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Journals / Acta Infologica / 2022 / Volume: 6 Issue: 2

Artificial Intelligence Applications in Rehabilitation Services

Rehabilitasyon Hizmetlerinde Yapay Zekâ Uygulamaları

Betül Akalın(Sağlık Bilimleri Üniversitesi Sağlık Yönetimi Anabilim Dalı)0000-0003-0402-2461
Mehmet Beşir Demirbaş(SAĞLIK BİLİMLERİ ÜNİVERSİTESİ, HAMİDİYE SAĞLIK BİLİMLERİ ENSTİTÜSÜ, SAĞLIK YÖNETİMİ (DR))0000-0002-5137-0496
Pages
—
Publication Date
2022-12-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

It will be possible to provide ease of education and reduce the possible burnout of the patient and physiotherapist during the rehabilitation process.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The world of technology is in a rapid development process.
📊
Key Findings & Data:In this process, technology has adapted to many areas, and the artificial intelligence it brings with it has become particularly useful in the field of health.
💡
Conclusion & Impact:It will be possible to provide ease of education and reduce the possible burnout of the patient and physiotherapist during the rehabilitation process.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Artificial intelligence · artificial intelligence in rehabilitation · artificial intelligence applications

Abstract

The world of technology is in a rapid development process. In this process, technology has adapted to many areas, and the artificial intelligence it brings with it has become particularly useful in the field of health. The study focuses on technological developments in rehabilitation services, which are a subbranch of health, and on the health management perspective of how AI benefits patients and health professionals. The study found that the use of artificial intelligence in the rehabilitation process has provided many benefits in terms of management, temporal, spatial and material, as well as improved quality and efficiency in health care. However, artificial intelligence practices are also effective in adapting the individual to social life by providing home rehabilitation to patients. The use of artificial intelligence in rehabilitation services will provide flexible structuring of variables such as time, intensity, continuity and speed of treatment for the healthcare provider and the patient, objective data contribution with reliable and valid user detection hardware, real-time feedback, and real-life simulation. It will be possible to provide ease of education and reduce the possible burnout of the patient and physiotherapist during the rehabilitation process.

Özet

Teknoloji dünyası hızlı bir gelişim süreci içerisindedir. Bu süreçte birçok alana uyarlanan teknoloji ve beraberinde getirdiği yapay zekâ özellikle sağlık alanında oldukça kullanışlı hale gelmiştir. Bu kapsamda yapılan çalışma, sağlığın bir alt dalı olan rehabilitasyon hizmetlerinde yaşanan teknolojik gelişmeler ile yapay zekanın hasta ve sağlık profesyonellerine ne gibi yararlar sağladığına sağlık yönetimi bakış açısıyla odaklanmaktadır. Yapılan çalışma sonucunda rehabilitasyon sürecinde yapay zekâ kullanımının yönetim açısından zamansal, mekânsal ve maddi birçok yarar sağlamasının yanı sıra sağlık hizmetlerinde kalite ve verimliliği arttırdığı görülmüştür. Bununla beraber, yapay zekâ uygulamaları hastalara evde rehabilitasyon imkânı sunarak bireyi sosyal hayata adapte etmekte de etkilidir. Rehabilitasyon hizmetlerinde yapay zekâ kullanımı ile sağlık hizmet sunucusu ve hasta için tedavinin zaman, yoğunluk, devamlılık, hız gibi değişkenlerin esnek bir biçimde yapılandırılmasının sağlanması, güvenilir ve geçerli kullanıcı algılama donanımı ile objektif veri katkısı, gerçek zamanlı geribildirim sağlanması, gerçek yaşam simülasyonu ile aktivite edilmiş eğitim kolaylığı sunması ve rehabilitasyon sürecinde hasta ile fizyoterapistin olası tükenmişliğini azaltması mümkün olacaktır.

🔖

Cite This Article

Akalın, B., Demirbaş, M. B. (2022). Artificial Intelligence Applications in Rehabilitation Services. Acta Infologica, Volume: 6 Issue: 2. https://doi.org/10.26650/acin.1068576

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References (169)

  1. Abouzari, M., Goshtasbi, K., Sarna, B., Ghavami, Y., Parker, E. M., Khosravi, P., Mostaghni, N., Jamshidi, S., Saber, T., & Djalilian, H. R. (2021). Adapting Personal Therapies Using a Mobile Application for Tinnitus Rehabilitation: A Preliminary Study. Annals of Otology, Rhinology and Laryngology, 130(6), 571-577. https://doi.org/10.1177/0003489420962818 google scholar
  2. Ajiboye, A. B., Willett, F. R., Young, D. R., Memberg, W. D., Murphy, B. A., Miller, J. P., Walter, B. L., Sweet, J. A., Hoyen, H. A., Keith, M. W., Peckham, P. H., Simeral, J. D., Donoghue, J. P., Hochberg, L. R., & Kirsch, R. F. (2017). Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: a proof-of-concept demonstration. The Lancet, 389(10081), 1821-1830. https://doi.org/10.1016/ S0140-6736(17)30601-3 google scholar
  3. Akalın, B., & Veranyurt, Ü. (2020). Sağlıkta Dijitalleşme ve Yapay Zeka. SDÜ Sağlık Yönetimi Dergisi, 2(2), 128-137. google scholar
  4. Akdemir, N., & Akkuş, Y. (2006). Rehabilitasyon ve Hemşirelik. Hacettepe Üniversitesi Hemşirelik Fakültesi Dergisi, 13(1), 82-91. google scholar
  5. Akgöbek, Ö., & Çakır, F. (2009). Veri Madenciliğinde Bir Uzman Sistem Tasarımı. Akademik Bilişim’09 - XI. Akademik Bilişim Konferansı Bildirileri, 809-813. http://ab.org.tr/ab09/kitap/akgobek_cakir_AB09.pdf google scholar
  6. Allam, A., Kostova, Z., Nakamoto, K., & Schulz, P. J. (2015). The effect of social support features and gamification on a web-based intervention for rheumatoid arthritis patients: Randomized controlled trial. Journal of Medical Internet Research, 17(1), e14. https://doi.org/10.2196/jmir.3510 google scholar
  7. Althoff, T., Sosic, R., Hicks, J. L., King, A. C., Delp, S. L., & Leskovec, J. (2017). Large-scale physical activity data reveal worldwide activity inequality. Nature, 547(7663), 336-339. https://doi.org/10.1038/nature23018 google scholar
  8. Anderson, D. (2019). Artificial Intelligence and Applications in PM&R. American Journal of Physical Medicine & Rehabilitation, 98(11), e128-e129. https://doi.org/10.1097/PHM.0000000000001171 google scholar
  9. Ang, K. K., & Guan, C. (2015). Brain-computer interface for neurorehabilitation of upper limb after stroke. Proceedings of the IEEE, 103(6), 944-953. https://doi.org/10.1109/JPROC.2015.2415800 google scholar
