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On-line Access: 2024-08-27
Received: 2023-10-17
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Kezhou LIU, Mengjie YIN, Zhengting CAI. Research and application advances in rehabilitation assessment of stroke[J]. Journal of Zhejiang University Science B, 2022, 23(8): 625-641.
@article{title="Research and application advances in rehabilitation assessment of stroke",
author="Kezhou LIU, Mengjie YIN, Zhengting CAI",
journal="Journal of Zhejiang University Science B",
volume="23",
number="8",
pages="625-641",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.B2100999"
}
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%A Mengjie YIN
%A Zhengting CAI
%J Journal of Zhejiang University SCIENCE B
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%P 625-641
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%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B2100999
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T1 - Research and application advances in rehabilitation assessment of stroke
A1 - Kezhou LIU
A1 - Mengjie YIN
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%@ 1673-1581
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.B2100999
Abstract: stroke has a high incidence and disability rate, and rehabilitation is an effective means to reduce the disability rate of patients. To systematize rehabilitation assessment, which is the foundation for rehabilitation therapy, we summarize the assessment methods commonly used in research and clinical applications, including the various types of stroke rehabilitation scales and their applicability, and related biomedical detection technologies, including surface electromyography (sEMG), motion analysis systems, transcranial magnetic stimulation (TMS), magnetic resonance imaging (MRI), and combinations of different techniques. We also introduce some assessment techniques that are still in the experimental phase, such as the prospective application of artificial intelligence (AI) with optical correlation tomography (OCT) in stroke rehabilitation. This review provides a useful bibliography for the assessment of not only the severity of stroke injury, but also the therapeutic effects of stroke rehabilitation, and establishes a solid base for the future development of stroke rehabilitation skills.
[1]AdjabiI, OuahabiA, BenzaouiA, et al., 2020. Past, present, and future of face recognition: a review. Electronics, 9(8):1188.
[2]AhmedS, MayoNE, HigginsJ, et al., 2003. The Stroke Rehabilitation Assessment of Movement (STREAM): a comparison with other measures used to evaluate effects of stroke and rehabilitation. Phys Ther, 83(7):617-630.
[3]AndrowisGJ, PilkarR, RamanujamA, et al., 2018. Electromyography assessment during gait in a robotic exoskeleton for acute stroke. Front Neurol, 9:630.
[4]AumannS, DonnerS, FischerJ, et al., 2019. Optical coherence tomography (OCT): principle and technical realization. In: Bille JF (Ed.), High Resolution Imaging in Microscopy and Ophthalmology. Springer, Cham, p.59-85.
[5]AuriatAM, NevaJL, PetersS, et al., 2015. A review of transcranial magnetic stimulation and multimodal neuroimaging to characterize post-stroke neuroplasticity. Front Neurol, 6:226.
[6]BaranU, LiYD, WangRK, 2015. In vivo tissue injury mapping using optical coherence tomography based methods. Appl Opt, 54(21):6448-6453.
[7]BarrittAW, SmithardDG, 2009. Role of cerebral cortex plasticity in the recovery of swallowing function following dysphagic stroke. Dysphagia, 24(1):83-90.
[8]BernhardtJ, BorschmannK, BoydL, et al., 2016. Moving rehabilitation research forward: developing consensus statements for rehabilitation and recovery research. Int J Stroke, 11(4):454-458.
[9]BevilacquaDE, MaillardS, FerrariJ, 2019. Measuring joint hypermobility using the Hospital Del Mar criteria—a reliability analysis using secondary data analysis. Arch Rheum Arthritis Res, 1(1):1-6.
[10]BiscettiF, GiovanniniS, StrafaceG, et al., 2016. RANK/RANKL/OPG pathway: genetic association with history of ischemic stroke in Italian population. Eur Rev Med Pharmacol Sci, 20(21):4574-4580.
[11]BlumL, Korner-BitenskyN, 2008. Usefulness of the Berg Balance Scale in stroke rehabilitation: a systematic review. Phys Ther, 88(5):559-566.
