Nörorehabilitasyonda Entegre Teknolojiler: Kanıtlar, Mekanizmalar ve Gelecek Perspektifleri

Yıl 2026, Cilt: 6 Sayı: 1 , 72 - 85 , 27.04.2026

Muhammed Celal Eryilmaz

https://izlik.org/JA62DP44FR

Öz

Bu anlatı ve bütünleştirici inceleme, çağdaş nörorehabilitasyonda entegre teknolojilerin rolünü, öncelikle inme ve diğer merkezi sinir sistemi yaralanmalarına odaklanarak incelemektedir. Özellikle, yoğun, göreve özgü ve geri bildirim açısından zengin rehabilitasyon sağlamada robotik, Sanal Gerçeklik (SG) ve Beyin-Bilgisayar Arayüzlerinin (BBA) klinik ve nörobiyolojik etkilerini değerlendirmektedir. İncelenen literatürde, teknoloji destekli müdahaleler, 20-40 seans, saatte 300-400 görev tekrarı ve 60 dakikayı aşan günlük eğitim sürelerini içeren yapılandırılmış programlarla nicel olarak karakterize edilen yüksek dozlu eğitimi mümkün kılmaktadır. Bu yaklaşımlar motor ve bilişsel iyileşmeyi etkili bir şekilde desteklemektedir; ancak klinik sonuçlar, iyileşme aşaması ve fonksiyonel başlangıç noktasına bağlı olarak heterojen olmaya devam etmektedir. Kanıtlar, akut ve subakut hastaların spontan nöroplastisite penceresini değerlendirmek için yoğun robot destekli mobilizasyondan en fazla fayda sağladığını, oysa orta ila şiddetli bozuklukları olan kronik popülasyonların iyileşme platolarını aşmak için daha yüksek dozajlara ve hibrit sistemlere ihtiyaç duyduğunu göstermektedir. Ekonomik değerlendirmeler potansiyel uzun vadeli değeri gösterse de, mevcut maliyet etkinliği kanıtları küçük örneklem boyutları, metodolojik heterojenlik ve standartlaştırılmış uzun vadeli takip eksikliği nedeniyle ciddi şekilde sınırlıdır. Gelecekteki ilerleme, yapay zeka odaklı kişiselleştirme ve ölçeklenebilir ev tabanlı sistemlerin yaygınlaşmasına bağlıdır. Bu teknolojileri sürdürülebilir çözümlere başarıyla dönüştürmek için, uzun vadeli hasta uyumu, denetimsiz güvenlik izleme ve sağlam veri gizliliği/güvenlik protokolleri ile ilgili kritik zorlukların ele alınması gerekmektedir.

Anahtar Kelimeler

Nöro-geribildirim , Nöroplastisite , Nörolojik rehabilitasyon , Robotik , Sanal gerçeklik

Integrated Technologies in Neurorehabilitation: Evidence, Mechanisms, and Future Perspectives

https://izlik.org/JA62DP44FR

Öz

This narrative and integrative review examines the role of integrated technologies in contemporary neurorehabilitation, with a primary focus on stroke and other central nervous system injuries. Specifically, it evaluates the clinical and neurobiological effects of robotics, Virtual Reality (VR), and Brain–Computer Interfaces (BCI) in delivering intensive, task-specific, and feedback-rich rehabilitation. Across the reviewed literature, technology-supported interventions enable high-dose training, quantitatively characterized by structured programs involving 20–40 sessions, 300–400 task repetitions per hour, and daily training durations exceeding 60 minutes. These approaches effectively support motor and cognitive recovery; however, clinical outcomes remain heterogeneous based on the recovery stage and functional baseline. Evidence suggests that acute and subacute patients benefit most from intensive robotic-assisted mobilization to exploit the spontaneous neuroplasticity window, whereas chronic populations with moderate-to-severe impairments require higher dosages and hybrid systems to overcome recovery plateaus. Although economic evaluations indicate potential long-term value, current cost-effectiveness evidence is critically limited by small sample sizes, methodological heterogeneity, and a lack of standardized long-term follow-up. Future progress relies on AI-driven personalization and the expansion of scalable home-based systems. Successfully translating these technologies into sustainable solutions requires addressing critical challenges regarding long-term patient adherence, unsupervised safety monitoring, and robust data privacy/security protocols.

