Application of digital intellectualization technique in rehabilitation of spinal cord injury_West China Medical Journal

Authors：

CHENG Ruidong ^1,2 , ZHU Xinling ³ ,  YE Xiangming ^1,2

1. Center for Rehabilitation Medicine of Zhejiang, Department of Rehabilitation Medicine, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital, Hangzhou Medical College), Hangzhou, Zhejiang 310014, P. R. China;
2. Zhejiang Engineering Research Center for Digital-Intelligent Rehabilitation Equipment, Hangzhou, Zhejiang 310014, P. R. China;
3. Graduate School, Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310053, P. R. China;

Corresponding author：

YE Xiangming, Email: yexmdr@126.com

Keywords：

Spinal cord injury; digital intellectualization; exoskeleton robot; brain-computer interface; spinal cord neuromodulation

DOI：

10.7507/1002-0179.202403257

Video：

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With the breakthroughs of digitization, artificial intelligence and other technologies and the gradual expansion of application fields, more and more studies have been conducted on the application of digital intelligence technologies such as exoskeleton robots, brain-computer interface, and spinal cord neuromodulation to improve or compensate physical function after spinal cord injury (SCI) and improve self-care ability and quality of life of patients with SCI. The development of digital intelligent rehabilitation technology provides a new application platform for the functional reconstruction after SCI, and the digital and intelligentized rehabilitation technology has broad application prospects in the clinical rehabilitation treatment after SCI. This article elaborates on the current status of exoskeleton robots, brain-computer interface technology, and spinal cord neuromodulation technology for functional recovery after SCI.

Citation： CHENG Ruidong, ZHU Xinling, YE Xiangming. Application of digital intellectualization technique in rehabilitation of spinal cord injury. West China Medical Journal, 2024, 39(6): 851-855. doi: 10.7507/1002-0179.202403257 Copy

