| 1 |
GBD 2021 Stroke Collaborators. Global, regional, and national burden of stroke and its risk factors, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021 [J]. Lancet Neurol, 2024, 23(10): 973-1003.
|
| 2 |
Wang W, Jiang B, Sun H, et al. Prevalence, incidence, and mortality of stroke in China: results from a nationwide population-based survey of 480 687 adults [J]. Circulation, 2017, 135(8): 759-771.
|
| 3 |
王泽, 王宝军, 张玮泽. 脑卒中康复治疗研究进展 [J/OL]. 中华脑血管病杂志(电子版), 2022, 16(4): 221-224.
|
| 4 |
Langhorne P, Coupar F, Pollock A. Motor recovery after stroke: a systematic review [J]. Lancet Neurol, 2009, 8(8): 741-754.
|
| 5 |
Tiwari A, Delmas S, Poisson SN, et al. Cognitive impairments impact functional mobility in stroke survivors [J]. Neurorehabil Neural Repair, 2025, 39(12): 959-971.
|
| 6 |
Bernhardt J, Churilov L, Ellery F, et al. Prespecified dose-response analysis for A Very Early Rehabilitation Trial (AVERT) [J]. Neurology, 2016, 86(23): 2138-2145.
|
| 7 |
Amano S, Umeji A, Uchita A, et al. Clinimetric properties of the Fugl-Meyer assessment with adapted guidelines for the assessment of arm function in hemiparetic patients after stroke [J]. Top Stroke Rehabil, 2018, 25(7): 500-508.
|
| 8 |
Alghadir AH, Al-Eisa ES, Anwer S, et al. Reliability, validity, and responsiveness of three scales for measuring balance in patients with chronic stroke [J]. BMC Neurol, 2018, 18(1): 141.
|
| 9 |
Duncan PW, Propst M, Nelson SG. Reliability of the Fugl-Meyer assessment of sensorimotor recovery following cerebrovascular accident [J]. Phys Ther, 1983, 63(10): 1606-1610.
|
| 10 |
Lang CE, Macdonald JR, Reisman DS, et al. Observation of amounts of movement practice provided during stroke rehabilitation [J]. Arch Phys Med Rehabil, 2009, 90(10): 1692-1698.
|
| 11 |
中华人民共和国国家卫生健康委员会. 中国卫生健康统计年鉴2023 [M]. 北京: 中国协和医科大学出版社, 2023: 42.
|
| 12 |
Jing Q, Tang Q, Sun M, et al. Regional disparities of rehabilitation resources for persons with disabilities in China: data from 2014 to 2019 [J]. Int J Environ Res Public Health, 2020, 17(19): 7319.
|
| 13 |
Sivan M, Negrini S. An expanded workforce is needed to strengthen rehabilitation in health systems [J]. BMJ, 2024, 384: q60.
|
| 14 |
Gaboury I, Tousignant M, Corriveau H, et al. Effects of telerehabilitation on patient adherence to a rehabilitation plan: protocol for a mixed methods trial [J]. JMIR Res Protoc, 2021, 10(10): e32134.
|
| 15 |
Li J, Li LSW. Development of rehabilitation in China [J]. Phys Med Rehabil Clin N Am, 2019, 30(4): 769-773.
|
| 16 |
Huang L, Jiang L, Xu Y, et al. Design and implementation of informatization for unified management of stroke rehabilitation in urban multi-level hospitals [J]. Front Neurosci, 2023, 17: 1100681.
|
| 17 |
Chen S, Fan S, Qiao Z, et al. Transforming healthcare: intelligent wearable sensors empowered by smart materials and artificial intelligence [J]. Adv Mater, 2025, 37(21): 2500412.
|
| 18 |
Chen S, Qi J, Fan S, et al. Flexible wearable sensors for cardiovascular health monitoring [J]. Adv Healthc Mater, 2021, 10(17): 2100116.
|
| 19 |
Ashammakhi N, Hernandez AL, Unluturk BD, et al. Biodegradable implantable sensors: materials design, fabrication, and applications [J]. Adv Funct Mater, 2021, 31(49): 2170365.
|
| 20 |
Li B, Cao PF, Saito T, et al. Intrinsically self-healing polymers: from mechanistic insight to current challenges [J]. Chem Rev, 2023, 123(2): 701-735.