Anahtar Kelimeler

Neurofeedback , Neuroplasticity , Neurological rehabilitation , Robotics , Virtual reality

Kaynakça

• Honaga K. Neurorehabilitation for stroke patients with hemiparesis - functional recovery and motor learning. Juntendo Med J. 2021;67(1):24-31.
• Braun R, Wittenberg G. Motor recovery: how rehabilitation techniques and technologies can enhance recovery and neuroplasticity. Semin Neurol. 2021;41(2):167-76.
• Bhasin A, Kuthiala N, Srivastava MV, Kumaran SS. Neural substrates of motor learning strategies in stroke. Phys Ther Rehabil. 2021;8(1):4. doi:10.7243/2055-2386-8-4
• Halford E, Jakubiszak S, Krug K, Umphress A. What is task-oriented training? a scoping review. Student J Occup Ther. 2024;4(1):1-23.
• Kim M, Lim H, Lee H, Han I, Ku J, Kang Y. Brain–computer interface-based action observation combined with peripheral electrical stimulation enhances corticospinal excitability in healthy subjects and stroke patients. J Neural Eng. 2022;19(3):036039.
• Shah R, Daulat S, Ramu V, et al. Applications of brain-computer interface in action observation and motor imagery. In New Insights in Brain-Computer Interface Systems, ed. Kashou NH, London, IntechOpen, 2023, 1-14.
• Grosmaire A, Pila O, Breuckmann P, Duret C. Robot-assisted therapy for upper limb paresis after stroke: use of robotic algorithms in advanced practice. NeuroRehabilitation. 2022;51(4):577-93.
• Wang C, Hu T, Lin Y, Chen C, Hsu Y, Kao C. Use of noninvasive brain stimulation and neurorehabilitation devices to enhance poststroke recovery: review of the current evidence and pitfalls. J Int Med Res. 2024;52(4).doi:10.1177/03000605241238066
• Knežević S. Brain-computer interfaces in neurorehabilitation for central nervous system diseases: applications in stroke, multiple sclerosis and parkinson's disease. Sanamed. 2025;20(1):49-59.
• Yuan K, Chen C, Wang X, Chu C, Tong K. BCI training effects on chronic stroke correlate with functional reorganization in motor-related regions: a concurrent EEG and fMRI study. Brain Sci. 2021;11(1):56.
• Sasidharan SM, Mdletshe S, Wang A. Machine learning in stroke lesion segmentation and recovery forecasting: a review. Applied Sciences. 2025; 15(18): 10082.
• Uddin M, Ganapathy K, Syed-Abdul S. Digital technology enablers of tele-neurorehabilitation in pre- and post-covid-19 pandemic era – a scoping review. Int J Telerehabil. 2024;16(1):e6611. doi: 10.5195/ijt.2024.6611.
• Gebreheat G, Goman A, Porter‐Armstrong A. The use of home-based digital technology to support post- stroke upper limb rehabilitation: a scoping review. Clin Rehabil. 2023;38(1):60-71.
• Nalongo A. Robotics in physical therapy: enhancing patient outcomes. ROJESR. 2025;4(2):87-94.
• Park J, Park G, Kim H, et al. A comparison of the effects and usability of two exoskeletal robots with and without robotic actuation for upper extremity rehabilitation among patients with stroke: a single- blinded randomised controlled pilot study. J Neuroeng Rehabil. 2020;17(1):137. doi: 10.1186/s12984-020-00763-6.
• Arantes A, Bressan N, Borges L, McGibbon C. Evaluation of a novel real-time adaptive assist-as-needed controller for robot-assisted upper extremity rehabilitation following stroke. PLoS One. 2023;18(10):e0292627. doi: 10.1371/journal.pone.0292627.
• Rodgers H, Bosomworth H, Krebs H, et al. Robot-assisted training compared with an enhanced upper limb therapy programme and with usual care for upper limb functional limitation after stroke: the ratuls three-group rct. Health Technol Assess. 2020;24(54):1-232. doi: 10.3310/hta24540.
• Chen Z, Wang C, Fan W, et al. Robot-assisted arm training versus therapist-mediated training after stroke: a systematic review and meta-analysis. J Healthc Eng. 2020;2020: 8810867. doi: 10.1155/2020/8810867.
• Koleva I, Yoshinov RR, Yoshinov B. Clinical significance of robot manipulators for grasp, balance, and gait recovery (from the medical point of view). In Exploring the World of Robot Manipulators, ed. Küçük S, London, IntechOpen, 2024, 1-24.
• Munari D, Fonte C, Varalta V, et al. Effects of robot-assisted gait training combined with virtual reality on motor and cognitive functions in patients with multiple sclerosis: a pilot, single-blind, randomized controlled trial. Restor Neurol Neurosci. 2020;38(2):151-64.
• Kiper P, Godart N, Cavalier M, et al. Effects of immersive virtual reality on upper-extremity stroke rehabilitation: a systematic review with meta-analysis. J Clin Med. 2023;13(1):146. doi:10.3390/jcm13010146.
• Georgiev DD, Georgieva I, Gong Z, Nanjappan V, Georgiev GV. Virtual reality for neurorehabilitation and cognitive enhancement. Brain Sci. 2021;11(2):221. doi: 10.3390/brainsci11020221.
