Neurorehabilitation Robot-Assisted for Stroke Recovery: Hybrid Exoskeleton Assistive Limb (HEAL)
Abstract
Conventional rehabilitation techniques that require manual intervention and the use of devices are identified as having several drawbacks. These include limited features, fatigue for both patients and therapists during prolonged rehabilitation sessions, time-consuming procedures, high operational and maintenance costs for devices, a lack of motivation for patients, limited accessibility, and challenges in measuring or monitoring rehabilitation progress. In response to these challenges, the Hybrid Exoskeleton Assistive Limb (HEAL) is introduced as a tailored solution with distinctive features. These features include real-time electromyographic (EMG) monitoring, a therapist-friendly graphical interface, and advanced techniques in the rehabilitation process. HEAL utilizes robotics-assisted rehabilitation for repetitive, precise, and controlled movements, enhancing brain-muscle motor function, developing muscle strength, and providing a wide range of motion. The system focuses on upper limb robotic rehabilitation and consists of an EMG to read muscle responses, an Arduino microcontroller for signal processing, and a high-torque precision servo motor for controlling limb movements. HEAL emphasizes the Brain-Muscle-Computer control process rather than passive rehabilitation, which relies on external forces to move the muscles, as demonstrated by the therapist. HEAL is particularly suitable for neurorehabilitation, emphasizing recovery and improvement of function in individuals with neurological disorders or injuries, especially in stroke patients. HEAL's ability to tailor rehabilitation programs individually offers personalized rehabilitation, considering each patient's unique needs, goals, and abilities. It utilizes advanced technologies for targeted and efficient rehabilitation. The HEAL device is cost-saving with a compact design, positioning it as a promising and comprehensive solution in stroke neurorehabilitation.
Keywords
References
K. S. Tan and N. Venketasubramanian, “Stroke Burden in Malaysia,” Cerebrovasc Dis Extra, vol. 12, no. 2, pp. 58–62, Mar. 2022, doi: 10.1159/000524271.
“Stroke | National Institute of Neurological Disorders and Stroke.” Accessed: Dec. 14, 2023. [Online]. Available: https://www.ninds.nih.gov/health-information/disorders/stroke
F. D. N. A. P. Shelton and M. J. Reding, “Effect of Lesion Location on Upper Limb Motor Recovery After Stroke,” Stroke, vol. 32, no. 1, pp. 107–112, 2001, doi: 10.1161/01.STR.32.1.107.
M. Taketomi, Y. Shimizu, H. Kadone, Y. Hada, and M. Yamazaki, “Hybrid Assistive Limb Intervention for Hemiplegic Shoulder Dysfunction Due to Stroke,” Cureus, Nov. 2021, doi: 10.7759/cureus.19827.
Y. Sankai, “HAL: Hybrid assistive limb based on cybernics,” Springer Tracts in Advanced Robotics, vol. 66, no. STAR, pp. 25–34, 2010, doi: 10.1007/978-3-642-14743-2_3/COVER.
D. J. Reinkensmeyer, L. Marchal-Crespo, and V. Dietz, “Neurorehabilitation Technology,” 2022.
P. Groshaw and I. Hardiman, “GENERALO*ELEC~TRIC HARD IMAN I ARM TEST HARDIMAN I PROTOTYPE PROJECT,” 1969.
“The world’s first Wearable Cyborg ‘HAL’ - CYBERDYNE.” Accessed: Dec. 14, 2023. [Online]. Available: https://www.cyberdyne.jp/english/products/HAL/
Y. Mao and S. K. Agrawal, “Transition from mechanical arm to human arm with CAREX: A cable driven ARm EXoskeleton (CAREX) for neural rehabilitation,” 2012 IEEE International Conference on Robotics and Automation, pp. 2457–2462, 2012, [Online]. Available: https://api.semanticscholar.org/CorpusID:15906506
P. L. Gribble, D. J. Ostry, V. Sanguineti, and R. Laboissière, “Are Complex Control Signals Required for Human Arm Movement?,” J Neurophysiol, vol. 79, no. 3, pp. 1409–1424, Mar. 1998, doi: 10.1152/jn.1998.79.3.1409.
M. G. Catalano, G. Grioli, M. Garabini, F. Bonomo, M. Mancini,N. Tsagarakis, and A. Bicchi, “Vsa-cubebot: a modular variable stiffness platform for multiple degrees of freedom robots,” in IEEE International Conference on Robotics and Automation(ICRA 2011), pp. 5090–5095, IEEE, 2011.
G. Kasman and S. Wolf, “SURFACE EMG MADE EASY: A Beginner’s Guide for Rehabilitation Clinicians,” 2002. [Online]. Available: www.noraxon.com
C. Della Santina et al., “Controlling Soft Robots: Balancing Feedback and Feedforward Elements,” IEEE Robot Autom Mag, vol. 24, no. 3, pp. 75–83, Sep. 2017, doi: 10.1109/MRA.2016.2636360.
Lin, T.H., Putranto, A., Chen, P.H., Teng, Y.Z. and Chen, L., 2023. High-mobility inchworm climbing robot for steel bridge inspection. Automation in Construction, 152, p.104905.
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Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
ISSN 2089-3272
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