[12]BoseckerC, DipietroL, VolpeB, et al., 2010. Kinematic robot-based evaluation scales and clinical counterparts to measure upper limb motor performance in patients with chronic stroke. Neurorehabil Neural Repair, 24(1):62-69.
[13]BoydLA, HaywardKS, WardNS, et al., 2017. Biomarkers of stroke recovery: consensus-based core recommendations from the stroke recovery and rehabilitation roundtable. Neurorehabil Neural Repair, 31(10-11):864-876.
[14]CaliandroP, MolteniF, SimbolottiC, et al., 2020. Exoskeleton-assisted gait in chronic stroke: an EMG and functional near-infrared spectroscopy study of muscle activation patterns and prefrontal cortex activity. Clin Neurophysiol, 131(8):1775-1781.
[15]CalvertGA, BrammerMJ, MorrisRG, et al., 2000. Using fMRI to study recovery from acquired dysphasia. Brain Lang, 71(3):391-399.
[16]CarnevaleA, LongoUG, SchenaE, et al., 2019. Wearable systems for shoulder kinematics assessment: a systematic review. BMC Musculoskelet Disord, 20:546.
[17]ChenJ, SunDL, ShiYH, et al., 2019. Dynamic alterations in spontaneous neural activity in multiple brain networks in subacute stroke patients: a resting-state fMRI study. Front Neurosci, 12:994.
[18]ChoiWJ, ReifR, YousefiS, et al., 2014. Improved microcirculation imaging of human skin in vivo using optical microangiography with a correlation mapping mask. J Biomed Opt, 19(3):036010.
[19]ChoiWJ, LiYD, WangRK, 2019. Monitoring acute stroke progression: multi-parametric OCT imaging of cortical perfusion, flow, and tissue scattering in a mouse model of permanent focal ischemia. IEEE Trans Med Imaging, 38(6):1427-1437.
[20]CôtéR, BattistaRN, WolfsonC, et al., 1989. The Canadian Neurological Scale validation and reliability assessment. Neurology, 39(5):638-643.
[21]Dacosta-AguayoR, GrañaM, SavioA, et al., 2014. Prognostic value of changes in resting-state functional connectivity patterns in cognitive recovery after stroke: a 3T fMRI pilot study. Hum Brain Mapp, 35(8):3819-3831.
[22]de CarloTE, RomanoA, WaheedNK, et al., 2015. A review of optical coherence tomography angiography (OCTA). Int J Retina Vitreous, 1:5.
[23]DiasN, LiXH, ZhangC, et al., 2018. Innervation asymmetry of the external anal sphincter in aging characterized from high-density intra-rectal surface EMG recordings. Neurourol Urodyn, 37(8):2544-2550.
[24]DiasN, ZhangC, LiXH, et al., 2019. Neural control properties of the external anal sphincter in young and elderly women. Neurourol Urodyn, 38(7):1828-1833.
[25]DiesfeldtHFA, 1983. Verbal fluency tests and their significance for psychogeriatric practice. Tijdschr Gerontol Geriatr, 14:49-59.
[26]EkinciY, YaşaroğluOF, DügerT, 2021. Content comparison of four commonly used amputee mobility assessment scales in the literature by linking to the International Classification of Functioning, Disability, and Health. Prosthet Orthot Int, 45(6):544-552.
[27]EldaiefMC, DickersonBC, CamprodonJA, 2022. Transcranial magnetic stimulation for the neurological patient: scientific principles and applications. Semin Neurol, 42(2):149-157.
[28]EnderbyP, 1980. Frenchay Dysarthria Assessment. Int J Lang Commun Disord, 15(3):165-173.
[29]FengL, ZhouD, LuoC, et al., 2021. Clinically applicable artificial intelligence algorithm for the diagnosis, evaluation, and monitoring of acute retinal necrosis. J Zhejiang Univ-Sci B (Biomed & Biotechnol), 22(6):504-511.
[30]FerransCE, PowersMJ, 1985. Quality of life index: development and psychometric properties. Adv Nurs Sci, 8(1):15-24.
[31]FerransCE, PowersMJ, 1992. Psychometric assessment of the quality of life index. Res Nurs Health, 15(1):29-38.