  10. Antonio Regalado. (2021). Elon Musk’s Neuralink is neuroscience theater | MIT Technology Review. Technology Review. https://www.technologyreview. com/2020/08/30/1007786/elon-musks-neuralink-demo-update-neuroscience-theater/ google scholar
  11. Ardan, M., Rahman, F. F., & Geroda, G. B. (2020). The influence of physical distance to student anxiety on COVID-19, Indonesia. Journal of Critical Reviews, 7(17), 1126-1132. https://doi.org/10.31838/jcr.07.17.141 google scholar
  12. Bacek, T., Moltedo, M., Langlois, K., Prieto, G. A., Sanchez-Villamanan, M. C., Gonzalez-Vargas, J., Vanderborght, B., Lefeber, D., & Moreno, J. C. (2017). BioMot exoskeleton - Towards a smart wearable robot for symbiotic human-robot interaction. IEEE International Conference on Rehabilitation Robotics, 1666-1671. https://doi.org/10.1109/ICORR.2017.8009487 google scholar
  13. Bai, J., Song, A., Xu, B., Nie, J., & Li, H. (2017). A Novel Human-Robot Cooperative Method for Upper Extremity Rehabilitation. International Journal of Social Robotics, 9(2), 265-275. https://doi.org/10.1007/s12369-016-0393-4 google scholar
  14. Barrios-Muriel, J., Romero-Sanchez, F., Alonso-Sanchez, F. J., & Salgado, D. R. (2020). Advances in orthotic and prosthetic manufacturing: A technology review. Materials, 13(2). https://doi.org/10.3390/ma13020295 google scholar
  15. Baxter, P., Morris, C., Rosenbaum, P., Paneth, N., Leviton, A., Goldstein, M., Bax, M., Colver, A., Damiano, D., Graham, H. K., Brien, G. O., & Shea, T. M. O. (2007). The Definition and Classification of Cerebral Palsy. Developmental Medicine & Child Neurology, 49, 1-44. https://doi. org/10.1111/j.1469-8749.2007.00001.x google scholar
  16. Benharref, A., & Serhani, M. A. (2014). Novel cloud and SOA-based framework for E-health monitoring using wireless biosensors. IEEE Journal of Biomedical and Health Informatics, 18(1), 46-55. https://doi.org/10.1109/JBHI.2013.2262659 google scholar
  17. Berton, A., Longo, U. G., Candela, V., Fioravanti, S., Giannone, L., Arcangeli, V., Alciati, V., Berton, C., Facchinetti, G., Marchetti, A., Schena, E., De Marinis, M. G., & Denaro, V. (2020). Virtual reality, augmented reality, gamification, and telerehabilitation: Psychological impact on orthopedic patients’ rehabilitation. Journal of Clinical Medicine, 9(8), 1-13. https://doi.org/10.3390/jcm9082567 google scholar
  18. Bidargaddi, N. P., & Sarela, A. (2008). Activity and heart rate-based measures for outpatient cardiac rehabilitation. Methods of Information in Medicine, 47(3), 208-216. https://doi.org/10.3414/ME9112 google scholar
  19. Blumrosen, G., Miron, Y., Intrator, N., & Plotnik, M. (2016). A Real-Time Kinect Signature-Based Patient Home Monitoring System. Sensors (Basel, Switzerland), 16(11). https://doi.org/10.3390/s16111965 google scholar
  20. Bockbrader, M. (2019). Upper limb sensorimotor restoration through brain-computer interface technology in tetraparesis. Current Opinion in Biomedical Engineering, 11(Figure 1), 85-101. https://doi.org/10.1016/j.cobme.2019.09.002 google scholar
  21. Bockbrader, M. A., Francisco, G., Lee, R., Olson, J., Solinsky, R., & Boninger, M. L. (2018). Brain Computer Interfaces in Rehabilitation Medicine. PM and R, 10(9), S233-S243. https://doi.org/10.1016/j.pmrj.2018.05.028 google scholar
  22. Bong, J. H., Jung, S., Park, N., Kim, S. J., & Park, S. (2020). Development of a Novel Robotic Rehabilitation System With Muscle-to-Muscle Interface. Frontiers in Neurorobotics, 14(February), 1-13. https://doi.org/10.3389/fnbot.2020.00003 google scholar
  23. Branco, M. P., Freudenburg, Z. V., Aarnoutse, E. J., Bleichner, M. G., Vansteensel, M. J., & Ramsey, N. F. (2017). Decoding hand gestures from primary somatosensory cortex using high-density ECoG. NeuroImage, 147, 130-142. https://doi.org/10.1016/j.neuroimage.2016.12.004 google scholar
  24. Brennan, D. M., Tindall, L., Theodoros, D., Brown, J., Campbell, M., Christiana, D., Smith, D., Cason, J., Lee, A., & American Telemedicine Association. (2011). A blueprint for telerehabilitation guidelines--October 2010. Telemedicine Journal and E-Health: The Official Journal of the American Telemedicine Association, 17(8), 662-665. https://doi.org/10.1089/tmj.2011.0036 google scholar
  25. Budaklı, M. T., & Yılmaz, C. (2021). Stewart platform based robot design and control for passive exercises in ankle and knee rehabilitation. Journal of the Faculty of Engineering and Architecture of Gazi University, 36(4), 1831-1846. https://doi.org/10.17341/gazimmfd.846641 google scholar
  26. Bulut, İ. C. (2019). Sağlıklı Ofis Çalışanlarında Mobil Uygulama Destekli Egzersiz Programının Ağrı, Yaşam Kalitesi ve İş Performansına Etkisi. Medipol Üniversitesi. google scholar
  27. Burgar, C. G., Lum, P. S., Erika Scremin, A. M., Garber, S. L., Machiel van der Loos, H. F., Kenney, D., & Shor, P. (2011). Robot-assisted upper-limb therapy in acute rehabilitation setting following stroke: Department of veterans affairs multisite clinical trial. Journal of Rehabilitation Research andDevelopment, 48(4), 445-458. https://doi.org/10.1682/JRRD.2010.04.0062 google scholar
  28. Büyükgöze, S. (2021). Beyin Bilgisayar Arayüzleri ve Uygulama Alanlari. google scholar
  29. Cason, J. (2009). A Pilot Telerehabilitation Program: Delivering Early Intervention Services to Rural Families. International Journal of Telerehabilitation, 1(1), 29-38. https://doi.org/10.5195/ijt.2009.6007 google scholar
  30. Chae, S. H., Kim, Y., Lee, K. S., & Park, H. S. (2020). Development and clinical evaluation of a web-based upper limb home rehabilitation system using a smartwatch and machine learning model for chronic stroke survivors: Prospective comparative study. JMIR MHealth and UHealth, 8(7). https:// doi.org/10.2196/17216 google scholar
  31. Chan, Z. Y. S., MacPhail, A. J. C., Au, I. P. H., Zhang, J. H., Lam, B. M. F., Ferber, R., & Cheung, R. T. H. (2019). Walking with head-mounted virtual and augmented reality devices: Effects on position control and gait biomechanics. PLoS ONE, 14(12), 1-14. https://doi.org/10.1371/journal.pone.0225972 google scholar
  32. Chang, Y. J., Chen, S. F., & Huang, J. Da. (2011). A Kinect-based system for physical rehabilitation: A pilot study for young adults with motor disabilities. Research in Developmental Disabilities, 32(6), 2566-2570. https://doi.org/10.1016/j.ridd.2011.07.002 google scholar