|
| 21 |
Liu X, Wang L, Xiang Y, et al. Magnetic soft microfiberbots for robotic embolization [J]. Sci Robot, 2024, 9(87): eadh2479.
|
| 22 |
Nguyen DT, Zeng Q, Tian X, et al. Ambient health sensing on passive surfaces using metamaterials [J]. Sci Adv, 2024, 10(1): eadj6613.
|
| 23 |
Morales D, Palleau E, Dickey MD, et al. Electro-actuated hydrogel walkers with dual responsive legs [J]. Soft Matter, 2014, 10(9): 1337-1348.
|
| 24 |
Lee J, Tan MWM, Parida K, et al. Water-processable, stretchable, self-healable, thermally stable, and transparent ionic conductors for actuators and sensors [J]. Adv Mater, 2020, 32(7): e1906679.
|
| 25 |
Jung YH, Hong SK, Wang HS, et al. Flexible piezoelectric acoustic sensors and machine learning for speech processing [J]. Adv Mater, 2020, 32(35): e1904020.
|
| 26 |
Hu Y, Gao S, Lu H, et al. Acid-resistant and physiological pH-responsive DNA hydrogel composed of A-motif and i-motif toward oral insulin delivery [J]. J Am Chem Soc, 2022, 144(12): 5461-5470.
|
| 27 |
Zhu X, Wu K, Xie X, et al. A robust near-field body area network based on coaxially-shielded textile metamaterial [J]. Nat Commun, 2024, 15(1): 6589.
|
| 28 |
Qiu S, Zhao H, Jiang N, et al. Multi-sensor information fusion based on machine learning for real applications in human activity recognition: state-of-the-art and research challenges [J]. Inf Fusion, 2022, 80: 241-265.
|
| 29 |
Han Y, Tao Q, Zhang X. Multijoint continuous motion estimation for human lower limb based on surface electromyography [J]. Sensors (Basel), 2025, 25(3): 719.
|
| 30 |
Rashid SM, Ghiasi AR. Adaptive digital twin integration with multilevel inverter control for energy efficient smart rehabilitation systems [J]. Sci Rep, 2025, 15(1): 8511.
|
| 31 |
Premchand B, Zhang Z, Ang KK, et al. A personalized multimodal BCI-soft robotics system for rehabilitating upper limb function in chronic stroke patients [J]. Biomimetics, 2025, 10(2): 94.
|
| 32 |
González-España JJ, Sánchez-Rodríguez L, Pacheco-Ramírez MA, et al. At-home stroke neurorehabilitation: early findings with the NeuroExo BCI system [J]. Sensors (Basel), 2025, 25(5): 1322.
|
| 33 |
吴章薇, 张通, 赵军, 等. 脑卒中偏瘫患者连续步行中骨盆不对称活动的动态分析 [J/OL]. 中华脑血管病杂志(电子版), 2025, 19(5): 364-374.
|
| 34 |
Xu S, Wang D, Huang X, et al. Markerless motion capture system for stroke gait analysis [C]// Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023). Bellingham (USA): SPIE, 2023: 127240A.
|
| 35 |
Alammari BJ, Schoenwether B, Ripic Z, et al. Validity of AI-driven markerless motion capture for spatiotemporal gait analysis in stroke survivors [J]. Sensors, 2025, 25(17): 5315.
|
| 36 |
吕晓东, 刘海杰, 陈腾, 等. 一种脑卒中患者上肢康复评估系统: 中国, CN116966056A [P]. 2023-10-31.
|
| 37 |
Bai A, Song H, Wu Y, et al. Sliding-window CNN+channel-time attention transformer network trained with inertial measurement units and surface electromyography data for the prediction of muscle activation and motion dynamics leveraging IMU-only wearables for home-based shoulder rehabilitation [J]. Sensors (Basel), 2025, 25(4): 1275.
|
| 38 |
Luo Y, Liu C, Lee YJ, et al. Adaptive tactile interaction transfer via digitally embroidered smart gloves [J]. Nat Commun, 2024, 15(1): 868.
|
| 39 |
Angkanapiwat Y, Slepyan A, Greene RJ, et al. SensoPatch: a reconfigurable haptic feedback with high-density tactile sensing glove [C/OL]// 2024 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE, 2024 [2024-09-27].