[32]FolsteinMF, RobinsLN, HelzerJE, 1983. The Mini-Mental State Examination. Arch Gen Psychiatry, 40(7):812.
[33]Fugl-MeyerAR, JääsköL, LeymanI, et al., 1975. The post-stroke hemiplegic patient. 1. A method for evaluation of physical performance. Scand J Rehabil Med, 7(1):13-31.
[34]GallasS, MoirotP, DebonoG, et al., 2007. Mylohyoid motor-evoked potentials relate to swallowing function after chronic stroke dysphagia. Neurogastroenterol Motil, 19(6):453-458.
[35]GladstoneDJ, DanellsCJ, BlackSE, 2002. The Fugl-Meyer assessment of motor recovery after stroke: a critical review of its measurement properties. Neurorehabil Neural Repair, 16(3):232-240.
[36]GoenA, TiwariDC, 2013. Review of surface electromyogram signals: its analysis and applications. World Acad Sci Eng Technol Int J Electr Comput Eng, 7(11):936-943.
[37]GoldsteinLB, BertelsC, DavisJN, 1989. Interrater reliability of the NIH stroke scale. Arch Neurol, 46(6):660-662.
[38]GolestaniAM, TymchukS, DemchukA, et al., 2013. Longitudinal evaluation of resting-state fMRI after acute stroke with hemiparesis. Neurorehabil Neural Repair, 27(2):153-163.
[39]GowlandC, StratfordP, WardM, et al., 1993. Measuring physical impairment and disability with the Chedoke-McMaster Stroke Assessment. Stroke, 24(1):58-63.
[40]GuoQH, SunYM, YuPM, et al., 2007. Norm of auditory verbal learning test in the normal aged in China community. Chin J Clin Psychol, 15(2):132-134, 141 (in Chinese).
[41]GuptaD, BansalP, ChoudharyK, 2018. The state of the art of feature extraction techniques in speech recognition. In: Agrawal SS, Devi A, Wason R, et al. (Eds.), Speech and Language Processing for Human-Machine Communications. Springer, Singapore, p.195-207.
[42]HajekVE, RutmanDL, ScherH, 1989. Brief assessment of cognitive impairment in patients with stroke. Arch Phys Med Rehabil, 70(2):114-117.
[43]HamzeiF, LiepertJ, DettmersC, et al., 2006. Two different reorganization patterns after rehabilitative therapy: an exploratory study with fMRI and TMS. NeuroImage, 31(2):710-720.
[44]HanJ, WaddingtonG, AdamsR, et al., 2016. Assessing proprioception: a critical review of methods. J Sport Health Sci, 5(1):80-90.
[45]HantsonL, de WeerdtW, de KeyserJ, et al., 1994. The European Stroke Scale. Stroke, 25(11):2215-2219.
[46]HartiganI, 2007. A comparative review of the Katz ADL and the Barthel Index in assessing the activities of daily living of older people. Int J Older People Nurs, 2(3):204-212.
[47]HebertR, CarrierR, BilodeauA, 1988. The Functional Autonomy Measurement System (SMAF): description and validation of an instrument for the measurement of handicaps. Age Ageing, 17(5):293-302.
[48]HolbrookM, SkilbeckCE, 1983. An activities index for use with stroke patients. Age Ageing, 12(2):166-170.
[49]HuXG, SureshAK, RymerWZ, et al., 2015. Assessing altered motor unit recruitment patterns in paretic muscles of stroke survivors using surface electromyography. J Neural Eng, 12(6):066001.
[50]HwangP, SohnMK, KimCS, et al., 2016. Tibial somatosensory evoked potential can prognosticate for ambulatory function in subacute hemiplegic stroke. J Clin Neurosci, 26:122-125.
[51]ImuraT, MitsutakeT, IwamotoY, et al., 2021. A systematic review of the usefulness of magnetic resonance imaging in predicting the gait ability of stroke patients. Sci Rep, 11:14338.
[52]JangSH, 2011. A review of diffusion tensor imaging studies on motor recovery mechanisms in stroke patients. NeuroRehabilitation, 28(4):345-352.
[53]JaraczK, KozubskiW, 2003. Quality of life in stroke patients. Acta Neurol Scand, 107(5):324-329.