1.	Duan R, Qu M, Yuan Y, et al. Clinical benefit of rehabilitation training in spinal cord injury: a systematic review and meta-analysis. Spine (Phila Pa 1976), 2021, 46(6): E398-E410.
2.	Cardenas DD, Dalal K. Spinal cord injury rehabilitation. Phys Med Rehabil Clin N Am, 2014, 25(3): xv-xvi.
3.	刘小舟, 赖逸菲, 金子焯, 等. 脊髓损伤研究及治疗进展. 江西中医药, 2022, 53(11): 65-71.
4.	田祎, 姚东晓, 雷德强. 生物材料结合细胞移植治疗脊髓损伤的研究进展. 沈阳医学院学报, 2023, 25(1): 78-82.
5.	陈宣维. 基因工程及干细胞移植治疗脊髓损伤: 9352020Y0051. 2020-06-12.
6.	王艳军, 郑建中, 张爱莲, 等. 我国区域医疗信息化研究领域中研究热点与主流知识群的分析. 中华医学科研管理杂志, 2016, 29(2): 144-151.
7.	张传洋, 郭宇, 庞宇飞, 等. 数智化医疗信息利用与服务模式框架构建. 图书情报工作, 2023, 67(13): 49-58.
8.	傅苏颖. 数智化改变医药传统架构引领技术发展和产业升级. 中国证券报, 2023-09-29(A07).
9.	Zheng Y, Mao YR, Yuan TF, et al. Multimodal treatment for spinal cord injury: a sword of neuroregeneration upon neuromodulation. Neural Regen Res, 2020, 15(8): 1437-1450.
10.	杨彬, 陶广义, 杨顺, 等. 人工智能在脊髓神经损伤与修复领域研究热点的可视化分析. 中国组织工程研究, 2025, 29(4): 761-770.
11.	Kuroda Y, Young M, Shoman H, et al. Advanced rehabilitation technology in orthopaedics-a narrative review. Int Orthop, 2021, 45(8): 1933-1940.
12.	Masengo G, Zhang X, Dong R, et al. Lower limb exoskeleton robot and its cooperative control: a review, trends, and challenges for future research. Front Neurorobot, 2023, 16: 913748.
13.	Yıldırım MA, Öneş K, Gökşenoğlu G. Early term effects of robotic assisted gait training on ambulation and functional capacity in patients with spinal cord injury. Turk J Med Sci, 2019, 49(3): 838-843.
14.	Donisi L, Cesarelli G, Pisani N, et al. Wearable sensors and artificial intelligence for physical ergonomics: a systematic review of literature. Diagnostics (Basel), 2022, 12(12): 3048.
15.	Vélez-Guerrero MA, Callejas-Cuervo M, Mazzoleni S. Artificial intelligence-based wearable robotic exoskeletons for upper limb rehabilitation: a review. Sensors (Basel), 2021, 21(6): 2146.
16.	Mekki M, Delgado AD, Fry A, et al. Robotic rehabilitation and spinal cord injury: a narrative review. Neurotherapeutics, 2018, 15(3): 604-617.
17.	高峰, 杜良杰, 李建军. 脊髓损伤患者的下肢功能重建: 智能化康复手段. 中国康复理论与实践, 2008, 14(9): 845-846.
18.	徐发树. 截瘫助行外骨骼机器人安全性研究. 成都: 电子科技大学, 2022.
19.	Maeshima S, Osawa A, Nishio D, et al. Efficacy of a hybrid assistive limb in post-stroke hemiplegic patients: a preliminary report. BMC Neurol, 2011, 11: 116.
20.	Kerdraon J, Previnaire JG, Tucker M, et al. Evaluation of safety and performance of the self balancing walking system Atalante in patients with complete motor spinal cord injury. Spinal Cord Ser Cases, 2021, 7(1): 71.
21.	Esquenazi A, Talaty M, Packel A, et al. The ReWalk powered exoskeleton to restore ambulatory function to individuals with thoracic-level motor-complete spinal cord injury. Am J Phys Med Rehabil, 2012, 91(11): 911-921.
22.	Farris R, Quintero H, Withrow TJ, et al. Design and simulation of a joint-coupled orthosis for regulating FES-aided gait. IEEE Int Conf Robot Autom, 2009: 12-17.
23.	Yang W, Zhang J, Zhang S, et al. Lower limb exoskeleton gait planning based on crutch and human-machine foot combined center of pressure. Sensors (Basel), 2020, 20(24): 7216.
24.	Wang H, Yang C, Yang W, et al. A rehabilitation gait for the balance of human and lower extremity exoskeleton system based on the transfer of gravity center. Ind Robot, 2019, 46(3): 150.
25.	Chen Q, Cheng H, Yue C, et al. Dynamic balance gait for walking assistance exoskeleton. Appl Bionics Biomech, 2018, 2018: 7847014.
26.	Veneman JF, Kruidhof R, Hekman EE, et al. Design and evaluation of the LOPES exoskeleton robot for interactive gait rehabilitation. IEEE Trans Neural Syst Rehabil Eng, 2007, 15(3): 379-386.
27.	Wang S, Wang L, Meijneke C, et al. Design and control of the MINDWALKER exoskeleton. IEEE Trans Neural Syst Rehabil Eng, 2015, 23(2): 277-286.
28.	Louie DR, Eng JJ, Lam T, et al. Gait speed using powered robotic exoskeletons after spinal cord injury: a systematic review and correlational study. J Neuroeng Rehabil, 2015, 12: 82.
29.	Baunsgaard CB, Nissen UV, Brust AK, et al. Exoskeleton gait training after spinal cord injury: an exploratory study on secondary health conditions. J Rehabil Med, 2018, 50(9): 806-813.
