Hamos J. Armin, Birouas Ionut Flaviu, Anton Daniel Melentie, Tarca Radu Catalin*
University of Oradea, Faculty of Managerial and Technological Engineering, Department of Mechatronics, Oradea, Romania
* Corresponding author. E-mail: rtarca@uoradea.ro
Robotica & Management, Vol. 27, No. 1, pp. 04-08
DOI: https://doi.org/10.24193/rm.2022.1.1
Abstract: The paper deals with a short review regarding neurorehabilitation technologies for regaining human hand mobility functions after a cerebrovascular accident or stroke. The aim of this paper is to form a general understanding of the current technologies used in the field of neurorehabilitation and highlight key characteristics, advantages and disadvantages. Technologies that are studies include robot exoskeletons, electro stimulation, brain computer interfaces (BCI), EEG and limb mounted sensors. After a presenting a summary of current existing technologies, a brief conclusion proposing the future direction of this study is proposed.
Keywords: Neurorehabilitation, neuroplasticity, exoskeleton, EMG, BCI.
References
[1] Elliott K. C., Bundy D. T., Guggenmos D. J., Nudo R. J.: “Physiological basis of neuromotor recovery”, in Rehabilitation Robotics, Elsevier, 2018, pp. 1–13.
[2] Huang X., Naghdy F., Naghdy G., Du H., Todd C.: “The Combined Effects of Adaptive Control and Virtual Reality on Robot-Assisted Fine Hand Motion Rehabilitation in Chronic Stroke Patients: A Case Study”, J. Stroke Cerebrovasc. Dis., vol. 27, no. 1, pp. 221–228, 2018.
[3] Riccio A. et al.: “Chapter 12 – Interfacing brain with computer to improve communication and rehabilitation after brain damage”, in Brain-Computer Interfaces: Lab Experiments to Real-World Applications, vol. 228, D. B. T.-P. in B. R. Coyle, Ed. Elsevier, 2016, pp. 357–387.
[4] Tamburin S., Smania N., Saltuari L., Hoemberg V., Sandrini G.: “Editorial: New advances in neurorehabilitation”, Front. Neurol., vol. 10, no. OCT, p. 1090, 2019.
[5] Semprini M. et al.: “Technological Approaches for Neurorehabilitation: From Robotic Devices to Brain Stimulation and Beyond”, Front. Neurol., vol. 9, no. APR, p. 1, Apr. 2018.
[6] Naseer N., Ayaz H., Dehais F.: “Portable and Wearable Brain Technologies for Neuroenhancement and Neurorehabilitation”, Biomed Res. Int., vol. 2018, Jun. 2018.
[7] Piradov M. A.,. Chernikova L. A, Suponeva N. A.: “Brain Plasticity and Modern Neurorehabilitation Technologies”, Her. Russ. Acad. Sci. 2018 882, vol. 88, no. 2, pp. 111–118, May 2018.
[8] Deng W., Papavasileiou I., Qiao Z., Zhang W., Lam K. Y., Han S.: “Advances in Automation Technologies for Lower Extremity Neurorehabilitation: A Review and Future Challenges”, IEEE Rev. Biomed. Eng., vol. 11, pp. 289–305, May 2018.
[9] Ogul O. E., Coskunsu D. K., Akcay S., Akyol K., Hanoglu L., Ozturk N.: “The effect of Electromyography (EMG)-driven Robotic Treatment on the recovery of the hand Nine years after stroke”, J. Hand Ther., Apr. 2021.
[10] Fasoli S. E.: “Rehabilitation Technologies to Promote Upper Limb Recovery after Stroke”, Stroke Rehabil., pp. 486–510, Jan. 2016.
[11] Baniqued P.D.E. et al.: “Brain–computer interface robotics for hand rehabilitation after stroke: a systematic review”, J. NeuroEngineering Rehabil. 2021 181, vol. 18, no. 1, pp. 1–25, Jan. 2021.
[12] Gomes P.: “Medical robotics: Minimally invasive surgery”, in Medical Robotics: Minimally Invasive Surgery, Elsevier, 2012, pp. 1–301.
[13] Wendong W. et al.: “Design and verification of a human–robot interaction system for upper limb exoskeleton rehabilitation”, Med. Eng. Phys., vol. 79, pp. 19–25, May 2020.
[14] Chatterjee R., Datta A., Sanyal D.K.: “Chapter 8 – Ensemble Learning Approach to Motor Imagery EEG Signal Classification”, in Machine Learning in Bio-Signal Analysis and Diagnostic Imaging, N. Dey, S. Borra, A. S. Ashour, and F. Shi, Eds. Academic Press, 2019, pp. 183–208.
[15] Furui A., Igaue T., Tsuji T.: “EMG pattern recognition via Bayesian inference with scale mixture-based stochastic generative models”, Expert Syst. Appl., vol. 185, p. 115644, Dec. 2021.
[16] Kumar J.S., Bhuvaneswari P.: “Analysis of electroencephalography (EEG) signals and its categorization – A study”, in Procedia Engineering, 2012, vol. 38, pp. 2525–2536.
[17] Scherbakov N., Von Haehling S., Anker S.D., Dirnagl U., Doehner W.: “Stroke induced Sarcopenia: muscle wasting and disability after stroke”, Int. J. Cardiol., vol. 170, no. 2, pp. 89–94, Dec. 2013.
[18] English C., McLennan H., Thoirs K., Coates A., Bernhardt J.: “Loss of skeletal muscle mass after stroke: A systematic review”, Int. J. Stroke, vol. 5, no. 5, pp. 395–402, Oct. 2010.
[19] Bao S.C., Khan A., Song R., Tong R.K.Y.: “Rewiring the Lesioned Brain: Electrical Stimulation for Post-Stroke Motor Restoration”, J. Stroke, vol. 22, no. 1, p. 47, Jan. 2020.
[20] Lin Z., Yan T.: “Long-term effectiveness of neuromuscular electrical stimulation for promoting motor recovery of the upper extremity after stroke”, J. Rehabil. Med., vol. 43, no. 6, pp. 506–510, May 2011.
[21] You G., Liang H., Yan T.: “Functional electrical stimulation early after stroke improves lower limb motor function and ability in activities of daily living”, NeuroRehabilitation, vol. 35, no. 3, pp. 381–389, Nov. 2014.
[22] *** IEEE Staff: “2015 IEEE International Conference on Rehabilitation Robotics (ICORR 2015) Pages 1-513”, no. August, 2015.
[23] Tacchino G., Gandolla M., Coelli S., Barbieri R., Pedrocchi A., Bianchi A.M.: “EEG Analysis during Active and Assisted Repetitive Movements: Evidence for Differences in Neural Engagement”, IEEE Trans. Neural Syst. Rehabil. Eng., vol. 25, no. 6, pp. 761–771, Jun. 2017.
[24] Chowdhury A., Raza H., Dutta A., Prasad G.: “EEG-EMG based hybrid brain computer interface for triggering hand exoskeleton for neuro-rehabilitation”, ACM Int. Conf. Proceeding Ser., vol. Part F132085, Jun. 2017.