[54]JohanssonBB, 2011. Current trends in stroke rehabilitation. A review with focus on brain plasticity. Acta Neurol Scand, 123(3):147-159.
[55]JoshiCD, LahiriU, ThakorNV, 2013. Classification of gait phases from lower limb EMG: application to exoskeleton orthosis. Proceedings of 2013 IEEE Point-of-Care Healthcare Technologies (PHT), p.228-231.
[56]LACKallenberg, HermensHJ, 2009. Motor unit properties of biceps brachii in chronic stroke patients assessed with high-density surface EMG. Muscle Nerve, 39(2):177-185.
[57]KasnerSE, 2006. Clinical interpretation and use of stroke scales. Lancet Neurol, 5(7):603-612.
[58]KatzN, ItzkovichM, AverbuchS, et al., 1989. Loewenstein Occupational Therapy Cognitive Assessment (LOTCA) battery for brain-injured patients: reliability and validity. Am J Occup Ther, 43(3):184-192.
[59]KatzS, 1983. Assessing self-maintenance: activities of daily living, mobility, and instrumental activities of daily living. J Am Geriatr Soc, 31(12):721-727.
[60]KatzS, DownsTD, CashHR, et al., 1970. Progress in development of the index of ADL. Gerontologist, 10(1):20-30.
[61]KawamuraCM, de Morais FilhoMC, BarretoMM, et al., 2007. Comparison between visual and three-dimensional gait analysis in patients with spastic diplegic cerebral palsy. Gait Posture, 25(1):18-24.
[62]KidzińskiŁ, YangB, HicksJL, et al., 2020. Deep neural networks enable quantitative movement analysis using single-camera videos. Nat Commun, 11:4054.
[63]KimB, WinsteinC, 2017. Can neurological biomarkers of brain impairment be used to predict poststroke motor recovery? A systematic review. Neurorehabil Neural Repair, 31(1):3-24.
[64]KimBJ, KangHG, KimHJ, et al., 2014. Magnetic resonance imaging in acute ischemic stroke treatment. J Stroke, 16(3):131-145.
[65]LawtonMP, BrodyEM, 1969. Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist, 9(3):179-186.
[66]LeeMH, SiewiorekDP, SmailagicA, et al., 2020. Opportunities of a machine learning-based decision support system for stroke rehabilitation assessment. arXiv:2002.12261v2.
[67]LiF, AnBC, ZhengJJ, 2015. Evaluating hand neural-muscle function after stroke with surface electromyography (review). Chin J Rehabil Theory Pract, 21(3):280-283 (in Chinese).
[68]LiXY, ShinH, ZhouP, et al., 2014. Power spectral analysis of surface electromyography (EMG) at matched contraction levels of the first dorsal interosseous muscle in stroke survivors. Clin Neurophysiol, 125(5):988-994.
[69]LinacreJM, HeinemannAW, WrightBD, et al., 1994. The structure and stability of the Functional Independence Measure. Arch Phys Med Rehabil, 75(2):127-132.
[70]LindenbergR, RengaV, ZhuLL, et al., 2010. Structural integrity of corticospinal motor fibers predicts motor impairment in chronic stroke. Neurology, 74(4):280-287.
[71]LindenstrømE, BoysenG, ChristiansenLW, et al., 1991. Reliability of Scandinavian Neurological Stroke Scale. Cerebrovasc Dis, 1(2):103-107.
[72]LindmarkB, HamrinE, 1988. Evaluation of functional capacity after stroke as a basis for active intervention. Presentation of a modified chart for motor capacity assessment and its reliability. Scand J Rehabil Med, 20(3):103-109.
[73]LioiG, ButetS, FleuryM, et al., 2020. A multi-target motor imagery training using bimodal EEG-fMRI neurofeedback: a pilot study in chronic stroke patients. Front Hum Neurosci, 14:37.
[74]LiuC, ChenS, ZhangH, et al., 2021. Bioinformatic analysis for potential biological processes and key targets of heart failure-related stroke. J Zhejiang Univ-Sci B (Biomed & Biotechnol), 22(9):718-732.
