TECHNOLOGICAL REHABILITATION PHILOSOPHY

Year 2020, Volume: 6 Issue: 1, 18 - 23, 07.01.2020

Furkan Çakır , Neslihan Karabacak , Zubeyir Sarı

Abstract

Technological rehabilitation includes robotic and wearable devices, virtual reality applications, three-dimensional motion analysis systems and e-health and mobile health applications. Our aim was to determine the framework of the philosophy and aims of rehabilitation technology.
These systems have been developed to achieve objective and reliable results, to shape treatment sessions and to improve quality, reduce labor and cost. As the demand for therapy is expected to increase in the future, the technology that will enable patients to receive training with minimal therapist time consumption has an important role. E-health and mobile health systems can be utilized effectively in data generation, storage, transportation, analysis, sharing and security. Robotic devices, on the other hand, are the equipments that come to the forefront in rehabilitation applications with the development of technology. These devices help to make objective, reliable analysis by recording kinetic and kinematic data. Another example of technological rehabilitation is virtual reality (VR) applications. In these systems, by making use of virtual games and visual and audio feedback, it is aimed to get the task and many repetitions as motivated. Finally, optical systems are commonly used in motion analysis and are accepted as the gold standard. They require experienced personnel skills and sufficient laboratory space.
In the studies, it has been concluded that it has made significant contributions in terms of speed, efficiency, accessibility and cost. With such technologies, patients can exercise more often, resulting in better results and faster progress in motor (re) learning.
Although positive results are obtained in the current studies, the development of these systems continues and it is aimed to increase the further studies.

