智能康复机器人在脑卒中患者中的应用进展及护理启示

doi:10.3761/j.issn.0254-1769.2026.15.005

摘要/Abstract

摘要：

智能康复机器人能够有效提升脑卒中患者的康复依从性，改善其功能结局，是脑卒中康复领域的重要创新方向，在脑卒中患者康复训练中展现出独特优势。该文综述了智能控制技术机器人、脑机接口驱动的康复机器人、多模态融合机器人及虚拟现实与增强现实技术融合的机器人应用于脑卒中患者的康复训练方法、应用效果，指出当前智能康复机器人在应用上存在技术适老化设计不足、临床实效验证不充分、多学科协作欠缺及医保政策滞后等挑战，并针对性提出应用发展建议与护理启示，以推动智能康复机器人在脑卒中康复中的规范化与可持续应用。

关键词: 智能机器人, 脑卒中, 康复护理, 综述

Abstract:

Intelligent rehabilitation robots can effectively enhance rehabilitation compliance and improve functional outcomes in stroke patients，representing an important innovative direction in the field of stroke rehabilitation and demonstrating unique advantages in stroke rehabilitation training. This article reviews the rehabilitation training methods and application effects of intelligent rehabilitation robots in stroke patients，including intelligent control technology robots，brain-computer interface-driven rehabilitation robots，multimodal fusion robots，and virtual reality and augmented reality technology-integrated rehabilitation robots. It identifies current challenges in the application of intelligent rehabilitation robots，such as insufficient age-friendly design，inadequate clinical efficacy validation，lack of multidisciplinary collaboration，and lagging healthcare insurance policies. Corresponding recommendations for application development and nursing implications are proposed to promote the standardized and sustainable application of intelligent rehabilitation robots in stroke rehabilitation.

Key words: Intelligent Robots, Stroke, Rehabilitation Nursing, Review

杨佳圆, 李静, 蒋华. 智能康复机器人在脑卒中患者中的应用进展及护理启示[J]. 中华护理杂志, 2026, 61(15): 2048-2053.

YANG Jiayuan, LI Jing, JIANG Hua. Application progress of intelligent rehabilitation robots in stroke patients and nursing implication[J]. Chinese Journal of Nursing, 2026, 61(15): 2048-2053.