  33. Chung, S. W., Han, S. S., Lee, J. W., Oh, K. S., Kim, N. R., Yoon, J. P., Kim, J. Y., Moon, S. H., Kwon, J., Lee, H. J., Noh, Y. M., & Kim, Y. (2018). Automated detection and classification of the proximal humerus fracture by using deep learning algorithm. Acta Orthopaedica, 89(4), 468-473. https://doi.org/10.1080/17453674.2018.1453714 google scholar
  34. Cieza, A., Causey, K., Kamenov, K., Hanson, S. W., Chatterji, S., & Vos, T. (2020). Global estimates of the need for rehabilitation based on the Global Burden of Disease study 2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet, 396(10267), 2006-2017. https://doi. org/10.1016/S0140-6736(20)32340-0 google scholar
  35. Cikajlo, I., Rudolf, M., Goljar, N., Burger, H., & Matjacic, Z. (2011). Telerehabilitation using virtual reality task can improve balance in patients with stroke. Disability and Rehabilitation, 34(1), 13-18. https://doi.org/10.3109/09638288.2011.583308 google scholar
  36. Degol, J., Akhtar, A., Manja, B., & Bretl, T. (2016). Automatic grasp selection using a camera in a hand prosthesis. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2016-Octob, 431-434. https://doi.org/10.1109/EMBC.2016.7590732 google scholar
  37. D^az, I., Catalan, J. M., Badesa, F. J., Justo, X., Lledo, L. D., Ugartemendia, A., Gil, J. J., D^ez, J., & Garda-Aracil, N. (2018). Development of a robotic device for post-stroke home tele-rehabilitation. Advances in Mechanical Engineering, 10(1), 1-8. https://doi.org/10.1177/1687814017752302 google scholar
  38. Doiron-Cadrin, P., Kairy, D., Vendittoli, P. A., Lowry, V., Poitras, S., & Desmeules, F. (2016). Effects of a tele-prehabilitation program or an in-person prehabilitation program in surgical candidates awaiting total hip or knee arthroplasty: Protocol of a pilot single blind randomized controlled trial. Contemporary Clinical Trials Communications, 4, 192-198. https://doi.org/10.1016/j.conctc.2016.10.001 google scholar
  39. Dorsey, E. R., & Topol, E. J. (2016). State of Telehealth. New England Journal of Medicine, 375(2), 154-161. https://doi.org/10.1056/nejmra1601705 google scholar
  40. Duret, C., Grosmaire, A. G., & Krebs, H. I. (2019). Robot-assisted therapy in upper extremity hemiparesis: Overview of an evidence-based approach. Frontiers in Neurology, 10(APR), 1-8. https://doi.org/10.3389/fneur.2019.00412 google scholar
  41. Edgerton, V. R., & Roy, R. R. (2009). Robotic Training and Spinal Cord Plasticity. NIH-PA Author Manuscript, 78(1), 4-12. https://doi.org/10.1016/j.brainresbull.2008.09.018.Robotic google scholar
  42. Eriksson, L., Lindström, B., & Ekenberg, L. (2011). Patients’ experiences of telerehabilitation at home after shoulder joint replacement. Journal of Telemedicine and Telecare, 17(1), 25-30. https://doi.org/10.1258/jtt.2010.100317 google scholar
  43. Fan, Y. J., Yin, Y. H., Xu, L. Da, Zeng, Y., & Wu, F. (2014). IoT-based smart rehabilitation system. IEEE Transactions on Industrial Informatics, 10(2), 1568-1577. https://doi.org/10.1109/TII.2014.2302583 google scholar
  44. Fernandez-Llatas, C., & Garcfa-Gömez, J. M. (2014). Data mining in clinical medicine. In Data Mining in Clinical Medicine (Vol. 1246, pp. 1-267). https://doi.org/10.1007/978-1-4939-1985-7 google scholar
  45. Ferreira, C. M., & Serpa, S. (2018). Society 5.0 and Social Development: Contributions to a Discussion. Management and Organizational Studies, 5(4), 26. https://doi.org/10.5430/mos.v5n4p26 google scholar
  46. Fico, G., Fioravanti, A., Arredondo, M. T., Gorman, J., Diazzi, C., Arcuri, G., Conti, C., & Pirini, G. (2016). Integration of personalized healthcare pathways in an ICT platform for diabetes managements: A small-scale exploratory study. IEEE Journal of Biomedical and Health Informatics, 20(1), 29-38. https://doi.org/10.1109/JBHI.2014.2367863 google scholar
  47. Foong, R., Tang, N., Chew, E., Chua, K. S. G., Ang, K. K., Quek, C., Guan, C., Phua, K. S., Kuah, C. W. K., Deshmukh, V. A., Yam, L. H. L., & Rajeswaran, D. K. (2020). Assessment of the Efficacy of EEG-Based MI-BCI with Visual Feedback and EEG Correlates of Mental Fatigue for Upper-Limb Stroke Rehabilitation. IEEE Transactions on Biomedical Engineering, 67(3), 786-795. https://doi.org/10.1109/TBME.2019.2921198 google scholar
  48. Frederix, I., Hansen, D., Coninx, K., Vandervoort, P., Vandijck, D., Hens, N., Van Craenenbroeck, E., Van Driessche, N., & Dendale, P. (2016). Effect of comprehensive cardiac telerehabilitation on one-year cardiovascular rehospitalization rate, medical costs and quality of life: A cost-effectiveness analysis. European Journal of Preventive Cardiology, 23(7), 674-682. https://doi.org/10.1177/2047487315602257 google scholar
  49. Friedenberg, D. A., Schwemmer, M., Skomrock, N., Sederberg, P., Ting, J., & Sharma, G. (2018). Neural Decoding Requirements for a Take-home Brain Computer Interface. August, 43210. google scholar
  50. Fukuyama, M. (2018). Society 5.0: Aiming for a New Human-Centered Society. Japan SPOTLIGHT, August, 47-50. https://www.jef.or.jp/journal/pdf/220th_Special_Article_02.pdf google scholar
  51. Galna, B., Jackson, D., Schofield, G., McNaney, R., Webster, M., Barry, G., Mhiripiri, D., Balaam, M., Olivier, P., & Rochester, L. (2014). Retraining google scholar
  52. function in people with Parkinson’s disease using the Microsoft kinect: Game design and pilot testing. Journal of NeuroEngineering and Rehabilitation,11 (1), 1-12. https://doi.org/10.1186/1743-0003-11-60 google scholar
  53. Geman, O., Sanei, S., Costin, H., & Eftaxias, K. (2015). Challenges And Trends In Ambıent Assısted Lıvıng and Intellıgent Tools For Dısabled And Elderly People. IWCIM — Computational Intelligence for Multimedia Understanding, 1, 0-4. google scholar
  54. Gerke, S., Babic, B., Evgeniou, T., & Cohen, I. G. (2020). The need for a system view to regulate artificial intelligence/machine learning-based software as medical device. Npj Digital Medicine, 3(1), 1-4. https://doi.org/10.1038/s41746-020-0262-2 google scholar
  55. Ghwanmeh, S., Mohammad, A., & Al-Ibrahim, A. (2013). Innovative Artificial Neural Networks-Based Decision Support System for Heart Diseases Diagnosis. Journal ofIntelligent Learning Systems and Applications, 05(03), 176-183. https://doi.org/10.4236/jilsa.2013.53019 google scholar
  56. Giorgino, T., Tormene, P., Maggioni, G., Pistarini, C., & Quaglini, S. (2009). Wireless support to poststroke rehabilitation: MyHearts neurological rehabilitation concept. IEEE Transactions on Information Technology in Biomedicine, 13(6), 1012-1018. https://doi.org/10.1109/TITB.2009.2028020 google scholar