|
| 40 |
Xu G, Wang H, Zhao G, et al. Self-powered electrotactile textile haptic glove for enhanced human-machine interface [J]. Sci Adv, 2025, 11(12): eadt0318.
|
| 41 |
Labeit B, Michou E, Trapl-Grundschober M, et al. Dysphagia after stroke: research advances in treatment interventions [J]. Lancet Neurol, 2024, 23(4): 418-428.
|
| 42 |
Ge G, Cai Y, Dong Q, et al. A flexible pressure sensor based on rGO/polyaniline wrapped sponge with tunable sensitivity for human motion detection [J]. Nanoscale, 2018, 10(21): 10033-10040.
|
| 43 |
Do MH, Nhiem LT. Multifunctional sensor based on hybrid material of reduced graphene oxide and polyaniline [J]. J Electron Mater, 2023, 52(6): 4037-4044.
|
| 44 |
Zhong G, Liu Q, Huang Y, et al. A wideband multimodal flexible sensor integrating vertical graphene and sea urchin‐like nanoparticles for post‐stroke rehabilitation [J]. Adv Mater, 2025, 37(44): e08206.
|
| 45 |
Gazzanti Pugliese di Cotrone MA, Akhtar NF, Patera M, et al. Early detection of dysphagia signs in Parkinson's disease: an artificial intelligence-based approach using non-invasive sensors [J]. Sensors (Basel), 2025, 25(22): 6834.
|
| 46 |
Qiao J, Jiang YT, Dai Y, et al. Research on a real-time dynamic monitoring method for silent aspiration after stroke based on semisupervised deep learning: a protocol study [J]. Digit Health, 2023, 9: 20552076231183548.
|
| 47 |
杜润宜, 张玉梅, 刘利鹏, 等. 认知-运动双重任务训练对卒中后认知障碍的影响 [J/OL]. 中华脑血管病杂志(电子版), 2025, 19(2): 87-93.
|
| 48 |
Li B, Li M, Xia J, et al. Hybrid integrated wearable patch for brain EEG-fNIRS monitoring [J]. Sensors (Basel), 2024, 24(15): 4847.
|
| 49 |
Liu Z, Shore J, Wang M, et al. A systematic review on hybrid EEG/fNIRS in brain-computer interface [J]. Biomed Signal Process Control, 2021, 68: 102595.
|
| 50 |
Boudaya A, Chaabene S, Bouaziz B, et al. Mild cognitive impairment detection based on EEG and HRV data [J]. Digit Signal Process, 2024, 147: 104399.
|
| 51 |
Tee LY, Tan LF, Seetharaman S, et al. An automated mobile cognitive test for the identification of cognitive impairment: a cross-sectional feasibility and diagnostic study [J]. Mayo Clin Proc Digit Health, 2025, 3(3): 100252.
|
| 52 |
Berivanlou NH, Setarehdan SK, Noubari HA. Quantifying mental workload of operators performing N-back working memory task: toward fNIRS based passive BCI system [C]// 2016 23rd Iranian Conference on Biomedical Engineering and 2016 1st International Iranian Conference on Biomedical Engineering (ICBME). Piscataway (USA): IEEE, 2016: 140-145.
|
| 53 |
Kwak Y, Song WJ, Kim SE. FGANet: fNIRS-guided attention network for hybrid EEG-fNIRS brain-computer interfaces [J]. IEEE Trans Neural Syst Rehabil Eng, 2022, 30: 329-339.
|
| 54 |
Mcwillians EC, Barbey FM, Dyer JF, et al. Feasibility of repeated assessment of cognitive function in older adults using a wireless, mobile, dry-EEG headset and tablet-based games [J]. Front Psychiatry, 2021, 12: 574482.
|
| 55 |
Mohamed M, Mohamed N, Kim JG. P300 latency with memory performance: a promising biomarker for preclinical stages of Alzheimer's disease [J]. Biosensors (Basel), 2024, 14(12): 616.
|
| 56 |
Bonetti LV, Hassan SA, Lau ST, et al. Oxyhemoglobin changes in the prefrontal cortex in response to cognitive tasks: a systematic review [J]. Int J Neurosci, 2019, 129(2): 195-203.