[75]LiuJ, WangY, ZhaoYQ, et al., 2016. Measurement of cerebral blood flow rate and its relationship with brain function using optical coherence tomography. Proceedings of SPIE 9707, Dynamics and Fluctuations in Biomedical Photonics XIII, p.208-213.
[76]LorenziM, BonassiS, LorenziT, et al., 2018. A review of telomere length in sarcopenia and frailty. Biogerontology, 19(3-4):209-221.
[77]MaCC, LiuAJ, LiZZ, et al., 2014. Longitudinal study of diffusion tensor imaging properties of affected cortical spinal tracts in acute and chronic hemorrhagic stroke. J Clin Neurosci, 21(8):1388-1392.
[78]MaZH, DingN, YuY, et al., 2018. Quantification of cerebral vascular perfusion density via optical coherence tomography based on locally adaptive regional growth. Appl Opt, 57(35):10117-10124.
[79]MarottaN, AmmendoliaA, MarinaroC, et al., 2020. International classification of functioning, disability and health (ICF) and correlation between disability and finance assets in chronic stroke patients. Acta Biomed, 91(3):e2020064.
[80]McDonnellMN, StinearCM, 2017. TMS measures of motor cortex function after stroke: a meta-analysis. Brain Stimul, 10(4):721-734.
[81]MintzopoulosD, AstrakasLG, KhanichehA, et al., 2009. Connectivity alterations assessed by combining fMRI and MR-compatible hand robots in chronic stroke. NeuroImage, 47(Suppl 2):T90-T97.
[82]MouridsenK, ThurnerP, ZaharchukG, 2020. Artificial intelligence applications in stroke. Stroke, 51(8):2573-2579.
[83]NasreddineZS, PhillipsNA, BédirianV, et al., 2005. The montreal cognitive assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc, 53(4):695-699.
[84]NazmiN, RahmanMAA, YamamotoSI, et al., 2019. Walking gait event detection based on electromyography signals using artificial neural network. Biomed Signal Process Control, 47:334-343.
[85]OhSS, KimY, LeeYB, et al., 2022. Optical modalities for research, diagnosis, and treatment of stroke and the consequent brain injuries. Appl Sci, 12(4):1891.
[86]OsmonDC, SmetIC, WinegardenB, et al., 1992. Neurobehavioral Cognitive Status Examination: its use with unilateral stroke patients in a rehabilitation setting. Arch Phys Med Rehabil, 73(5):414-418.
[87]Ostrosky-SolísF, LozanoA, 2006. Digit span: effect of education and culture. Int J Psychol, 41(5):333-341.
[88]ParkCH, ChangWH, OhnSH, et al., 2011. Longitudinal changes of resting-state functional connectivity during motor recovery after stroke. Stroke, 42(5):1357-1362.
[89]ParkJS, HwangNK, KimHH, et al., 2019. Effect of neuromuscular electrical stimulation combined with effortful swallowing using electromyographic biofeedback on oropharyngeal swallowing function in stroke patients with dysphagia: a pilot study. Medicine (Baltimore), 98(44):e17702.
[90]ParkS, FisherAG, VelozoCA, 1994. Using the Assessment of Motor and Process Skills to compare occupational performance between clinic and home settings. Am J Occup Ther, 48(8):697-709.
[91]ParsonsMW, ChristensenS, McElduffP, et al., 2010. Pretreatment diffusion- and perfusion-MR lesion volumes have a crucial influence on clinical response to stroke thrombolysis. J Cereb Blood Flow Metab, 30(6):1214-1225.
[92]PellicciariMC, BonnìS, PonzoV, et al., 2018. Dynamic reorganization of TMS-evoked activity in subcortical stroke patients. Neuroimage, 175:365-378.
[93]PfefferRI, KurosakiTT, HarrahCH, et al., 1982. Measurement of functional activities in older adults in the community. J Gerontol, 37(3):323-329.
[94]PlatzT, EickhofC, NuyensG, et al., 2005. Clinical scales for the assessment of spasticity, associated phenomena, and function: a systematic review of the literature. Disabil Rehabil, 27(1-2):7-18.