  57. Gotsis, M., Tasse, A., Swider, M., Lympouridis, V., Poulos, I. C., Thin, A. G., Turpin, D., Tucker, D., & Jordan-Marsh, M. (2012). Mixed reality game prototypes for upper body exercise and rehabilitation. Proceedings - IEEE Virtual Reality, 181-182. https://doi.org/10.1109/VR.2012.6180940 google scholar
  58. Green, R. A. (2021). Possibilities in bioelectronics: Super humans or science fiction? APL Bioengineering, 5(4), 040401. https://doi.org/10.1063/5.0079530 google scholar
  59. Grossi, E. (2011). Artificial Neural Networks and Predictive Medicine: a Revolutionary Paradigm Shift. Artificial Neural Networks - Methodological Advances and Biomedical Applications. https://doi.org/10.5772/15810 google scholar
  60. Hailey, D., Roine, R., Ohinmaa, A., & Dennett, L. (2011). Evidence in routine care: a systematic review. Journal of Telemedicine and Telecare, 17(6), 281-287. google scholar
  61. Hamida, S. T. Ben, Hamida, E. Ben, & Ahmed, B. (2015). A new mHealth communication framework for use in wearable WBANs and mobile technologies. In Sensors (Switzerland) (Vol. 15, Issue 2). https://doi.org/10.3390/s150203379 google scholar
  62. Hasson, C. J., Caldwell, G. E., & Emmerik, R. E. A. Van. (2008). Impulsive choice and environmental enrichment: Effects of d-amphetamine and methylphenidate. Behavioural Brain Research, 193(1), 48-54. https://doi.org/10.1177/0278364907084588.Design google scholar
  63. Hausdorff, J. M. (2005). Gait variability: methods, modeling and meaning Example of Increased Stride Time Variability in Elderly Fallers Quantification of Stride-to-Stride Fluctuations. 9, 1-9. https://doi.org/10.1186/1743-Received google scholar
  64. Hazar, Y. (2020). Giyilebilir Dış İskelet El. In Batman Üniversitesi. google scholar
  65. Helmstaedter, M., Briggman, K. L., Turaga, S. C., Jain, V., Seung, H. S., & Denk, W. (2013). Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature, 500(7461), 168-174. https://doi.org/10.1038/nature12346 google scholar
  66. Holden, M. K. (2005). Virtual environments for motor rehabilitation: Review. Cyberpsychology and Behavior, 8(3), 187-211. https://doi.org/10.1089/ cpb.2005.8.187 google scholar
  67. Holden, M. K., Dyar, T. A., & Dayan-Cimadoro, L. (2007). Telerehabilitation using a virtual environment improves upper extremity function in patients with stroke. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 15(1), 36-42. https://doi.org/10.1109/TNSRE.2007.891388 google scholar
  68. Hsieh, Y. W., Wu, C. Y., Liao, W. W., Lin, K. C., Wu, K. Y., & Lee, C. Y. (2011). Effects of treatment intensity in upper limb robot-assisted therapy for chronic stroke: A pilot randomized controlled trial. Neurorehabilitation and Neural Repair, 25(6), 503-511. https://doi.org/10.1177/1545968310394871 google scholar
  69. Hu, M., Cheng, H. J., Ji, F., Chong, J. S. X., Lu, Z., Huang, W., Ang, K. K., Phua, K. S., Chuang, K. H., Jiang, X., Chew, E., Guan, C., & Zhou, J. H. (2021). Brain Functional Changes in Stroke Following Rehabilitation Using Brain-Computer Interface-Assisted Motor Imagery With and Without tDCS: A Pilot Study. Frontiers in Human Neuroscience, 15(July). https://doi.org/10.3389/fnhum.2021.692304 google scholar
  70. Iosa, M., Morone, G., Fusco, A., Castagnoli, M., Romana Fusco, F., Pratesi, L., & Paolucci, S. (2015). Leap motion controlled videogame-based therapy for rehabilitation of elderly patients with subacute stroke: A feasibility pilot study. Topics in Stroke Rehabilitation, 22(4), 306-316. https://doi. org/10.1179/1074935714Z.0000000036 google scholar
  71. Jayaraman, A., O’brien, M. K., Madhavan, S., Mummidisetty, C. K., Roth, H. R., Hohl, K., Tapp, A., Brennan, K., Kocherginsky, M., Williams, K. J., Takahashi, H., & Rymer, W. Z. (2019). Stride management assist exoskeleton vs functional gait training in stroke: A randomized trial. American Academy ofNeurology, 92(3), E1-E11. https://doi.org/10.1212/WNL.0000000000006782 google scholar
  72. Jiang, R., Kleer, R., & Piller, F. T. (2017). Predicting the future of additive manufacturing: A Delphi study on economic and societal implications of 3D printing for 2030. Technological Forecasting and Social Change, 117(January), 84-97. https://doi.org/10.1016/j.techfore.2017.01.006 google scholar
  73. Joe, S., Totaro, M., Wang, H., & Beccai, L. (2021). Development of the ultralight hybrid pneumatic artificial muscle: Modelling and optimization. PLoS ONE, 16, 1-21. https://doi.org/10.1371/journal.pone.0250325 google scholar
  74. Jopkiewicz, S., & Jopkiewicz, A. (2021). Innovations in the dimension of communication in health sector and the perspective of Society 5.0. Scientific Papers of Silesian University of Technology. Organization and Management Series, 2021(150), 47-56. https://doi. org/10.29119/1641-3466.2021.150.4 google scholar
  75. Jumphoo, T., Uthansakul, M., Duangmanee, P., Khan, N., & Uthansakul, P. (2021). Soft robotic glove controlling using brainwave detection for continuous rehabilitation at home. Computers, Materials and Continua, 66(1), 961-976. https://doi.org/10.32604/cmc.2020.012433 google scholar
  76. Kadivar, Z., Sullivan, J. L., Eng, D. P., Pehlivan, A. U., O’Malley, M. K., Yozbatiran, N., & Francisco, G. E. (2011). Robotic training and kinematic analysis of arm and hand after incomplete spinal cord injury: A case study. IEEE International Conference on Rehabilitation Robotics. https://doi.org/10.1109/ ICORR.2011.5975429 google scholar
  77. Kansal, V., Ranjan, R., Sinha, S., Tiwari, R., & Wickramasinghe, N. (2021). Healthcare and Knowledge Management for Society 5.0. In Healthcare and Knowledge Management for Society 5.0. https://doi.org/10.1201/9781003168638 google scholar
  78. Kara, G. (2019). Hemiparetik Bireylerde Denge Düzeyinin Belirlenmesi: Yapay Sinir Ağları Uygulaması. Pamukkale Üniversitesi. google scholar
  79. Kara, G., Altuğ, F., Kavaklıoğlu, K., & Cavlak, U. (2020). Nörolojik rehabilitasyonda yapay sinir ağı uygulamaları. 45(4), 1844-1846. google scholar
  80. Kawasaki, S., Ohata, K., Yoshida, T., Yokoyama, A., & Yamada, S. (2020). Gait improvements by assisting hip movements with the robot in children with cerebral palsy: A pilot randomized controlled trial. Journal of NeuroEngineering and Rehabilitation, 17(1), 1-8. https://doi.org/10.1186/ s12984-020-00712-3 google scholar