|
| 57 |
Yang D, Huang R, Yoo SH, et al. Detection of mild cognitive impairment using convolutional neural network: temporal-feature maps of functional near-infrared spectroscopy [J]. Front Aging Neurosci, 2020, 12: 572012.
|
| 58 |
Hassin-Baer S, Cohen OS, Israeli-Korn S, et al. Identification of an early-stage Parkinson's disease neuromarker using event-related potentials, brain network analytics and machine-learning [J]. PLoS One, 2022, 17(1): e0261947.
|
| 59 |
Liang J. Developing a wearable system with fNIRs & EEG multimodality classification engine & miniaturized device [C]// 2024 IEEE 2nd International Conference on Sensors, Electronics and Computer Engineering (ICSECE). Piscataway (USA): IEEE, 2024: 885-891.
|
| 60 |
Shibu CJ, Sreedharan S, Arun K, et al. Explainable artificial intelligence model to predict brain states from fNIRS signals [J]. Front Hum Neurosci, 2023, 16: 1029784.
|
| 61 |
Murphy TH, Corbett D. Plasticity during stroke recovery: from synapse to behaviour [J]. Nat Rev Neurosci, 2009, 10(12): 861-872.
|
| 62 |
Joy MT, Carmichael ST. Encouraging an excitable brain state: mechanisms of brain repair in stroke [J]. Nat Rev Neurosci, 2021, 22(1): 38-53.
|
| 63 |
Chen D, Shi J, Tao B, et al. A Novel transfer learning-based hybrid EEG-fNIRS brain-computer interface for intracerebral hemorrhage rehabilitation [J]. Adv Sci, 2025, 12(43): e05426.
|
| 64 |
Ren C, Li X, Gao Q, et al. The effect of brain-computer interface controlled functional electrical stimulation training on rehabilitation of upper limb after stroke: a systematic review and Meta-analysis [J]. Front Hum Neurosci, 2024, 18: 1438095.
|
| 65 |
Chen S, Xie N, Tang Y, et al. Long-term brain-computer interface functional electrical stimulation enhances neuroplasticity and functional recovery in elderly stroke: a 4.5-year longitudinal study integrating electroencephalography biomarkers and clinical assessments [J]. Research (Wash D C), 2025, 8: 0984.
|
| 66 |
Wang A, Tian X, Jiang D, et al. Rehabilitation with brain-computer interface and upper limb motor function in ischemic stroke: a randomized controlled trial [J]. Med, 2024, 5(6): 559-569.e4.
|
| 67 |
Biasiucci A, Leeb R, Iturrate I, et al. Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke [J]. Nat Commun, 2018, 9(1): 2421.
|
| 68 |
Li N, Yang Y, Li G, et al. Multi-sensor fusion-based mirror adaptive assist-as-needed control strategy of a soft exoskeleton for upper limb rehabilitation [J]. IEEE Trans Autom Sci Eng, 2024, 21(1): 475-487.
|
| 69 |
de Crignis AC, Ruhnau ST, Hösl M, et al. Robotic arm training in neurorehabilitation enhanced by augmented reality-a usability and feasibility study [J]. J Neuroeng Rehabil, 2023, 20(1): 105.
|
| 70 |
Wu B. A brain-computer-interface driven forearm exoskeleton with adaptive neuroregulation-based feedback for stroke rehabilitation [J]. Alex Eng J, 2025, 131: 199-208.
|
| 71 |
Guo Y, Tian Y, Wang H, et al. Adaptive hybrid-mode assist-as-needed control of upper limb exoskeleton for rehabilitation training [J]. Mechatronics, 2024, 100: 103188.
|
| 72 |
Alves T, Goncalves RS, Carbone G. Serious games strategies with cable-driven robots for rehabilitation tasks [C]// New Trends in Medical and Service Robotics: MESROB 2021. Basel (Switzerland): Springer, 2022: 1-9.
|
| 73 |
Shin S, Lee HJ, Chang WH, et al. A smart glove digital system promotes restoration of upper limb motor function and enhances cortical hemodynamic changes in subacute stroke patients with mild to moderate weakness: a randomized controlled trial [J]. J Clin Med, 2022, 11(24): 7343.