30.	Zariffa J, Kapadia N, Kramer JL, et al. Effect of a robotic rehabilitation device on upper limb function in a sub-acute cervical spinal cord injury population. IEEE Int Conf Rehabil Robot, 2011, 2011: 5975400.
31.	Swank C, Holden A, McDonald L, et al. Foundational ingredients of robotic gait training for people with incomplete spinal cord injury during inpatient rehabilitation (FIRST): a randomized controlled trial protocol. PLoS One, 2022, 17(5): e0267013.
32.	Topini A, Sansom W, Secciani N, et al. Variable admittance control of a hand exoskeleton for virtual reality-based rehabilitation tasks. Front Neurorobot, 2022, 15: 789743.
33.	Shi Y, Dong W, Lin W, et al. Soft wearable robots: development status and technical challenges. Sensors (Basel), 2022, 22(19): 7584.
34.	Samejima S, Khorasani A, Ranganathan V, et al. Brain-computer-spinal interface restores upper limb function after spinal cord injury. IEEE Trans Neural Syst Rehabil Eng, 2021, 29: 1233-1242.
35.	龚瑜, 蔺俊斌, 郝赤子, 等. 脑机接口在脊髓损伤康复中的应用进展. 中国康复医学杂志, 2020, 35(6): 744-748.
36.	殷祥志, 赵海波, 唐一杰, 等. 脑-机接口技术在脊髓损伤后运动功能改善中应用的研究进展. 中华创伤杂志, 2023, 39(3): 271-276.
37.	Bockbrader M, Annetta N, Friedenberg D, et al. Clinically significant gains in skillful grasp coordination by an individual with tetraplegia using an implanted brain-computer interface with forearm transcutaneous muscle stimulation. Arch Phys Med Rehabil, 2019, 100(7): 1201-1217.
38.	Davis KC, Meschede-Krasa B, Cajigas I, et al. Design-development of an at-home modular brain-computer interface (BCI) platform in a case study of cervical spinal cord injury. J Neuroeng Rehabil, 2022, 19(1): 53.
39.	Ferrero L, Quiles V, Ortiz M, et al. Assessing user experience with BMI-assisted exoskeleton in patients with spinal cord injury. Annu Int Conf IEEE Eng Med Biol Soc, 2022, 2022: 4064-4067.
40.	Ferrero L, Quiles V, Ortiz M, et al. Brain-computer interface enhanced by virtual reality training for controlling a lower limb exoskeleton. iScience, 2023, 26(5): 106675.
41.	Lobel DA, Lee KH. Brain machine interface and limb reanimation technologies: restoring function after spinal cord injury through development of a bypass system. Mayo Clin Proc, 2014, 89(5): 708-714.
42.	Do AH, Wang PT, King CE, et al. Brain-computer interface controlled robotic gait orthosis. J Neuroeng Rehabil, 2013, 10: 111.
43.	Rajasekaran V, López-Larraz E, Trincado-Alonso F, et al. Volition-adaptive control for gait training using wearable exoskeleton: preliminary tests with incomplete spinal cord injury individuals. J Neuroeng Rehabil, 2018, 15(1): 4.
44.	Vučković A, Wallace L, Allan DB. Hybrid brain-computer interface and functional electrical stimulation for sensorimotor training in participants with tetraplegia: a proof-of-concept study. J Neurol Phys Ther, 2015, 39(1): 3-14.
45.	Collinger JL, Wodlinger B, Downey JE, et al. High-performance neuroprosthetic control by an individual with tetraplegia. Lancet, 2013, 381(9866): 557-564.
46.	Cajigas I, Vedantam A. Brain-computer interface, neuromodulation, and neurorehabilitation strategies for spinal cord injury. Neurosurg Clin N Am, 2021, 32(3): 407-417.
47.	Pizzolato C, Gunduz MA, Palipana D, et al. Non-invasive approaches to functional recovery after spinal cord injury: therapeutic targets and multimodal device interventions. Exp Neurol, 2021, 339: 113612.
48.	Wagner FB, Mignardot JB, Le Goff-Mignardot CG, et al. Targeted neurotechnology restores walking in humans with spinal cord injury. Nature, 2018, 563(7729): 65-71.
49.	Rowald A, Komi S, Demesmaeker R, et al. Activity-dependent spinal cord neuromodulation rapidly restores trunk and leg motor functions after complete paralysis. Nat Med, 2022, 28(2): 260-271.
50.	Kathe C, Skinnider MA, Hutson TH, et al. The neurons that restore walking after paralysis. Nature, 2022, 611(7936): 540-547.
51.	Lorach H, Galvez A, Spagnolo V, et al. Walking naturally after spinal cord injury using a brain-spine interface. Nature, 2023, 618(7963): 126-133.
52.	于贝贝, 陆洋, 王劲. 脊髓电刺激重建咳嗽反射的应用进展. 中国微侵袭神经外科杂志, 2021, 26(3): 141-144.