  81. Khor, W. S., Baker, B., Amin, K., Chan, A., Patel, K., & Wong, J. (2016). Augmented and virtual reality in surgery-the digital surgical environment: Applications, limitations and legal pitfalls. Annals of Translational Medicine, 4(23), 1-10. https://doi.org/10.21037/atm.2016.12.23 google scholar
  82. Kim, Y. S., Shi, H., Dagalakis, N., Marvel, J., & Cheok, G. (2019). Design of a six-DOF motion tracking system based on a Stewart platform and ball-and-socket joints. Mechanism and Machine Theory, 133, 84-94. https://doi.org/10.1016/j.mechmachtheory.2018.10.021 google scholar
  83. Kinikli, G. İ., Eden, A., & Cavlak, U. (2017). Fizyoterapi ve Rehabilitasyon Eğitiminde Simülasyon Uygulamaları. Turkiye Klinikleri J Med Educ-Special Topics, 2(2), 104-110. google scholar
  84. Knight, C., Alderman, N., & Burgess, P. W. (2002). Development of a simplified version of the multiple errands test for use in hospital settings. Neuropsychological Rehabilitation, 12(3), 231-255. https://doi.org/10.1080/09602010244000039 google scholar
  85. Koh, G., Ho, W., Koh, Y. Q., Lim, D., Tay, A., Yen, S.-C., Kumar, Y., Wong, S. M., Cai, V., Cheong, A., Koh, K., Png, C., Ng, Y. S., & Hoenig, H. (2017). A Time Motion Analysis of Outpatient, Home and Telerehabilitation Sessions From Patient and Therapist Perspectives. Archives of Physical Medicine and Rehabilitation, 98(10), e28. https://doi.org/10.1016/j.apmr.2017.08.087 google scholar
  86. Köse, B. (2018). Türkiye’de ve Dünyada Mesleki Rehabilitasyon. Turkiye Klinikleri J Psychol-Special Topics, 3(1), 30-41. https://www.researchgate.net/ publication/324133065 google scholar
  87. Krausz, N. E., & Hargrove, L. J. (2019). A survey of teleceptive sensing for wearable assistive robotic devices. Sensors (Switzerland), 19(23), 1-27. https:// doi.org/10.3390/s19235238 google scholar
  88. Krebs, H. I., Volpe, B. T., Williams, D., Celestino, J., Charles, S. K., Lynch, D., & Hogan, N. (2007). Robot-aided neurorehabilitation: A robot for wrist rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 15(3), 327-335. https://doi.org/10.1109/TNSRE.2007.903899 google scholar
  89. Kreilinger, A., Kaiser, V., Rohm, M., Rupp, R., & Müller-Putz, G. R. (2013). BCI and FES Training of a Spinal Cord Injured End-User to Control a Neuroprosthesis. Biomedical Engineering / Biomedizinische Technik, 58, 7-8. https://doi.org/10.1515/bmt-2013-4443 google scholar
  90. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. PervasiveHealth: Pervasive Computing Technologies for Healthcare, 25, 1-9. https://doi.org/10.1145/3383972.3383975 google scholar
  91. Krogue, J. D., Cheng, K. V., Hwang, K. M., Toogood, P., Meinberg, E. G., Geiger, E. J., Zaid, M., McGill, K. C., Patel, R., Sohn, J. H., Wright, A., Darger, B. F., Padrez, K. A., Ozhinsky, E., Majumdar, S., & Pedoia, V. (2020). Automatic Hip Fracture Identification and Functional Subclassification with Deep Learning. Radiology: Artificial Intelligence, 2(2), e190023. https://doi.org/10.1148/ryai.2020190023 google scholar
  92. Kübler, A., & Birbaumer, N. (2008). Brain-computer interfaces and communication in paralysis: Extinction of goal directed thinking in completely paralysed patients? Clinical Neurophysiology, 119(11), 2658-2666. https://doi.org/10.1016/j.clinph.2008.06.019 google scholar
  93. Kuether, J., Moore, A., Kahan, J., Martucci, J., Messina, T., Perreault, R., Sembler, R., Tarutis, J., Zazulak, B., Rubin, L. E., & O’Connor, M. I. (2019). Telerehabilitation for Total Hip and Knee Arthroplasty Patients: A Pilot Series with High Patient Satisfaction. HSS Journal, 15(3), 221-225. https:// doi.org/10.1007/s11420-019-09715-w google scholar
  94. Kwakkel, G., Van Peppen, R., Wagenaar, R. C., Dauphinee, S. W., Richards, C., Ashburn, A., Miller, K., Lincoln, N., Partridge, C., Wellwood, I., & Langhorne, P. (2004). Effects of augmented exercise therapy time after stroke: A meta-analysis. Stroke, 35(11), 2529-2536. https://doi.org/10.1161/01. STR.0000143153.76460.7d google scholar
  95. Langan, J., Bhattacharjya, S., Subryan, H., Xu, W., Chen, B., Li, Z., & Cavuoto, L. (2020). In-home rehabilitation using a smartphone app coupled with 3D printed functional objects: Single-subject design study. JMIR MHealth and UHealth, 8(7), 1-12. https://doi.org/10.2196/19582 google scholar
  96. Laver, K., George, S., Thomas, S., Deutsch, J. E., & Crotty, M. (2012). Virtual reality for stroke rehabilitation. Stroke, 43(2). https://doi.org/10.1161/ STROKEAHA.111.642439 google scholar Lecun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539 google scholar
  97. Lee, M., Lee, S. H., Kim, T. Y., Yoo, H. J., Kim, S. H., Suh, D. W., Son, J., & Yoon, B. C. (2016). Feasibility of a Smartphone-Based Exercise Program for Office Workers With Neck Pain: An Individualized Approach Using a Self-Classification Algorithm. Archives of Physical Medicine and Rehabilitation, 98(1), 80-87. https://doi.org/10.1016/j.apmr.2016.09.002 google scholar
  98. Lewis, G. N., Woods, C., Rosie, J. A., & Mcpherson, K. M. (2011). Virtual reality games for rehabilitation of people with stroke: Perspectives from the users. Disability and Rehabilitation: Assistive Technology, 6(5), 453-463. https://doi.org/10.3109/17483107.2011.574310 google scholar
  99. Lincoln, N. B., Parry, R. H., & Vass, C. D. (1999). Randomized, controlled trial to evaluate increased intensity of physiotherapy treatment of arm function after stroke. Stroke, 30(3), 573-579. https://doi.org/10.1161/01.STR.30.3.573 google scholar
  100. Lind, A., Akbarian, E., Olsson, S., Nâsell, H., Sköldenberg, O., Razavian, A. S., & Gordon, M. (2021). Artificial intelligence for the classification of fractures around the knee in adults according to the 2018 AO/OTA classification system. PLoS ONE, 16(4 April), 1-15. https://doi.org/10.1371/journal. pone.0248809 google scholar
  101. Liu, F., Guan, B., Zhou, Z., Samsonov, A., Rosas, H., Lian, K., Sharma, R., Kanarek, A., Kim, J., Guermazi, A., & Kijowski, R. (2019). Fully Automated Diagnosis of Anterior Cruciate Ligament Tears on Knee MR Images by Using Deep Learning. Radiology: Artificial Intelligence, 1(3), 180091. https:// doi.org/10.1148/ryai.2019180091 google scholar
  102. Liu, F., Zhou, Z., Samsonov, A., Blankenbaker, D., Larison, W., Kanarek, A., Lian, K., Kambhampati, S., & Kijowski, R. (2018). Deep learning approach for evaluating knee MR images: Achieving high diagnostic performance for cartilage lesion detection. Radiology, 289(1), 160-169. https://doi. org/10.1148/radiol.2018172986 google scholar
