Rehabilitasyon Hizmetlerinde Yapay Zekâ Uygulamaları

Betül Akalın; Mehmet Beşir Demirbaş

doi:10.26650/acin.1068576

TR EN

Rehabilitasyon Hizmetlerinde Yapay Zekâ Uygulamaları

Öz

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.

Anahtar Kelimeler

Artificial Intelligence Applications in Rehabilitation Services

Öz

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.

Anahtar Kelimeler

Kaynakça

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
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
Akalın, B., & Veranyurt, Ü. (2020). Sağlıkta Dijitalleşme ve Yapay Zeka. SDÜ Sağlık Yönetimi Dergisi, 2(2), 128-137. google scholar
Akdemir, N., & Akkuş, Y. (2006). Rehabilitasyon ve Hemşirelik. Hacettepe Üniversitesi Hemşirelik Fakültesi Dergisi, 13(1), 82-91. google scholar
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
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
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
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

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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Bulut, İ. C. (2019). Sağlıklı Ofis Çalışanlarında Mobil Uygulama Destekli Egzersiz Programının Ağrı, Yaşam Kalitesi ve İş Performansına Etkisi. Medipol Üniversitesi. google scholar
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
Büyükgöze, S. (2021). Beyin Bilgisayar Arayüzleri ve Uygulama Alanlari. google scholar
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Galna, B., Jackson, D., Schofield, G., McNaney, R., Webster, M., Barry, G., Mhiripiri, D., Balaam, M., Olivier, P., & Rochester, L. (2014). Retraining google scholar
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
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
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
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
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
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
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
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
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
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
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
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
Hazar, Y. (2020). Giyilebilir Dış İskelet El. In Batman Üniversitesi. google scholar
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
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
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
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
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
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
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
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
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
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
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
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
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
Kara, G. (2019). Hemiparetik Bireylerde Denge Düzeyinin Belirlenmesi: Yapay Sinir Ağları Uygulaması. Pamukkale Üniversitesi. google scholar
Kara, G., Altuğ, F., Kavaklıoğlu, K., & Cavlak, U. (2020). Nörolojik rehabilitasyonda yapay sinir ağı uygulamaları. 45(4), 1844-1846. google scholar
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Nowakowski, P. R. (2017). Bodily processing: The role of morphological computation. Entropy, 19(7). https://doi.org/10.3390/e19070295 google scholar
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
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
Özturk, K., & Şahin, M. (2018). Yapay sinir ağları ve yapay zekâya genel bir bakış. Takvim-i Vekayi, 6(2), 25-36. google scholar
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Society, R. (2019). IHuman Blurring lines between mind and machine. google scholar
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
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
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
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
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
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
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
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
Tarakci, D. (2015). Pediatrik Rehabilitasyonda Oyun Konsolları ile Sanal Gerçeklik Uygulamaları (Issue August). google scholar
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
TDK. (2021). zekâ ne demek TDK Sözlük Anlamı. https://sozluk.gov.tr/ google scholar
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
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
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
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
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
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
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
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
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
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
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
Yiğit, P. (2011). Yapay Sinir Ağları ve Kredi Taleplerinin Değerlendirilmesi Üzerine Bir Uygulama. İstanbul Üniversitesi. google scholar

Ayrıntılar

Birincil Dil

Türkçe

Konular

Bilgisayar Yazılımı

Bölüm

Araştırma Makalesi

Yazarlar

Betül Akalın
0000-0003-0402-2461
Türkiye

Mehmet Beşir Demirbaş ^*
0000-0002-5137-0496
Türkiye

Yayımlanma Tarihi

31 Aralık 2022

Gönderilme Tarihi

5 Şubat 2022

Kabul Tarihi

18 Ağustos 2022

Yayımlandığı Sayı

Yıl 2022 Cilt: 6 Sayı: 2

DOI

https://doi.org/10.26650/acin.1068576

IZ

https://izlik.org/JA66LK43DC

Kaynak Göster

RIS / Bibtex

APA

Akalın, B., & Demirbaş, M. B. (2022). Rehabilitasyon Hizmetlerinde Yapay Zekâ Uygulamaları. Acta Infologica, 6(2), 141-161. https://doi.org/10.26650/acin.1068576

AMA

1.Akalın B, Demirbaş MB. Rehabilitasyon Hizmetlerinde Yapay Zekâ Uygulamaları. ACIN. 2022;6(2):141-161. doi:10.26650/acin.1068576

Chicago

Akalın, Betül, ve Mehmet Beşir Demirbaş. 2022. “Rehabilitasyon Hizmetlerinde Yapay Zekâ Uygulamaları”. Acta Infologica 6 (2): 141-61. https://doi.org/10.26650/acin.1068576.

EndNote

Akalın B, Demirbaş MB (01 Aralık 2022) Rehabilitasyon Hizmetlerinde Yapay Zekâ Uygulamaları. Acta Infologica 6 2 141–161.

IEEE

[1]B. Akalın ve M. B. Demirbaş, “Rehabilitasyon Hizmetlerinde Yapay Zekâ Uygulamaları”, ACIN, c. 6, sy 2, ss. 141–161, Ara. 2022, doi: 10.26650/acin.1068576.

ISNAD

Akalın, Betül - Demirbaş, Mehmet Beşir. “Rehabilitasyon Hizmetlerinde Yapay Zekâ Uygulamaları”. Acta Infologica 6/2 (01 Aralık 2022): 141-161. https://doi.org/10.26650/acin.1068576.

JAMA

1.Akalın B, Demirbaş MB. Rehabilitasyon Hizmetlerinde Yapay Zekâ Uygulamaları. ACIN. 2022;6:141–161.

MLA

Akalın, Betül, ve Mehmet Beşir Demirbaş. “Rehabilitasyon Hizmetlerinde Yapay Zekâ Uygulamaları”. Acta Infologica, c. 6, sy 2, Aralık 2022, ss. 141-6, doi:10.26650/acin.1068576.

Vancouver

1.Betül Akalın, Mehmet Beşir Demirbaş. Rehabilitasyon Hizmetlerinde Yapay Zekâ Uygulamaları. ACIN. 01 Aralık 2022;6(2):141-6. doi:10.26650/acin.1068576