Digital twin technology in biomechanics: Revolutionizing human movement analysis and rehabilitation practices
Abstract
Lower limb rehabilitation exoskeletons are wearable assistive rehabilitation devices designed to protect and aid patients in rehabilitation training. However, traditional lower limb rehabilitation exoskeleton systems are limited by information acquisition technology, mostly adopting passive training with fixed trajectories and lacking real-time motion data interaction, resulting in deficiencies in the overall system’s safety and autonomy. Based on this, this study proposes a lower limb rehabilitation exoskeleton system based on digital twin technology. By leveraging digital twin technology, the system achieves a deep integration of virtual and physical spaces, improves human-machine information interaction technology, and enhances the effectiveness of rehabilitation training. Experimental results demonstrate that the system can achieve personalized gait trajectory planning and real-time motion data interaction, providing a new solution for lower limb rehabilitation.
References
1. Guo H, Zhou X, Du Q. Research progress on the improvement of walking ability in children with cerebral palsy using lower limb rehabilitation robots. China Rehabilitation Medicine. 2021; 36(6): 6–9.
2. Lei J, Tang J. Current status and application of lower limb rehabilitation robots in improving walking ability for patients with spinal cord injury. Medical Information. 2022; 35(19): 159–62.
3. Viteckova S, Kutilek P, De Boisboissel G, et al. Empowering lower limbs exoskeletons: State-of-the-art. Robotica. 2018; 36(11): 1743–56.
4. Li WZ, Cao GZ, Zhu AB. Review on control strategies for lower limb rehabilitation exoskeletons. IEEE Access. 2021; 9: 40–60.
5. Liang X, Wang W, Hou Z, et al. Human-machine interaction control methods for rehabilitation robots. Science China: Information Sciences. 2018; 48(1): 23.
6. Kwon SH, Lee BS, Lee HJ, et al. Energy efficiency and patient satisfaction of gait with knee-ankle-foot orthosis and robot (ReWalk)-assisted gait in patients with spinal cord injury. Annals of Rehabilitation Medicine. 2020; 44(2): 31–41.
7. Zheng Y, Jing X, Li G. Application of human-machine intelligent collaboration in the field of medical rehabilitation robots. Big Data Times. 2018.
8. Fong J, Küçüktabak EB, Crocher V, et al. CANopen Robot Controller (CORC): An open software stack for human-robot interaction development. In: Wearable Robotics: Challenges and Trends. Springer Publishing; 2021. pp. 287–292.
9. Yan H, Wang H, Chen P, et al. Optimal design of upper limb rehabilitation robot with generalized shoulder joint. Acta Armamentarii. 2021; 42(11): 2491–2502.
10. Liu X, Yang M, Wang M, et al. Application of multi-position intelligent lower limb rehabilitation robot in rehabilitation training of stroke patients. Biomedical Engineering and Clinical Medicine. 2018; 22(3): 5.
11. Wang W, Song W, Qu S. Research and progress of upper limb rehabilitation robots in the rehabilitation of complications in stroke patients. Massage & Rehabilitation Medicine. 2018; 9(18): 3.
12. Luo S, Luo S, Lu Y, et al. Research on adaptive gait control method for walking-aid rehabilitation robots. Machinery Design & Manufacture. 2024; 6: 362–366.
13. Maranesi E, Riccardi GR, Di Donna V, et al. Effectiveness of intervention based on end-effector gait trainer in older patients with stroke: A systematic review. Journal of the American Medical Directors Association. 2020; 21(8): 36–44.
14. Ding H, Yang L, Yang Z, et al. Health status prediction of shearer driven by the fusion of digital twinning and deep learning. China Mechanical Engineering. 2020; 31 (7): 15-23.
15. Gao Y, Chen C, Zhang Y. Digital twin city: Mainstream mode of smart city construction [J]. China Construction Informatization. 2019; No.100 (21): 8-12.
16. Li J, Zhu L, Gou X. Kinematic analysis of lower limb rehabilitation exoskeleton mechanism based on human-machine closed chain. Journal of Engineering Design. 2019; 26 (1): 65-72,109.
17. Jiang S, Wang Z, Xia S, et al. Construction method of digital twin geometry model of aircraft flexible tooling [J]. Aviation Manufacturing Technology. 2022; 65(12): 86-91,111.
18. Jiang Y, Dong J. Application effect of lower limb rehabilitation robot training in rehabilitation treatment of patients with acute strok. Health World. 2021; 6: 197.
19. Hua Y, Zhu M, Zhang Y. Current application status and development trend of pediatric rehabilitation robots. Chinese Journal of Rehabilitation Theory and Practice. 2018; 24(6): 4.
20. Chen W, Wang L, Zhang L, et al. Dynamic analysis and simulation of lower limb exoskeleton rehabilitation robot. Machinery Design. 2018; 35(4): 7.
21. Huang J, Yuan M, Wang S, et al. Structural design and simulation analysis of lower limb rehabilitation robot. Mechanical & Electrical Engineering Magazine. 2018; 35(2): 5.
22. Lv X, Yang C, Jiang F, et al. Passive training control of sit-and-lie lower limb rehabilitation robot. Machinery Design & Manufacture. 2019; 4: 244–247.
23. Sun Qi, Xie J. Effect of upper limb rehabilitation robot on upper limb dysfunction in stroke patients. Chinese Journal of Physical Medicine and Rehabilitation. 2023; 9: 45.
24. Li Y, Zeng Q, Huang G. Clinical application progress of upper limb rehabilitation robots in stroke. Chinese Journal of Rehabilitation Theory and Practice. 2020; 26(3): 5.
25. Shen Z, Zhang L, Su Y, et al. Autonomous adaptive control strategy based on ankle rehabilitation robot. Chinese Journal of Medical Instrumentation/Zhongguo Yiliao Qixie Zazhi. 2024; 48(4).
Copyright (c) 2025 Author(s)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright on all articles published in this journal is retained by the author(s), while the author(s) grant the publisher as the original publisher to publish the article.
Articles published in this journal are licensed under a Creative Commons Attribution 4.0 International, which means they can be shared, adapted and distributed provided that the original published version is cited.
Submit a Paper