  103. Liu, X., & Wiersma, R. D. (2019). Optimization based trajectory planning for real-time 6DoF robotic patient motion compensation systems. PLoS ONE, 14 (1), 1-16. https://doi.org/10.1371/journal.pone.0210385 google scholar
  104. Llorens, R., Gil-Gomez, J. A., Mesa-Gresa, P., Alcaniz, M., Colomer, C., & Noe, E. (2011). BioTrak: A comprehensive overview. 2011 International Conference on Virtual Rehabilitation, ICVR 2011. https://doi.org/10.1109/ICVR.2011.5971843 google scholar
  105. Lo, K., Stephenson, M., & Lockwood, C. (2017). Effectiveness of robotic assisted rehabilitation for mobility and functional ability in adult stroke patients: a systematic review. JBI Database of Systematic Reviews and Implementation Reports, 15(12), 3049-3091. https://doi.org/10.11124/JBISRIR-2017-003456 google scholar
  106. Lockery, D., Peters, J. F., Ramanna, S., Shay, B. L., & Szturm, T. (2011). Store-and-feedforward adaptive gaming system for hand-finger motion tracking in telerehabilitation. IEEE Transactions on Information Technology in Biomedicine, 15(3), 467-473. https://doi.org/10.1109/TITB.2011.2125976 google scholar
  107. Lohse, K. R., Hilderman, C. G. E., Cheung, K. L., Tatla, S., & Van Der Loos, H. F. M. (2014). Virtual reality therapy for adults post-stroke: A systematic review and meta-analysis exploring virtual environments and commercial games in therapy. PLoS ONE, 9(3). https://doi.org/10.1371/journal. pone.0093318 google scholar
  108. Lorenzini, M. C., & Wittich, W. (2019). Measuring changes in device use of a head-mounted low vision aid after personalised telerehabilitation: Protocol for a feasibility study. BMJ Open, 9(9), 1-10. https://doi.org/10.1136/bmjopen-2019-030149 google scholar
  109. Lou, E., Hill, D. L., Raso, J. V., Moreau, M. J., & Mahood, J. K. (2005). Smart orthosis for the treatment of adolescent idiopathic scoliosis. Medical and Biological Engineering and Computing, 43(6), 746-750. https://doi.org/10.1007/BF02430952 google scholar
  110. Martm-Moreno, J., Ruiz-Fernandez, D., Soriano-Paya, A., & Berenguer-Miralles, V. J. (2008). Monitoring 3D movements for the rehabilitation of joints in physiotherapy. Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS’08 -“Personalized Healthcare through Technology,” 300000, 4836-4839. https://doi.org/10.1109/iembs.2008.4650296 google scholar
  111. Mathieson, K., Denison, T., & Winkworth-Smith, C. (2021). A transformative roadmap for neurotechnology in the UK. A Transformative Roadmap for Neurotechnology in the UK. https://ktn-uk.org/wp-content/uploads/2021/06/A-transformative-roadmap-for-neurotechnology-in-the-UK.pdf google scholar
  112. Mekki, M., Delgado, A. D., Fry, A., Putrino, D., & Huang, V. (2018). Robotic Rehabilitation and Spinal Cord Injury: a Narrative Review. Neurotherapeutics, 15(3), 604-617. https://doi.org/10.1007/s13311-018-0642-3 google scholar
  113. Mohammadi, A., Lavranos, J., Zhou, H., Mutlu, R., Alici, G., Tan, Y., Choong, P., & Oetomo, D. (2020). A practical 3D-printed soft robotic prosthetic hand with multi-articulating capabilities. PLoS ONE, 15(5), 1-23. https://doi.org/10.1371/journal.pone.0232766 google scholar
  114. Moon, S., Ahmadnezhad, P., Song, H. J., Thompson, J., Kipp, K., Akinwuntan, A. E., & Devos, H. (2020). Artificial neural networks in neurorehabilitation: A scoping review. NeuroRehabilitation, 46(3), 259-269. https://doi.org/10.3233/NRE-192996 google scholar
  115. Müller-Putz, G. R., Ofner, P., Schwarz, A., Pereira, J., Luzhnica, G., Di Sciascio, C., Veas, E., Stein, S., Williamson, J., Murray-Smith, R., Escolano, C., Montesano, L., Hessing, B., Schneiders, M., & Rupp, R. (2017). Moregrasp: Restoration of Upper Limb Function in Individuals with High Spinal Cord Injury by Multimodal Neuroprostheses for Interaction in Daily Activities. 7th Graz Brain-Computer Interface Conference, 338-343. google scholar
  116. Negrillo-Cardenas, J., Jimenez-Perez, J. R., & Feito, F. R. (2020). The role of virtual and augmented reality in orthopedic trauma surgery: From diagnosis to rehabilitation. Computer Methods and Programs in Biomedicine, 191. https://doi.org/10.1016/j.cmpb.2020.105407 google scholar
  117. Nicolas, L. F., & Gil, J. G. (2012). Brain computer interfaces, a review. Sensors, 12(2), 1211-1279. https://doi.org/10.3390/s120201211 google scholar
  118. Novak, D., & Riener, R. (2015). A survey of sensor fusion methods in wearable robotics. Robotics and Autonomous Systems, 73(September), 155-170. https://doi.org/10.1016/j.robot.2014.08.012 google scholar
  119. Nowakowski, P. R. (2017). Bodily processing: The role of morphological computation. Entropy, 19(7). https://doi.org/10.3390/e19070295 google scholar
  120. Olczak, J., Emilson, F., Razavian, A., Antonsson, T., Stark, A., & Gordon, M. (2020). Ankle fracture classification using deep learning: automating detailed AO Foundation/Orthopedic Trauma Association (AO/OTA) 2018 malleolar fracture identification reaches a high degree of correct classification. Acta Orthopaedica, 92(1), 102-108. https://doi.org/10.1080/17453674.2020.1837420 google scholar
  121. Onose, G., Grozea, C., Anghelescu, A., Daia, C., Sinescu, C. J., Ciurea, A. V., Spircu, T., Mirea, A., Andone, I., Spânu, A., Popescu, C., Mihâescu, A. S., Fazli, S., Danoczy, M., & Popescu, F. (2012). On the feasibility of using motor imagery EEG-based brain-computer interface in chronic tetraplegics for assistive robotic arm control: A clinical test and long-term post-trial follow-up. Spinal Cord, 50(8), 599-608. https://doi.org/10.1038/sc.2012.14 google scholar
  122. Özturk, K., & Şahin, M. (2018). Yapay sinir ağları ve yapay zekâya genel bir bakış. Takvim-i Vekayi, 6(2), 25-36. google scholar
  123. Page, S. J., Schmid, A., & Harris, J. E. (2012). Optimizing terminology for stroke motor rehabilitation: Recommendations from the american congress of rehabilitation medicine stroke movement interventions subcommittee. Archives of Physical Medicine and Rehabilitation, 93(8), 1395-1399. https:// doi.org/10.1016/j.apmr.2012.03.005 google scholar
  124. Pareto, L., Johansson, B., Ljungberg, C., Zeller, S., Sunnerhagen, K. S., Rydmark, M., & Broeren, J. (2011). Telehealth with 3D games for stroke rehabilitation. International Journal on Disability and Human Development, 10(4), 373-377. https://doi.org/10.1515/IJDHD.2011.062 google scholar