[95]QiR, 2005. Introduction of common stroke efficacy evaluation methods. Chin Acupunct Maribustion, 25(4):263-264 (in Chinese).
[96]ReitanRM, 1955. Investigation of the validity of Halstead’s measures of biological intelligence. AMA Arch Neurol Psych, 73(1):28-35.
[97]RosaMCN, MarquesA, DemainS, et al., 2014. Lower limb co-contraction during walking in subjects with stroke: a systematic review. J Electromyogr Kinesiol, 24(1):1-10.
[98]RoyallDR, CordesJA, PolkM, 1998. CLOX: an executive clock drawing task. J Neurol Neurosurg Psych, 64(5):588-594.
[99]RozanskiGM, HuntleyAH, CrosbyLD, et al., 2020. Lower limb muscle activity underlying temporal gait asymmetry post-stroke. Clin Neurophysiol, 131(8):1848-1858.
[100]RudrapatnaSU, WielochT, BeirupK, et al., 2014. Can diffusion kurtosis imaging improve the sensitivity and specificity of detecting microstructural alterations in brain tissue chronically after experimental stroke? Comparisons with diffusion tensor imaging and histology. NeuroImage, 97:363-373.
[101]SackAT, DEJLinden, 2003. Combining transcranial magnetic stimulation and functional imaging in cognitive brain research: possibilities and limitations. Brain Res Rev, 43(1):41-56.
[102]ScanoA, ZanolettiM, PirovanoI, et al., 2019. NIRS-EMG for clinical applications: a systematic review. Appl Sci, 9(15):2952.
[103]ScarpinaF, TaginiS, 2017. The Stroop Color and Word Test. Front Psychol, 8:557.
[104]SchlagerA, AhlqvistK, Rasmussen-BarrE, et al., 2018. Inter- and intra-rater reliability for measurement of range of motion in joints included in three hypermobility assessment methods. BMC Musculoskelet Disord, 19:376.
[105]SchwarzA, KanzlerCM, LambercyO, et al., 2019. Systematic review on kinematic assessments of upper limb movements after stroke. Stroke, 50(3):718-727.
[106]ShahS, VanclayF, CooperB, 1989. Improving the sensitivity of the Barthel Index for stroke rehabilitation. J Clin Epidemiol, 42(8):703-709.
[107]ShahSK, 1984. Reliability of the original Brunnstrom recovery scale following hemiplegia. Aust Occup Ther J, 31(4):144-151.
[108]SrinivasanVJ, RadhakrishnanH, JiangJY, et al., 2012. Op
[109]tical coherence microscopy for deep tissue imaging of the cerebral cortex with intrinsic contrast. Opt Express, 20(3):2220-2239.
[110]SteinbergN, AdamsR, AyalonM, et al., 2019. Recent ankle injury, sport participation level, and tests of proprioception. J Sport Rehabil, 28(8):824-830.
[111]StinearCM, BarberPA, PetoeM, et al., 2012. The PREP algorithm predicts potential for upper limb recovery after stroke. Brain, 135(Pt 8):2527-2535.
[112]StinearCM, LangCE, ZeilerS, et al., 2020. Advances and challenges in stroke rehabilitation. Lancet Neurol, 19(4):348-360.
[113]SulterG, SteenC, de KeyserJ, 1999. Use of the Barthel Index and Modified Rankin Scale in acute stroke trials. Stroke, 30(8):1538-1541.
[114]TakaraK, 1971. Two-point discrimination on various type of skin graft to hand and foot. Kumamoto Igakkai Zasshi, 45(1):94-121.
[115]TaoWJ, LiuT, ZhengRC, et al., 2012. Gait analysis using wearable sensors. Sensors (Basel), 12(2):2255-2283.
[116]TopolEJ, 2019. High-performance medicine: the convergence of human and artificial intelligence. Nat Med, 25(1):44-56.
[117]TsaoCW, AdayAW, AlmarzooqZI, et al., 2022. Heart disease and stroke statistics—2022 update: a report from the American Heart Association. Circulation, 145(8):e153-e639.