• Blázquez‐González P, González R, Mesa A, et al. Efficacy of the use of video games on mood, anxiety and depression in stroke patients: preliminary findings of a randomised controlled trial. J Neurol. 2024;271(3):1224-34.
• Balasubramanian P, Leon R, Snyder D, Beardsley S, Hyngstrom A, Schmit B. Altered cortical activity during a finger tap in people with stroke. Brain Topogr. 2024;37(5):907-20.
• Rustamov N, Souders L, Sheehan L, et al. IpsiHand brain-computer interface therapy induces broad upper extremity motor recovery in chronic stroke. Neurorehabil Neural Repair. 2025;39(1):74-86.
• Chen Z, Gu M, He C, Xiong C, Xu J, Huang X. Robot-assisted arm training in stroke individuals with unilateral spatial neglect: a pilot study. Front Neurol. 2021;12:691444. doi: 10.3389/fneur.2021.691444.
• Moskiewicz D, Sarzyńska-Długosz I. Modern technologies supporting motor rehabilitation after stroke: a narrative review. J Clin Med. 2025;14(22):8035. doi: 10.3390/jcm14228035.
• Alt Murphy M, Pradhan S, Levin M, Hancock NJ. Uptake of technology for neurorehabilitation in clinical practice: a scoping review. Phys Ther. 2024;104(2):pzad140. doi: 10.1093/ptj/pzad140.
• Olawade D, Modum ER, Olawuyi O, et al. The role of digital twin technology in physiotherapy and rehabilitation practice. Virtual Reality & Intelligent Hardware. 2026;8(1):71-86.
• Calabrò RS, Cerasa A, Ciancarelli I, et al. The arrival of the metaverse in neurorehabilitation: fact, fake or vision? Biomedicines. 2022;10(10):2602. doi: 10.3390/biomedicines10102602.
• Xue Q, Zhengang Q. Neural regulation technology combined with functional neuroimaging in stroke rehabilitation: mechanism research and application progress. Balneo PRM Res J. 2025;16(3):837. doi: 10.12680/balneo.2025.837
• Petrova M, Ryzhova O, Cheboksarov D, Саенко И, Sueva V, Петриков С. An outlook of early rehabilitation of stroke patients using vr technologies. Phys Rehabil Med Med Rehabil. 2023;5(2):157-66.
• Feitosa J, Fernandes C, Casseb R, Castellano G. Effects of virtual reality-based motor rehabilitation: a systematic review of fmri studies. J Neural Eng. 2022;19(1):011002.
• Nunes J, Vourvopoulos A, Blanco-Mora D, et al. Brain activation by a vr-based motor imagery and observation task: an fmri study. PLoS One. 2023;18(9):e0291528.
• Sun X, Dai C, Wu X, et al. Current implications of EEG and fNIRS as functional neuroimaging techniques for motor recovery after stroke. Med Rev (Berl). 2024;4(6):492-509.
• Ottiger B, Veerbeek J, Cazzoli D, Nyffeler T, Vanbellingen T. The flow state scale for rehabilitation tasks: a new flow experience questionnaire for stroke patients. Am J Occup Ther. 2024;78(2):7802180030. doi: 10.5014/ajot.2024.050401.
• Bok SK, Song Y, Lim A, Jin S, Kim N, Ko G. High-tech home-based rehabilitation after stroke: a systematic review and meta-analysis. J Clin Med. 2023;12(7):2668. doi:10.3390/jcm12072668.
• Munari D, Wartburg A, Garcia-Marti VG, Zadravec M, Matjačić Z, Veneman JF. Clinical feasibility of applying immersive virtual reality during robot-assisted gait training for individuals with neurological diseases: a pilot study. Brain Sci. 2024;14(10):1002. doi: 10.3390/brainsci14101002.
• Proulx CE, Higgins J, Vincent C, Vaughan T, Hewko M, Gagnon DH. User-centered development process of an operating interface to couple a robotic glove with a virtual environment to optimize hand rehabilitation following a stroke. J Rehabil Assist Technol Eng. 2023;10:20556683231166574. doi:10.1177/20556683231166574
• Fiore S, Battaglino A, Sinatti P, et al. The effectiveness of robotic rehabilitation for the functional recovery of the upper limb in post-stroke patients: a systematic review. Retos. 2023;50:91-101.
• Kiyono K, Tanabe S, Hirano S, et al. Effectiveness of robotic devices for medical rehabilitation: an umbrella review. J Clin Med. 2024;13(21):6616.
• Calabrò R, Morone G, Naro A, et al. Robot-assisted training for upper limb in stroke (robotas): an observational, multicenter study to identify determinants of efficacy. J Clin Med. 2021;10(22):5245. doi:10.3390/jcm10225245
• Vu K, Catalano J, Holder Z, et al. Enhancing upper limb rehabilitation in hospital occupational therapy using a machine learning human-robot interaction (hri) platform integrated with real-time visual feedback. Proc Int Symp Hum Factors Ergon Health Care. 2025;14(1):128-32.
• Ai Q, Liu Z, Meng W, Liu Q, Xie S. Machine learning in robot-assisted upper limb rehabilitation: a focused review. IEEE Trans Cogn Dev Syst. 2023;15(4):2053-63.
• Devittori G, Dinacci D, Romiti D, et al. Unsupervised robot-assisted rehabilitation after stroke: feasibility, effect on therapy dose, and user experience. J Neuroeng Rehabil. 2024;21(1):52. doi:10.1186/s12984-024-01347-4