  125. Pastor, I., Hayes, H. A., & Bamberg, S. J. M. (2012). A feasibility study of an upper limb rehabilitation system using Kinect and computer games. Conference Proceedings:. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2012, 1286-1289. google scholar
  126. Pee, L. G., Pan, S. L., & Cui, L. (2019). Artificial intelligence in healthcare robots: A social informatics study of knowledge embodiment. Journal of the Association for Information Science and Technology, 70(4), 351-369. https://doi.org/10.1002/asi.24145 google scholar
  127. Pehlivan, A. U., Sergi, F., Erwin, A., Yozbatiran, N., Francisco, G. E., & O’Malley, M. K. (2014). Design and validation of the RiceWrist-S exoskeleton for robotic rehabilitation after incomplete spinal cord injury. Robotica, 32(8), 1415-1431. https://doi.org/10.1017/S0263574714001490 google scholar
  128. Peretti, A., Amenta, F., Tayebati, S. K., Nittari, G., & Mahdi, S. S. (2017). Telerehabilitation: Review of the state-of-the-art and areas of application. JMIR Rehabilitation and Assistive Technologies, 4(2), 1-9. https://doi.org/10.2196/rehab.7511 google scholar
  129. Pokorny, C., Klobassa, D. S., Pichler, G., Erlbeck, H., Real, R. G. L., Kübler, A., Lesenfants, D., Habbal, D., Noirhomme, Q., Risetti, M., Mattia, D., & Müller-Putz, G. R. (2013). The auditory P300-based single-switch brain-computer interface: Paradigm transition from healthy subjects to minimally conscious patients. ArtificialIntelligence inMedicine, 59(2), 81-90. https://doi.org/10.1016Zj.artmed.2013.07.003 google scholar
  130. Pool, D., Elliott, C., Bear, N., Donnelly, C. J., Davis, C., Stannage, K., & Valentine, J. (2016). Neuromuscular electrical stimulation-assisted gait increases muscle strength and volume in children with unilateral spastic cerebral palsy. Developmental Medicine and Child Neurology, 58(5), 492-501. https:// doi.org/10.1111/dmcn.12955 google scholar
  131. Poole, D. L., Mackworth, A., & Goebel, R. G. (1998). Computational Intelligence and Knowledge. In Computational Intelligence: A Logical Approach (pp. 1-22). https://www.cs.ubc.ca/~poole/ci.html google scholar
  132. Portillo-Lara, R., Tahirbegi, B., Chapman, C. A. R., Goding, J. A., & Green, R. A. (2021). Mind the gap: State-of-the-art technologies and applications for EEG-based brain-computer interfaces. APL Bioengineering, 5(3), 1-16. https://doi.org/10.1063/5.0047237 google scholar
  133. Riener, R. (2012). Rehabilitation robotics. In An Introduction to Rehabilitation Engineering (Vol. 3, Issues 1-2). https://doi.org/10.1561/2300000028 google scholar Rizzo, A., & Kim, G. J. (2005). A SWOT Analysis of the Field of Virtual Reality Rehabilitation and Therapy. Frontiers in Robotics and AI, 14(2), 119-146. https://doi.org/10.3389/frobt.2019.00101 google scholar
  134. Roblot, V., Giret, Y., Bou Antoun, M., Morillot, C., Chassin, X., Cotten, A., Zerbib, J., & Fournier, L. (2019). Artificial intelligence to diagnose meniscus tears on MRI. Diagnostic and Interventional Imaging, 100(4), 243-249. https://doi.org/10.1016/j.diii.2019.02.007 google scholar
  135. Rodda, J., & Graham, H. K. (2001). Classification of gait patterns in spastic hemiplegia and spastic diplegia: A basis for a management algorithm. European Journal ofNeurology, 8(03), 98-108. https://doi.org/10.1046/j.1468-1331.2001.00042.x google scholar
  136. Rolim, C. O., Koch, F. L., Westphall, C. B., Werner, J., Fracalossi, A., & Salvador, G. S. (2010). A cloud computing solution for patient’s data collection in health care institutions. 2nd International Conference on EHealth, Telemedicine, and Social Medicine, ETELEMED 2010, Includes MLMB 2010; BUSMMed 2010, iii, 95-99. https://doi.org/10.1109/eTELEMED.2010.19 google scholar
  137. Russell, S., & Norvig, P. (2010). Artificial intelligence (A modern approach). In 2010 The 2nd International Conference on Computer and Automation Engineering, ICCAE 2010 (Vol. 4). https://doi.org/10.1109/ICCAE.2010.5451578 google scholar
  138. Sanchez, J. C., Mahmoudi, B., DiGiovanna, J., & Principe, J. C. (2009). Exploiting co-adaptation for the design of symbiotic neuroprosthetic assistants. Neural Networks, 22(3), 305-315. https://doi.org/10.1016/j.neunet.2009.03.015 google scholar
  139. Saracino, L., Avizzano, C. A., Ruffaldi, E., Cappiello, G., Curto, Z., & Scoglio, A. (2016). MOTORE++ a portable haptic device for domestic rehabilitation. IECON Proceedings (Industrial Electronics Conference), 728-734. https://doi.org/10.1109/IECON.2016.7793115 google scholar
  140. Sarsak, H. I. (2020). Telerehabilitation services: a successful paradigm for occupational therapy clinical services? International Physical Medicine & Rehabilitation Journal, 5(2). https://doi.org/10.15406/ipmrj.2020.05.00237 google scholar
  141. Shih, J. J., Krusienski, D. J., & Wolpaw, J. R. (2012). Brain-computer interfaces in medicine. Mayo Clinic Proceedings, 87(3), 268-279. https://doi. org/10.1016/j.mayocp.2011.12.008 google scholar
  142. Singh, S., & Ramakrishna, S. (2017). Biomedical applications of additive manufacturing: Present and future. Current Opinion in Biomedical Engineering, 2, 105-115. https://doi.org/10.1016/j.cobme.2017.05.006 google scholar
  143. Society, R. (2019). IHuman Blurring lines between mind and machine. google scholar
  144. Soekadar, S. R., Witkowski, M., Gomez, C., Opisso, E., Medina, J., Cortese, M., Cempini, M., Carrozza, M. C., Cohen, L. G., Birbaumer, N., & Vitiello, N. (2016). Hybrid EEG/EOG-based brain/neural hand exoskeleton restores fully independent daily living activities after quadriplegia. Science Robotics, 1(1). https://doi.org/10.1126/scirobotics.aag3296 google scholar Song, A., Wu, C., Ni, D., Li, H., & Qin, H. (2016). One-Therapist to Three-Patient Telerehabilitation Robot System for the Upper Limb after Stroke. International Journal ofSocial Robotics, 8(2), 319-329. https://doi.org/10.1007/s12369-016-0343-1 google scholar
  145. Spina, G., Huang, G., Vaes, A., Spruit, M., & Amft, O. (2013). COPDTrainer: A smartphone-based motion rehabilitation training system with real-time acoustic feedback. UbiComp 2013 - Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 597-606. https://doi.org/10.1145/2493432.2493454 google scholar
  146. Sullivan, E., & Barnes, D. (2007). Relationships among functional outcome measures used for assessing children with ambulatory CP. Developmental Medicine and Child Neurology, 49(5), 338-344. http://www.embase.com/search/results?subaction=viewrecord&from=export&id=L46730684%5Cnhttp:// dx.doi.org/10.1111/j.1469-8749.2007.00338.x%5Cnhttp://limo.libis.be/resolver?&sid=EMBASE&issn=00121622&id=doi:10.1111%2Fj.1469-8749.2007.00338.x&atitle=Relationsh google scholar