|
| 74 |
Farrens AJ, Reinsdorf D, Garcia-Fernandez L, et al. Variants of active assist robotic therapy: feasibility of virtual assistance and proprioceptive training as gauged by their effects on success and motivation during finger movement training after stroke [J]. J Neuroeng Rehabil, 2025, 22(1): 167.
|
| 75 |
Chai X, Cao T, He Q, et al. Brain-computer interface digital prescription for neurological disorders [J]. CNS Neurosci Ther, 2024, 30(2): e14615.
|
| 76 |
Kiyono K, Tanabe S, Hirano S, et al. Effectiveness of robotic devices for medical rehabilitation: an umbrella review [J]. J Clin Med, 2024, 13(21): 6616.
|
| 77 |
Doumas I, Lejeune T, Edwards M, et al. Clinical validation of an individualized auto-adaptative serious game for combined cognitive and upper limb motor robotic rehabilitation after stroke [J]. J Neuroeng Rehabil, 2025, 22(1): 10.
|
| 78 |
Soni AK, Kumar M, Kothari S. Efficacy of home based computerized adaptive cognitive training in patients with post stroke cognitive impairment: a randomized controlled trial [J]. Sci Rep, 2025, 15(1): 1072.
|
| 79 |
Liscano Y, Bernal L, Díaz-Vallejo J. Effectiveness of AI-assisted digital therapies for post-stroke aphasia rehabilitation: a systematic review [J]. Brain Sci, 2025, 15(9): 1007.
|
| 80 |
García-Rudolph A, Wright MA, Teixidó-Font C, et al. Digital twins in stroke rehabilitation: a scoping review of objectives, data sources, mechanisms, outcomes, and desirable properties [J]. Top Stroke Rehabil, 2025: 1-15.
|
| 81 |
Ye D, Luo H, Winstein C, et al. Towards AI-based precision rehabilitation via contextual model-based reinforcement learning [J]. J Neuroeng Rehabil, 2025, 22(1): 263.
|
| 82 |
Pedroso AF, Schwamm LH, Khera R. Distributed precision stroke care: artificial intelligence-driven stroke management using multimodal sensor data [J]. Stroke, 2026, 57(2): 526-537.
|
| 83 |
Chen Y, Wang W, Diao J, et al. Digital-twin-based patient evaluation during stroke rehabilitation [C]// Proceedings of the ACM/IEEE 14th International Conference on Cyber-Physical Systems. San Antonio (USA): ACM, 2023: 22-33.
|
| 84 |
Cirillo E, Conte C, Moccardi A, et al. Resilient AI in therapeutic rehabilitation: the integration of computer vision and deep learning for dynamic therapy adaptation [J]. Appl Sci, 2025, 15(12): 6800.
|
| 85 |
Hou K, Khan MMR, Rahman MH. An accessible AI-assisted rehabilitation system for guided upper limb therapy [J]. Sensors (Basel), 2025, 25(19): 6239.
|
| 86 |
Wu Q, Qin P, Kuang J, et al. A training-free infant spontaneous movement assessment method for cerebral palsy prediction based on videos [J]. IEEE Trans Neural Syst Rehabil Eng, 2023, 31: 1670-1679.
|
| 87 |
Ward RE, Setiawan IMA, Quinby E, et al. Mobile health to support community-integration of individuals with disabilities using iMHere 2.0: focus group study [J]. JMIR Hum Factors, 2022, 9(1): e31376.
|
| 88 |
Mohammad Namdar M, Lowery Wilson M, Murtonen KP, et al. How AI-based digital rehabilitation improves end-user adherence: rapid review [J]. JMIR Rehabil Assist Technol, 2025, 12: e69763.
|
| 89 |
Tang J, Abedi A, Colella TJF, et al. Rehabilitation exercise quality assessment and feedback generation using large language models with prompt engineering [C]// ArtifiAI for Aging Rehabilitation and Intelligent Assisted Living: IJCAI 2025. Singapore (Singapore): Springer, 2025: 60-75.
|
| 90 |
国家医疗保障局. 国家医保局印发《手术和治疗辅助操作类医疗服务价格项目立项指南(试行)》 [EB/OL]. 2026-01-20[2026-01-28].
|
| 91 |
潘钰, 张皓, 谢青, 等. 脑卒中智能康复技术应用专家共识 [J]. 中国康复医学杂志, 2025, 40(9): 1289-1297.
|