Keywords

Technology, Innovation, Robotics, Virtual Reality

References

• http://www.acces.nysed.gov/vr/137000-rehabilitation-technology-policy Technology -Related Assistance for Individuals with Disabilities – Access Date:30.05.2019 Işik, A. H., & Güler, İ. (2010). Teletıpta Mobil Uygulama Çalışması ve Mobil İletişim Teknolojilerinin Analizi. International Journal of Informatics Technologies, 3(1). Krebs HI, Hogan N, Aisen ML, Volpe BT: Robot-aided neurorehabilitation. IEEE Trans Rehabil Eng 1998, 6(1):75-87. Krebs HI, P JJ, Dipietro L, Ferraro M, Volpe BT, Hogan N: Rehabilitation Robotics: Performance-Based Progressive Robot-Assisted Therapy. Autonomous Robots 2003, 15.7-20. Johnson MJ, Loos HF Van der, Burgar CG, Shor P, Leifer LJ: Experimental results using force-feedback cueing in robot-assisted stroke therapy. IEEE Trans Neural Syst Rehabil Eng 2005, 13(3):335-348. Merians AS, Jack D, Boian R, Tremaine M, Burdea GC, Adamovich SV, Recce M, Poizner H: Virtual reality-augmented rehabilitation for patients following stroke. Phys Ther 2002, 82(9):898-915. http://ec.europa.eu/health/ehealth/policy/index_en.htm eHealth : Digital health and care – Access Date:30.05.2019 Hanaylı, M. C., Serbest, S., & Ürekli, T. (2015). Otizmli Çocukların Sosyal Becerilerini Geliştirmeye Yönelik Android Uygulaması. XVII. Akademik Bilişim Konferansı. Eskişehir: Anadolu Üniversitesi. Güler, E., & Eby, G. (2015). Akıllı Ekranlarda Mobil Sağlık Uygulamaları. Eğitim ve Öğretim Araştırmaları Dergisi, 4(3), 45-51. Kılıç, T. (2017). e-Sağlık, İyi Uygulama Örneği; Hollanda. Gümüşhane Üniversitesi Sağlık Bilimleri Dergisi, 6(3), 203-217. Garcia, N., Sabater-Navarro, J. M., Gugliemeli, E., & Casals, A. (2011). Trends in rehabilitation robotics. Bertomeu-Motos, A., Lledó, L., Díez, J., Catalan, J., Ezquerro, S., Badesa, F., & Garcia-Aracil, N. (2015). Estimation of human arm joints using two wireless sensors in robotic rehabilitation tasks. Sensors, 15(12), 30571-30583. Mehrholz, J., & Pohl, M. (2012). Electromechanical-assisted gait training after stroke: a systematic review comparing end-effector and exoskeleton devices. Journal of rehabilitation medicine, 44(3), 193-199. Bruni, M. F., Melegari, C., De Cola, M. C., Bramanti, A., Bramanti, P., & Calabrò, R. S. (2018). What does best evidence tell us about robotic gait rehabilitation in stroke patients: a systematic review and meta-analysis. Journal of Clinical Neuroscience, 48, 11-17. Krebs, H. I., & Hogan, N. (2012). Robotic therapy: the tipping point. American journal of physical medicine & rehabilitation/Association of Academic Physiatrists, 91(11 0 3), S290. Hammami, N., Coroian, F. O., Julia, M., Amri, M., Mottet, D., Hérisson, C., & Laffont, I. (2012). Isokinetic muscle strengthening after acquired cerebral damage: a literature review. Annals of physical and rehabilitation medicine, 55(4), 279-291. Mehrholz, J., Hädrich, A., Platz, T., Kugler, J., & Pohl, M. (2012). Electromechanical and robot‐assisted arm training for improving generic activities of daily living, arm function, and arm muscle strength after stroke. Cochrane database of systematic reviews, (6). Forrester, L. W., Roy, A., Goodman, R. N., Rietschel, J., Barton, J. E., Krebs, H. I., & Macko, R. F. (2013). Clinical application of a modular ankle robot for stroke rehabilitation. NeuroRehabilitation, 33(1), 85-97. Reinkensmeyer, D. J., & Boninger, M. L. (2012). Technologies and combination therapies for enhancing movement training for people with a disability. Journal of neuroengineering and rehabilitation, 9(1), 17. Laffont, I., Bakhti, K., Coroian, F., Van Dokkum, L., Mottet, D., Schweighofer, N., & Froger, J. (2014). Innovative technologies applied to sensorimotor rehabilitation after stroke. Annals of physical and rehabilitation medicine, 57(8), 543-551. Lourenço, F., Postolache, O., & Postolache, G. (2018, May). Tailored virtual reality and mobile application for motor rehabilitation. In 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) (pp. 1-6). IEEE. Kim, M., Jeon, C., & Kim, J. (2017). A study on immersion and presence of a portable hand haptic system for immersive virtual reality. Sensors, 17(5), 1141. Witmer, B. G., & Singer, M. J. (1998). Measuring presence in virtual environments: A presence questionnaire. Presence, 7(3), 225-240. Corbetta, D., Imeri, F., & Gatti, R. (2015). Rehabilitation that incorporates virtual reality is more effective than standard rehabilitation for improving walking speed, balance and mobility after stroke: a systematic review. Journal of physiotherapy, 61(3), 117-124. Lledó, L. D., Díez, J. A., Bertomeu-Motos, A., Ezquerro, S., Badesa, F. J., Sabater-Navarro, J. M., & García-Aracil, N. (2016). A comparative analysis of 2D and 3D tasks for virtual reality therapies based on robotic-assisted neurorehabilitation for post-stroke patients. Frontiers in aging neuroscience, 8, 205. Doyon, J., & Benali, H. (2005). Reorganization and plasticity in the adult brain during learning of motor skills. Current opinion in neurobiology, 15(2), 161-167. Ravi, D. K., Kumar, N., & Singhi, P. (2017). Effectiveness of virtual reality rehabilitation for children and adolescents with cerebral palsy: an updated evidence-based systematic review. Physiotherapy, 103(3), 245-258. Petraglia, F., Scarcella, L., Pedrazzi, G., Brancato, L., Puers, R., & Costantino, C. (2018). Inertial sensors versus standard systems in gait analysis: a systematic review and meta-analysis. Eur J Phys Rehabil Med.