  147. Sunarti, S., Fadzlul Rahman, F., Naufal, M., Risky, M., Febriyanto, K., & Masnina, R. (2021). Artificial intelligence in healthcare: opportunities and risk for future. Gaceta Sanitaria, 35, S67-S70. https://doi.org/10.1016/j.gaceta.2020.12.019 google scholar
  148. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 07-12-June, 1-9. https://doi.org/10.1109/ CVPR.2015.7298594 google scholar
  149. Tanaka, N., Matsushita, S., Sonoda, Y., Maruta, Y., Fujitaka, Y., Sato, M., Simomori, M., Onaka, R., Harada, K., Hirata, T., Kinoshita, S., Okamoto, T., & Okamura, H. (2019). Effect of Stride Management Assist Gait Training for Poststroke Hemiplegia: A Single Center, Open-Label, Randomized Controlled Trial. Journal ofStroke and Cerebrovascular Diseases, 28(2), 477-486. https://doi.org/10.1016/j.jstrokecerebrovasdis.2018.10.025 google scholar
  150. Tang, S., Ghosh, R., Thakor, N. V., & Kukreja, S. L. (2016). Orientation estimation and grasp type detection of household objects for upper limb prostheses with dynamic vision sensor. Proceedings - 2016 IEEE Biomedical Circuits and Systems Conference, BioCAS 2016, 1(c), 99-102. https://doi.org/10.1109/ BioCAS.2016.7833734 google scholar
  151. Tanzi, L., Vezzetti, E., Moreno, R., Aprato, A., Audisio, A., & Masse, A. (2020). Hierarchical fracture classification of proximal femur X-Ray images using a multistage Deep Learning approach. European Journal ofRadiology, 133(October). https://doi.org/10.1016/j.ejrad.2020.109373 google scholar
  152. Tarakci, D. (2015). Pediatrik Rehabilitasyonda Oyun Konsolları ile Sanal Gerçeklik Uygulamaları (Issue August). google scholar
  153. Tarakçı, D. (2021, June 3). SD PLATFORM - Dergi - Rehabilitasyonda yapay zekâ. SD Platform. https://www.sdplatform.com/Dergi/1420/Rehabilitasyonda-yapay-zek.aspx google scholar
  154. TDK. (2021). zekâ ne demek TDK Sözlük Anlamı. https://sozluk.gov.tr/ google scholar
  155. Then, J. W., Shivdas, S., Tunku Ahmad Yahaya, T. S., Ab Razak, N. I., & Choo, P. T. (2020). Gamification in rehabilitation of metacarpal fracture using cost-effective end-user device: A randomized controlled trial. Journal of Hand Therapy, 33(2), 235-242. https://doi.org/10.1016/jjht.2020.03.029 google scholar
  156. Thomas, T. M., Candrea, D. N., Fifer, M. S., McMullen, D. P., Anderson, W. S., Thakor, N. V., & Crone, N. E. (2019). Decoding native cortical representations for flexion and extension at upper limb joints using electrocorticography. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(2), 293-303. https://doi.org/10.1109/TNSRE.2019.2891362 google scholar
  157. Thompson, M. K., Moroni, G., Vaneker, T., Fadel, G., Campbell, R. I., Gibson, I., Bernard, A., Schulz, J., Graf, P., Ahuja, B., & Martina, F. (2016). Design for Additive Manufacturing: Trends, opportunities, considerations, and constraints. CIRP Annals - Manufacturing Technology, 65(2), 737-760. https:// doi.org/10.1016/j.cirp.2016.05.004 google scholar
  158. Tonet, O., Marinelli, M., Citi, L., Rossini, P. M., Rossini, L., Megali, G., & Dario, P. (2008). Defining brain-machine interface applications by matching interface performance with device requirements. Journal ofNeuroscience Methods, 167(1), 91-104. https://doi.org/10.1016/j.jneumeth.2007.03.015 google scholar
  159. Tousignant, M., Moffet, H., Boissy, P., Corriveau, H., Cabana, F., & Marquis, F. (2011). A randomized controlled trial of home telerehabilitation for post-knee arthroplasty. Journal of Telemedicine and Telecare, 17(4), 195-198. https://doi.org/10.1258/jtt.2010.100602 google scholar
  160. Vall, M. Du. (2019). A superintelligent people-centered society, a few words about the idea of Society 5.0. Panstwo i Spoleczenstwo, 2, 11-31. https://doi. org/10.34697/2451-0858-pis-2019-2-001 google scholar
  161. van Dokkum, L. E. H., Ward, T., & Laffont, I. (2015). Brain computer interfaces for neurorehabilitation-its current status as a rehabilitation strategy post-stroke. Annals ofPhysical and Rehabilitation Medicine, 58(1), 3-8. https://doi.org/10.1016/j.rehab.2014.09.016 google scholar
  162. Vanmulken, D. A. M. M., Spooren, A. I. F., Bongers, H. M. H., & Seelen, H. A. M. (2015). Robot-assisted task-oriented upper extremity skill training in cervical spinal cord injury: A feasibility study. Spinal Cord, 53(7), 547-551. https://doi.org/10.1038/sc.2014.250 google scholar
  163. Wang, J., Chen, H., Liang, H., Wang, W., Liang, Y., Liang, Y., & Zhang, Y. (2019). Low-frequency fluctuations amplitude signals exhibit abnormalities of intrinsic brain activities and reflect cognitive impairment in leukoaraiosis patients. Medical Science Monitor, 25, 5219-5228. https://doi.org/10.12659/ MSM.915528 google scholar
  164. Weiss, P. L., Sveistrup, H., Rand, D., & Kizony, R. (2009). Video capture virtual reality: A decade of rehabilitation assessment and intervention. Physical Therapy Reviews, 14(5), 307-321. https://doi.org/10.1179/108331909X12488667117339 google scholar
  165. Yang, S., MacLachlan, R. A., & Riviere, C. N. (2015). Manipulator design and operation of a six-degree-of-freedom handheld tremor-canceling microsurgical instrument. IEEE/ASME Transactions on Mechatronics, 20(2), 761-772. https://doi.org/10.1109/TMECH.2014.2320858 google scholar
  166. Yiğit, P. (2011). Yapay Sinir Ağları ve Kredi Taleplerinin Değerlendirilmesi Üzerine Bir Uygulama. İstanbul Üniversitesi. google scholar
  167. Brennan, D. M., Tindall, L., Theodoros, D., Brown, J., Campbell, M., Christiana, D., Smith, D., Cason, J., Lee, A., & American Telemedicine Association. (2011). A blueprint for telerehabilitation guidelines--October 2010. Telemedicine Journal and E-Health: The Official Journal of the American Telemedicine Association, 17(8), 662-665. https://doi.org/10.1089/tmj.2011.0036 google scholar
  168. Hausdorff, J. M. (2005). Gait variability: methods, modeling and meaning Example of Increased Stride Time Variability in Elderly Fallers Quantification of Stride-to-Stride Fluctuations. 9, 1-9. https://doi.org/10.1186/1743-Received google scholar
  169. Pastor, I., Hayes, H. A., & Bamberg, S. J. M. (2012). A feasibility study of an upper limb rehabilitation system using Kinect and computer games. Conference Proceedings:. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2012, 1286-1289. google scholar