabil. 37, 371–375 (2022) [Google Scholar]
  21. N. Mangalabarathi, B. Devi, K Chinnathambi et al., Effectiveness of nurse-led stroke rehabilitation on awareness, activities of daily living and coping in stroke patients at a tertiary care hospital in India, Cureus, e72843 (2024). https://doi.org/10.7759/cureus.72843 [Google Scholar]
  22. R. Dizor, A. Raj, B. Gonzalez et al., Deep reinforcement learning to assess lower extremity movement intention and assist a rehabilitation exoskeleton, in: Proc. SPIE 13058, Disruptive Technologies in Information Sciences VIII, 1305805 (6 June 2024). https://doi.org/10.1117/12.3013039 [Google Scholar]
  23. Y. Liu, Z. Chen, Y. Li et al., Robot search path planning method based on prioritized deep reinforcement learning, Control Autom. Syst. 20, 2669–2680 (2022) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.