[118]van DokkumL, HauretI, MottetD, et al., 2014. The contribution of kinematics in the assessment of upper limb motor recovery early after stroke. Neurorehabil Neural Repair, 28(1):4-12.
[119]VeerbeekJM, KwakkelG, van WegenEEH, et al., 2011. Early prediction of outcome of activities of daily living after stroke: a systematic review. Stroke, 42(5):1482-1488.
[120]VermeerKA, MoJ, WedaJJA, et al., 2014. Depth-resolved model-based reconstruction of attenuation coefficients in optical coherence tomography. Biomed Opt Express, 5(1):322-337.
[121]VinstrupJ, CalatayudJ, JakobsenMD, et al., 2018. Hand strengthening exercises in chronic stroke patients: dose-response evaluation using electromyography. J Hand Ther, 31(1):111-121.
[122]WaddingtonG, AdamsR, HanJ, et al., 2014. A new method for measuring dynamic proprioception. J Sci Med Sport, 18(Suppl 1):e141.
[123]WangC, MengB, ChenJP, 2019. Applicability of Boston naming test for assessment of postoperative language dysfunction. Zhejiang Med, 41(16):1742-1745 (in Chinese).
[124]WangH, MagnainC, SakadžićS, et al., 2017. Characterizing the optical properties of human brain tissue with high numerical aperture optical coherence tomography. Biomed Opt Express, 8(12):5617-5636.
[125]WangP, WangHX, 2016. Advance in neuro-electrophysiological techniques in functional evaluation after stroke (review). Chin J Rehabil Theory, 22(12):1404-1407 (in Chinese).
[126]WangRK, AnL, 2009. Doppler optical micro-angiography for volumetric imaging of vascular perfusion in vivo. Opt Express, 17(11):8926-8940.
[127]WangTL, MantiniD, GillebertCR, 2018. The potential of real-time fMRI neurofeedback for stroke rehabilitation: a systematic review. Cortex, 107:148-165.
[128]WareJE, SherbourneCD, 1992. The MOS 36-ltem short-form health survey (SF-36): I. Conceptual framework and item selection. Med Care, 30(6):473-483.
[129]WeiPN, ZhangJH, WeiPP, et al., 2020. Different sEMG and EEG features analysis for gait phase recognition. Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, p.1002-1006.
[130]WeinsteinS, 1993. Fifty years of somatosensory research. J Hand Ther, 6(1):11-22.
[131]WHO (World Health Organization), 2001. International Classification of Functioning, Disability and Health (ICF). World Health Organization, Geneva.
[132]WilliamsLS, WeinbergerM, HarrisLE, et al., 1999. Development of a stroke-specific quality of life scale. Stroke, 30(7):1362-1369.
[133]YamadaK, SakaiK, AkazawaK, et al., 2013. Detection of early neuronal damage in CADASIL patients by q-space MR imaging. Neuroradiology, 55(3):283-290.
[134]YangSS, LiuKZ, DingHJ, et al., 2019. Longitudinal in vivo intrinsic optical imaging of cortical blood perfusion and tissue damage in focal photothrombosis stroke model. J Cereb Blood Flow Metab, 39(7):1381-1393.
[135]YooAJ, BarakER, CopenWA, et al., 2010. Combining acute diffusion-weighted imaging and mean transmit time lesion volumes with national institutes of health stroke scale score improves the prediction of acute stroke outcome. Stroke, 41(8):1728-1735.
[136]YoonHS, YouJSH, 2017. Reflex-mediated dynamic neuromuscular stabilization in stroke patients: EMG processing and ultrasound imaging. Technol Health Care, 25(S1):99-106.
[137]ZhaoQH, GuoQH, LiF, et al., 2013. The Shape Trail Test: application of a new variant of the trail making test. PLoS ONE, 8(2):e57333.
[138]ZhengJJ, HuYH, YuZW, 2007. Application of surface electromyography in the estimate of neural-muscle function (review). Chin J Rehabil Theory Pract, 13(8):741-742 (in Chinese).
[139]ZhouN, 2002. A new approach to stroke assessment: SIAS. Foreign Med Sci (Sec Phys Med Relakieitation), 22(1):1-4 (in Chinese).
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