1. Duan R, Qu M, Yuan Y, et al. Clinical benefit of rehabilitation training in spinal cord injury: a systematic review and meta-analysis. Spine (Phila Pa 1976), 2021, 46(6): E398-E410.
2. Cardenas DD, Dalal K. Spinal cord injury rehabilitation. Phys Med Rehabil Clin N Am, 2014, 25(3): xv-xvi.
3. 刘小舟, 赖逸菲, 金子焯, 等. 脊髓损伤研究及治疗进展. 江西中医药, 2022, 53(11): 65-71.
4. 田祎, 姚东晓, 雷德强. 生物材料结合细胞移植治疗脊髓损伤的研究进展. 沈阳医学院学报, 2023, 25(1): 78-82.
5. 陈宣维. 基因工程及干细胞移植治疗脊髓损伤: 9352020Y0051. 2020-06-12.
6. 王艳军, 郑建中, 张爱莲, 等. 我国区域医疗信息化研究领域中研究热点与主流知识群的分析. 中华医学科研管理杂志, 2016, 29(2): 144-151.
7. 张传洋, 郭宇, 庞宇飞, 等. 数智化医疗信息利用与服务模式框架构建. 图书情报工作, 2023, 67(13): 49-58.
8. 傅苏颖. 数智化改变医药传统架构引领技术发展和产业升级. 中国证券报, 2023-09-29(A07).
9. Zheng Y, Mao YR, Yuan TF, et al. Multimodal treatment for spinal cord injury: a sword of neuroregeneration upon neuromodulation. Neural Regen Res, 2020, 15(8): 1437-1450.
10. 杨彬, 陶广义, 杨顺, 等. 人工智能在脊髓神经损伤与修复领域研究热点的可视化分析. 中国组织工程研究, 2025, 29(4): 761-770.
11. Kuroda Y, Young M, Shoman H, et al. Advanced rehabilitation technology in orthopaedics-a narrative review. Int Orthop, 2021, 45(8): 1933-1940.
12. Masengo G, Zhang X, Dong R, et al. Lower limb exoskeleton robot and its cooperative control: a review, trends, and challenges for future research. Front Neurorobot, 2023, 16: 913748.
13. Yıldırım MA, Öneş K, Gökşenoğlu G. Early term effects of robotic assisted gait training on ambulation and functional capacity in patients with spinal cord injury. Turk J Med Sci, 2019, 49(3): 838-843.
14. Donisi L, Cesarelli G, Pisani N, et al. Wearable sensors and artificial intelligence for physical ergonomics: a systematic review of literature. Diagnostics (Basel), 2022, 12(12): 3048.
15. Vélez-Guerrero MA, Callejas-Cuervo M, Mazzoleni S. Artificial intelligence-based wearable robotic exoskeletons for upper limb rehabilitation: a review. Sensors (Basel), 2021, 21(6): 2146.
16. Mekki M, Delgado AD, Fry A, et al. Robotic rehabilitation and spinal cord injury: a narrative review. Neurotherapeutics, 2018, 15(3): 604-617.
17. 高峰, 杜良杰, 李建军. 脊髓损伤患者的下肢功能重建: 智能化康复手段. 中国康复理论与实践, 2008, 14(9): 845-846.
18. 徐发树. 截瘫助行外骨骼机器人安全性研究. 成都: 电子科技大学, 2022.
19. Maeshima S, Osawa A, Nishio D, et al. Efficacy of a hybrid assistive limb in post-stroke hemiplegic patients: a preliminary report. BMC Neurol, 2011, 11: 116.
20. Kerdraon J, Previnaire JG, Tucker M, et al. Evaluation of safety and performance of the self balancing walking system Atalante in patients with complete motor spinal cord injury. Spinal Cord Ser Cases, 2021, 7(1): 71.
21. Esquenazi A, Talaty M, Packel A, et al. The ReWalk powered exoskeleton to restore ambulatory function to individuals with thoracic-level motor-complete spinal cord injury. Am J Phys Med Rehabil, 2012, 91(11): 911-921.
22. Farris R, Quintero H, Withrow TJ, et al. Design and simulation of a joint-coupled orthosis for regulating FES-aided gait. IEEE Int Conf Robot Autom, 2009: 12-17.
23. Yang W, Zhang J, Zhang S, et al. Lower limb exoskeleton gait planning based on crutch and human-machine foot combined center of pressure. Sensors (Basel), 2020, 20(24): 7216.
24. Wang H, Yang C, Yang W, et al. A rehabilitation gait for the balance of human and lower extremity exoskeleton system based on the transfer of gravity center. Ind Robot, 2019, 46(3): 150.
25. Chen Q, Cheng H, Yue C, et al. Dynamic balance gait for walking assistance exoskeleton. Appl Bionics Biomech, 2018, 2018: 7847014.
26. Veneman JF, Kruidhof R, Hekman EE, et al. Design and evaluation of the LOPES exoskeleton robot for interactive gait rehabilitation. IEEE Trans Neural Syst Rehabil Eng, 2007, 15(3): 379-386.
27. Wang S, Wang L, Meijneke C, et al. Design and control of the MINDWALKER exoskeleton. IEEE Trans Neural Syst Rehabil Eng, 2015, 23(2): 277-286.
28. Louie DR, Eng JJ, Lam T, et al. Gait speed using powered robotic exoskeletons after spinal cord injury: a systematic review and correlational study. J Neuroeng Rehabil, 2015, 12: 82.
29. Baunsgaard CB, Nissen UV, Brust AK, et al. Exoskeleton gait training after spinal cord injury: an exploratory study on secondary health conditions. J Rehabil Med, 2018, 50(9): 806-813.