参考文献 35

[1]	Wu SM, Wang YN, Liu JF, et al. Stroke in China:advances in prevention and management on the path to a healthy China[J]. Lancet Neurol, 2026, 25(4):379-395.
[2]	朱金凤, 蔡卫新, 陶子荣, 等. 中青年出血性脑卒中患者康复运动依从性现状及其影响因素分析[J]. 中华护理杂志, 2023, 58(5):580-586.
	Zhu JF, Cai WX, Tao ZR, et al. Analysis of rehabilitation exercise compliance and its influencing factors in young and middle-aged patients with hemorrhagic stroke[J]. Chin J Nurs, 2023, 58(5):580-586.
[3]	Zapata M, Guevara D, Obregón J, et al. Advancements in assistive robotics:a systematic review of inclusive technologies for people with disabilities[J]. IEEE Access, 2025, 13:171871-171888.
[4]	Banyai AD, Brişan C. Robotics in physical rehabilitation：systematic review[J]. Healthcare, 2024, 12(17):1720.
[5]	秦岩丁, 孟京艳, 于宁波, 等. 上肢康复外骨骼机器人关键技术研究综述与展望[J]. 机器人, 2026, 48(2):375-394.
	Qin YD, Meng JY, Yu NB, et al. Review and prospect on key technologies of upper-limb rehabilitation exoskeleton robots[J]. Robot, 2026, 48(2):375-394.
[6]	王晓锋, 胡梦巧, 汪嫣, 等. 外骨骼机器人辅助步态训练对脑卒中和脊髓损伤下肢功能康复效果的系统综述[J]. 中国康复理论与实践, 2025, 31(8):914-921.
	Wang XF, Hu MQ, Wang Y, et al. Effect of exoskeleton robot-assisted gait training on lower limb function after stroke and spinal cord injury:a systematic review[J]. Chin J Rehabil Theory Pract, 2025, 31(8):914-921.
[7]	Rad OL, Brisan C. Control algorithms in robot-assisted rehabilitation:a systematic review[J]. Appl Sci, 2025, 15(16):9184.
[8]	Rustamzadeh O, Ali Hosseini S, Tanha RR, et al. Robotic rehabilitation and intelligent algorithms improving the performance skills of stroke patients:a scoping review[J]. J Bodyw Mov Ther, 2026, 46:308-331.
[9]	Nicora G, Pe S, Santangelo G, et al. Systematic review of AI/ML applications in multi-domain robotic rehabilitation:trends,gaps,and future directions[J]. J Neuro Engineering Rehabil, 2025, 22(1):79.
[10]	Zhang LS, Tian X, Fan YQ, et al. Development and efficacy assessment of an angle sensor-integrated upper limb exoskeleton system for autonomous rehabilitation training[J]. Sensors, 2025, 25(13):3984.
[11]	Choi J, Kim H, Choi J, et al. EMG-based gait speed control of a lower-limb exoskeleton for gait rehabilitation:a proof-of-concept study with post-stroke chronic hemiparetic patients[J]. Int J Precis Eng Manuf, 2025, 26(11):3059-3067.
[12]	张霖, 郑雅兴, 熊桃, 等. 非侵入式脑机接口技术在脑卒中患者上肢功能康复中应用的范围综述及护理启示[J]. 中华护理杂志, 2025, 60(13):1658-1664.
	Zhang L, Zheng YX, Xiong T, et al. A scoping review of application of non-invasive brain computer interface technology in upper limb functional rehabilitation of stroke patients and nursing implications[J]. Chin J Nurs, 2025, 60(13):1658-1664.
[13]	Chand T, Verma K, Gupta R, et al. Assistive and rehabilitative brain-computer interfaces:innovations in patient care[M]//Design and control of rehabilitation robots. Cham: Springer Nature Switzerland,2025:1-32.
[14]	赵东辉, 鲍思宇, 牛春晖, 等. 兼具多种理疗手法和穴位识别能力的多模态交互理疗机器人[J]. 机器人, 2026, 48(2):284-297,432.
	Zhao DH, Bao SY, Niu CH, et al. A multi-modal interactive physiotherapy robot with multiple physiotherapy operations and acupoint recognition[J]. Robot, 2026, 48(2):284-297,432.
[15]	Sheng B, Li SY, Chen XH, et al. Multimodal assessment of upper limb rehabilitation in stroke patients based on cross-attention mechanism[J]. Appl Soft Comput, 2026, 197:115199.
[16]	王庆雷, 眭有昕, 朱兰, 等. 虚拟现实上肢康复机器人对脑卒中偏瘫患者脑网络的影响[J]. 中国康复医学杂志, 2025, 40(7):995-1001.
	Wang QL, Sui YX, Zhu L, et al. Effects of virtual reality upper limb rehabilitation robot on brain network in hemiplegic patients with stroke[J]. Chin J Rehabil Med, 2025, 40(7):995-1001.
[17]	Yu Y, Huang WH, Tuerxun H, et al. Enhanced neuroplasticity and gait recovery in stroke patients:a comparative analysis of active and passive robotic training modes[J]. BMC Neurol, 2025, 25(1):239.
[18]	潘慧璇, 顾志娥, 张恒柱, 等. 基于自我决定理论的脑卒中患者康复动机影响因素的混合研究系统评价[J]. 军事护理, 2025, 42(12):91-95.
