Properties of Mechatronic System for Hand Rehabilitation

• Autorzy: Tutak Jacek Stanisław , Gurak Szymon

Identyfikatory

DOI

10.12913/22998624/100344

• Języki publikacji: EN

Abstrakty

EN

The article describes an innovative mechatronic device for hand rehabilitation, which enables diagnostics, comprehensive exercises and reporting the rehabilitation results of individual fingers of people who have lost their full efficiency as a result of past illnesses (i.a. stroke) and orthopedic injuries. The basic purpose of the device is to provide controlled, active exercises of the individual fingers, to widen the range of their movements, and to increase their precision of movement. The developed mechatronic device works with original software for PCs containing a diagnostic module, reporting module and a set of virtual reality exercises using biofeedback. The device uses auditory and visual biofeedback, and electromyography (EMG).

Słowa kluczowe

EN

mechatronic device; EMG; cerebral stroke; hand; rehabilitation

PL

urządzenie mechatroniczne; EMG; wylew krwi do mózgu; dłoń; rehabilitacja

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2018
• Tom: Vol. 12, no 4
• Strony: 103--114
• Opis fizyczny: Bibliogr. 24 poz., fig.

Twórcy

autor

Tutak Jacek Stanisław

• The Faculty of Mechanical Engineering and Aeronautics, Rzeszow University of Technology, Al. Powstańców Warszawy 8, 35-959 Rzeszów, Poland

autor

Gurak Szymon

• The Faculty of Mechanical Engineering and Aeronautics, Rzeszow University of Technology, Al. Powstańców Warszawy 8, 35-959 Rzeszów, Poland

Bibliografia

• 1. Bhagat N.A., French J., Venkatakrishnan A., Yozbatiran N., Francisco G.E. and O’Malley M.K. Detecting movement intent from scalp EEG in a novel upper limb robotic rehabilitation system for stroke. Conf Proc IEEE Eng Med Biol Soc, 2014, 4127–4130.
• 2. Budzik G., Turek P. and Traciak J. The influence of change in slice thickness on the accuracy of reconstruction of cranium geometry. J Engineering in Medicine, 231(3), 2017, 197–202.
• 3. Choi J., Jung C., Kim Y., et al. Virtual coupling triggering for interaction force reduction of haptic free-motion using surface EMG. Int. J. Precis. Eng. Manuf., 18(7), 2017, 1013–1020.
• 4. Dobkin B.H. Strategies for stroke rehabilitation. Lancet Neurol, 3(9), 2004, 528–536.
• 5. Gierlak P., Kurc K. and Szybicki D. Mobile crawler robot vibration analysis in the contexts of motion speed selection. Journal of Vibroengineering, 19(4), 2017, 2403–2412.
• 6. Hendzel Z., Burghardt A. and Gierlak P. Onventional and fuzzy force control in robotised machining. Solid State Phenomena. Trans Tech Publications, 210, 2014, 178–185.
• 7. Hurkmans H.L., Ribbers G.M. and Streur-Kranenburg M.F. Energy expenditure in chronic stroke patients playing Wii Sports: a pilot study. Journal of NeuroEngineering and Rehabilitation, 8, 2011, 38–45.
• 8. Kudasik T., Libura M., Markowska O. and Miechowicz S. Methods for designing and fabrication large-size medical models for orthopaedics. Bulletin of the Polish Academy of Sciences Technical Sciences, 63(3), 2015, 623–627.
• 9. Kudasik T., Libura M., Markowska O. and Miechowicz S. Methods of reconstructing complex multi-structural anatomical objects with RP techniques. Bulletin of the Polish Academy of Sciences Technical Sciences, 64(2), 2016, 315–323,
• 10. Kurc K., Szybicki D., Burghardt A. and Muszyńska M. The application of virtual prototyping methods to determine the dynamic parameters of mobile robot. Open Engineering, 6(1), 2016, 55–63.
• 11. Kurillo G., Bajd T. and Kamnik R. Static analysis of two-fingered grips. Journal of automatic control, 12(1), 2002, 38–45.
• 12. Kutlu M., Freeman C.T. and Hallewell E. Upper-limb stroke rehabilitation using electrode-array based functional electrical stimulation with sensing and control innovations. Med Eng Phys., 38(4), 2016, 366–379.
• 13. Moon S.B., Ji Y.H., Jang H.Y. et al. Gait analysis of hemiplegic patients in ambulatory rehabilitation training using a wearable lower-limb robot: A pilot study. Int. J. Precis. Eng. Manuf., 18(12), 2017, 1773–1781.
• 14. Pouya M. and Pashaki P.V. Optimal Design of Fractional Sliding Mode Control Based on Multi- Objective Genetic Algorithm for a Two-Link Flexible Manipulator Adv. Sci. Technol. Res. J. 2017; 11(3):56–65
• 15. Resquín F., Gómez A.C. and Gonzalez-Vargas J. Hybrid robotic systems for upper limb rehabilitation after stroke: A review. Med Eng Phys., 38(11), 2016, 1279–1288.
• 16. Selles R.W., Michielsen M.E. and Bussmann J.B. Effects of a mirror-induced visual illusion on a reaching task in stroke patients: implications for mirror therapy training. Neurorehabil Neural Repair, 28(7), 2014, 652–659.
• 17. Sheng B., Zhang Y. and Meng W. Bilateral robots for upper-limb stroke rehabilitation: State of the art and future prospects. Med Eng Phys., 38(7), 2016, 587–606.
• 18. Shing Lo H. and Quan Xie S. Exoskeleton robots for upper-limb rehabilitation: State of the art and future prospects. Med Eng Phys., 34(3), 2012, 261–268.
• 19. Triwiyanto T., Wahyunggoro O., Nugroho H.A. and Herianto H. Evaluating the performance of Kalman filter on elbow joint angle prediction based on electromyography. Int. J. Precis. Eng. Manuf., 18(12), 2017, 1739–1748.
• 20. Tutak J.S. Design of ELISE robot for the paretic upper limb of stroke survivors. Journal of Vibroengineering, 18(6), 2016, 4069–4085.
• 21. Tutak J.S. Virtual reality and exercises for paretic upper limb of stroke survivors. Tehnički vjesnik – Tehnical Gazette, 24(2), 2016, 451–458.
• 22. Tutak J.S. and Kołodziej W. Device to rehabilitate one’s Physical and Learning Abilities. Tehnički vjesnik – Tehnical Gazette, 25(4), 2018, 1059–1066.
• 23. Yozbatiran N., Berliner J., O’Malley M.K. and Pehlivan A.U. Robotic training and clinical assessment of upper extremity movements after spinal cord injury: a single case report. J Rehabil Med., 44(2), 2012, 186–188.
• 24. Yozbatiran N. and Cramer S.C. Imaging Motor Recovery after Stroke. NeuroRx. 3(4), 2006, 482–488.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).