30. Zariffa J, Kapadia N, Kramer JL, et al. Effect of a robotic rehabilitation device on upper limb function in a sub-acute cervical spinal cord injury population. IEEE Int Conf Rehabil Robot, 2011, 2011: 5975400.
31. Swank C, Holden A, McDonald L, et al. Foundational ingredients of robotic gait training for people with incomplete spinal cord injury during inpatient rehabilitation (FIRST): a randomized controlled trial protocol. PLoS One, 2022, 17(5): e0267013.
32. Topini A, Sansom W, Secciani N, et al. Variable admittance control of a hand exoskeleton for virtual reality-based rehabilitation tasks. Front Neurorobot, 2022, 15: 789743.
33. Shi Y, Dong W, Lin W, et al. Soft wearable robots: development status and technical challenges. Sensors (Basel), 2022, 22(19): 7584.
34. Samejima S, Khorasani A, Ranganathan V, et al. Brain-computer-spinal interface restores upper limb function after spinal cord injury. IEEE Trans Neural Syst Rehabil Eng, 2021, 29: 1233-1242.
35. 龚瑜, 蔺俊斌, 郝赤子, 等. 脑机接口在脊髓损伤康复中的应用进展. 中国康复医学杂志, 2020, 35(6): 744-748.
36. 殷祥志, 赵海波, 唐一杰, 等. 脑-机接口技术在脊髓损伤后运动功能改善中应用的研究进展. 中华创伤杂志, 2023, 39(3): 271-276.
37. Bockbrader M, Annetta N, Friedenberg D, et al. Clinically significant gains in skillful grasp coordination by an individual with tetraplegia using an implanted brain-computer interface with forearm transcutaneous muscle stimulation. Arch Phys Med Rehabil, 2019, 100(7): 1201-1217.
38. Davis KC, Meschede-Krasa B, Cajigas I, et al. Design-development of an at-home modular brain-computer interface (BCI) platform in a case study of cervical spinal cord injury. J Neuroeng Rehabil, 2022, 19(1): 53.
39. Ferrero L, Quiles V, Ortiz M, et al. Assessing user experience with BMI-assisted exoskeleton in patients with spinal cord injury. Annu Int Conf IEEE Eng Med Biol Soc, 2022, 2022: 4064-4067.
40. Ferrero L, Quiles V, Ortiz M, et al. Brain-computer interface enhanced by virtual reality training for controlling a lower limb exoskeleton. iScience, 2023, 26(5): 106675.
41. Lobel DA, Lee KH. Brain machine interface and limb reanimation technologies: restoring function after spinal cord injury through development of a bypass system. Mayo Clin Proc, 2014, 89(5): 708-714.
42. Do AH, Wang PT, King CE, et al. Brain-computer interface controlled robotic gait orthosis. J Neuroeng Rehabil, 2013, 10: 111.
43. Rajasekaran V, López-Larraz E, Trincado-Alonso F, et al. Volition-adaptive control for gait training using wearable exoskeleton: preliminary tests with incomplete spinal cord injury individuals. J Neuroeng Rehabil, 2018, 15(1): 4.
44. Vučković A, Wallace L, Allan DB. Hybrid brain-computer interface and functional electrical stimulation for sensorimotor training in participants with tetraplegia: a proof-of-concept study. J Neurol Phys Ther, 2015, 39(1): 3-14.
45. Collinger JL, Wodlinger B, Downey JE, et al. High-performance neuroprosthetic control by an individual with tetraplegia. Lancet, 2013, 381(9866): 557-564.
46. Cajigas I, Vedantam A. Brain-computer interface, neuromodulation, and neurorehabilitation strategies for spinal cord injury. Neurosurg Clin N Am, 2021, 32(3): 407-417.
47. Pizzolato C, Gunduz MA, Palipana D, et al. Non-invasive approaches to functional recovery after spinal cord injury: therapeutic targets and multimodal device interventions. Exp Neurol, 2021, 339: 113612.
48. Wagner FB, Mignardot JB, Le Goff-Mignardot CG, et al. Targeted neurotechnology restores walking in humans with spinal cord injury. Nature, 2018, 563(7729): 65-71.
49. Rowald A, Komi S, Demesmaeker R, et al. Activity-dependent spinal cord neuromodulation rapidly restores trunk and leg motor functions after complete paralysis. Nat Med, 2022, 28(2): 260-271.
50. Kathe C, Skinnider MA, Hutson TH, et al. The neurons that restore walking after paralysis. Nature, 2022, 611(7936): 540-547.
51. Lorach H, Galvez A, Spagnolo V, et al. Walking naturally after spinal cord injury using a brain-spine interface. Nature, 2023, 618(7963): 126-133.
52. 于贝贝, 陆洋, 王劲. 脊髓电刺激重建咳嗽反射的应用进展. 中国微侵袭神经外科杂志, 2021, 26(3): 141-144.

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