	Pan HX, Gu ZE, Zhang HZ, et al. A systematic review of mixed studies in factors influencing rehabilitation motivation in stroke patients based on self-determination theory[J]. Mil Nurs, 2025, 42(12):91-95.
[19]	Wang GX, Zhang Q, Wang LM, et al. The impact of teachers’ autonomy support on students’ academic performance from the perspective of hyperscanning:the mediating role of autonomy need satisfaction[J]. Soc Cogn Affect Neurosci, 2025, 20(1):nsaf115.
[20]	Arntz A, Weber F, Handgraaf M, et al. Technologies in home-based digital rehabilitation:scoping review[J]. JMIR Rehabil Assist Technol, 2023, 10:e43615.
[21]	Quan W, Liu SK, Cao M, et al. A comprehensive review of virtual reality technology for cognitive rehabilitation in patients with neurological conditions[J]. Appl Sci, 2024, 14(14):6285.
[22]	Li H, Guo SX, He RJ, et al. A home-based dual-mode upper limb rehabilitation system:teleoperation mode and bilateral mode with sEMG and IMU[J]. IEEE J Biomed Health Inform, 2025, 29(11):8140-8152.
[23]	Fusari G, Coleman S, Davies J, et al. UPBEAT trial:a randomised feasibility evaluation of a digital system for UPper limB rEhabilitation After sTroke[J]. Pilot Feasibility Stud, 2025, 11(1):136.
[24]	Zanatta F, Gorini A, Fiorentino L, et al. Robot-assisted therapy following stroke:what effects on quality of life,cognitive and psychosocial outcomes?A systematic review[J]. J Rehabil Med, 2026:jrm44943.
[25]	Liu SY, Li M, Ding JL, et al. Effectiveness,feasibility,acceptability,and safety of digital interventions in post-stroke rehabilitation:a systematic review and meta-analysis of randomized controlled trials[J]. Arch Phys Med Rehabil, 2025, 106(12):1912-1932.
[26]	Pongsing P, Neranon P, Phukpattaranont P, et al. Enhancing upper-limb rehabilitation robot:autonomous self-adaptive resistance generation using EMG-based Fuzzy-PI control[J]. Adv Robot, 2024, 38(6):421-438.
[27]	Bartoli D, Petrizzo A, Vellone E, et al. Impact of telehealth on stroke survivor-caregiver dyad in at-home rehabilitation:a systematic review[J]. J Adv Nurs, 2024, 80(10):4003-4033.
[28]	Evidence reviews for robot-assisted arm training:Stroke rehabilitation in adults(update):evidence review[M]. London: National Institute for Health and Care Excellence(NICE), 2023.
[29]	Ferreira-Brito F, Alves S, Guerreiro T, et al. Digital health and patient adherence:a qualitative study in older adults[J]. Digit Health, 2024, 10:20552076231223805.
[30]	谭雨婷, 张志霞, 杨振, 等. 中年脑卒中患者康复锻炼生态瞬时评估影响因素的质性研究[J]. 中华护理杂志, 2024, 59(21):2620-2626.
	Tan YT, Zhang ZX, Yang Z, et al. A qualitative study of the influencing factors of ecological momentary assessment of rehabilitation exercise in middle-aged stroke patients[J]. Chin J Nurs, 2024, 59(21):2620-2626.
[31]	Han Y, Zhang JQ, Qing W, et al. Influencing factors of digital health technology anxiety in the elderly:a systematic review and meta-analysis[J]. Front Psychol, 2025, 16:1645753.
[32]	Aprile IG, Quaglini S, Turchetti G, et al. Rehabilitation robotics and allied digital technologies:opportunities,barriers and solutions for improving their clinical implementation. A position paper from the Fit for Medical Robotics Initiative[J]. Front Robot AI, 2025, 12:1531067.
[33]	Mathias R, McCulloch P, Chalkidou A, et al. Digital health technologies need regulation and reimbursement that enable flexible interactions and groupings[J]. npj Digit Med, 2024, 7:148.
[34]	卢霄蒙, 耿文慧, 尹运腾, 等. 数字健康在肿瘤病人支持性照顾需求中的应用现状[J]. 护理研究, 2023, 37(13):2404-2409.
	Lu XM, Geng WH, Yin YT, et al. Application status quo of digital health in supportive care needs of cancer patients[J]. Chin Nurs Res, 2023, 37(13):2404-2409.
[35]	朱金凤, 张远星, 王芳, 等. 脑卒中患者远程康复运动管理有效性及安全性的系统评价再评价[J]. 中华护理杂志, 2022, 57(12):1447-1455.
	Zhu JF, Zhang YX, Wang F, et al. The effectiveness and safety of telerehabilitation management in stroke patients:an overview of systematic reviews[J]. Chin J Nurs, 2022, 57(12):1447-1455.