Designing Minimalistic Powered Arm Orthosis for Brachial Plexus Injuries

Authors

  • A. V. Kotsiubailo National Technical University of Ukraine ``Igor Sikorsky Kyiv Polytechnic Institute'', Kyiv, Ukraine
  • A. V. Savchuk National Technical University of Ukraine ``Igor Sikorsky Kyiv Polytechnic Institute'', Kyiv, Ukraine
  • D. V. Omelkyna Ukrainian Catholic University, Lviv, Ukraine
  • S. Ye. Shoferystov Vprovadzhuvalna Eksperymentalna Laboratoriya Limited liability company, Kyiv, Ukraine
  • Ia. I. Lavrenko National Technical University of Ukraine ``Igor Sikorsky Kyiv Polytechnic Institute'', Kyiv, Ukraine https://orcid.org/0000-0002-4384-4866
  • I. B. Tretiak Romodanov Neurosurgery Institute, Kyiv, Ukraine
  • S. M. Yakovenko West Virginia State University, West Virginia, USA https://orcid.org/0000-0002-5946-6409
  • O. M. Lysenko National Technical University of Ukraine ``Igor Sikorsky Kyiv Polytechnic Institute'', Kyiv, Ukraine http://orcid.org/0000-0003-1051-1149
  • A. O. Popov National Technical University of Ukraine ``Igor Sikorsky Kyiv Polytechnic Institute'', Kyiv, Ukraine; Ukrainian Catholic University, Lviv, Ukraine https://orcid.org/0000-0002-1194-4424

DOI:

https://doi.org/10.20535/RADAP.2024.96.50-61

Keywords:

upper limb orthosis, electromyography signal, neck muscle activity

Abstract

Elbow paresis, often resulting from brachial plexus injury, presents a significant challenge in the field of rehabilitation. To address this, we have developed a prototype powered orthosis that utilizes non-invasive surface electromyography (EMG) signals from neck muscles, such as the sternocleidomastoid, for intuitive control. This EMG-driven system allows for the precise manipulation of the elbow joint, covering the full physiological range of motion. The prototype's design integrates an EMG signal processor with an orthosis action operator, creating a seamless interface between human intent and mechanical action. Healthy participants were able to use neck muscle contractions to control elbow rotation effectively, demonstrating the system's potential for real-world application. The scaled EMG envelope directly influences the orthosis's rotational actuator, ensuring responsive and accurate control. Through rigorous sensitivity analysis, we optimized the control algorithm by adjusting EMG window lengths, signal filtering, and thresholding parameters. This optimization process ensures that the system can adapt to individual user needs, providing personalized and efficient control. The real-time control achieved with this prototype marks a significant step forward in the development of biomedical rehabilitation devices. It not only offers a practical solution for those affected by elbow paresis but also lays the groundwork for future advancements in neuromechanical interfaces. Our ongoing research aims to refine this technology further, exploring the integration of signal processing algorithms to predict and adapt to user movements, thereby creating a more natural and intuitive user experience. The ultimate goal is to develop a fully functional orthosis that can be readily implemented in clinical settings, providing a non-invasive, effective solution for elbow rehabilitation.

References

References

Nakipoğlu Yüzer, et al. (2018). The regularity of orthosis use and the reasons for disuse in stroke patients. International Journal of Rehabilitation Research, Vol. 41, Iss. 3, pp. 270-275 DOI: 10.1097/MRR.0000000000000299.

Jafarnezhadgero A. A., Mousavi S. H., Madadi-Shad M., Hijmans J. M. (2020). Quantifying lower limb inter-joint coordination and coordination variability after four-month wearing arch support foot orthoses in children with flexible flat feet. Human Movement Science, Vol. 70, 102593. DOI: 10.1016/j.humov.2020.102593.

Choo Y. J., Chang M. C. (2021). Effectiveness of an ankle–foot orthosis on walking in patients with stroke: a systematic review and meta-analysis. Sci Rep, Vol. 11, 15879. DOI: 10.1038/s41598-021-95449-x.

Young N., Terrington N., Francis D., Robinson L. S. (2018). Orthotic management of fixed flexion deformity of the proximal interphalangeal joint following traumatic injury: A systematic review. Hong Kong Journal of Occupational Therapy, Vol. 31, Iss. 1. pp. 3-13. DOI: 10.1177/1569186118764067.

Ramdharry G., Marsden J., Day B., Thompson A. (2006). De-stabilizing and training effects of foot orthoses in multiple sclerosis. Multiple Sclerosis Journal, Vol. 12, Iss. 2, pp. 219-226. DOI: 10.1191/135248506ms1266oa.

Ries A. J., Novacheck T. F., Schwartz M. H. (2014). A data driven model for optimal orthosis selection in children with cerebral palsy. Gait & Posture, Vol. 40, Iss. 4, pp. 539-544. DOI: 10.1016/j.gaitpost.2014.06.011.

Gijon-Nogueron, G., Ramos-Petersen, L., Ortega-Avila, A. B., et al. (2011). Effectiveness of foot orthoses in patients with rheumatoid arthritis related to disability and pain: a systematic review and meta-analysis. Qual Life Res, Vol. 27, pp. 3059–3069. DOI: 10.1007/s11136-018-1913-5.

Wong A. L., Wilson M., et al. (2017). The optimal orthosis and motion protocol for extensor tendon injury in zones IV-VIII: A systematic review. Journal of Hand Therapy, Vol. 30, Iss. 4, pp. 447-456. DOI: 10.1016/j.jht.2017.02.013.

Minh V., Tamre M., Safonov A., Musalimov V., Kovalenko P., Monakhov I. (2020) Design and implementation of a mechatronic elbow orthosis. Mechatronic Systems and Control, Vol. 48. DOI:10.2316/J.2020.201-0056.

Medina F., Perez K., Cruz-Ortiz D., Ballesteros M., Chairez I. (2021). Control of a hybrid upper-limb orthosis device based on a data-driven artificial neural network classifier of electromyography signals. Biomedical Signal Processing and Control, Vol. 68, 102624. DOI: 10.1016/j.bspc.2021.102624.

Hung K., Cheung H.-Y., Wan N., et al. (2021). Design, development, and evaluation of upper and lower limb orthoses with intelligent control for rehabilitation. IET Science, Mesurements and Technology, Vol. 15, Iss. 9, pp. 738-748. DOI: 10.1049/smt2.12074.

Durandau G. and Suleiman W. (2016). User-safe orthosis based on compliant actuators: Mechanical design and control framework. 55th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), pp. 1508-1513. DOI: 110.1109/SICE.2016.7749223.

Shalaby R., Schauer T., Liedecke W. and Raisch J. (2011). Amplifer design for EMG recording from stimulation

electrodes during functional electrical stimulation leg cycling ergometry. Biomedical Engineering, Vol. 56, pp. 23-33. DOI: 10.1515/bmt.2010.055.

Tang Z., Yu H., Yang H., Zhang L., Zhang Lu. (2022). Effect of velocity and acceleration in joint angle estimation for an EMG-Based upper-limb exoskeleton control. Computers in Biology and Medicine, Vol. 141, 105156. DOI: 110.1016/j.compbiomed.2021.105156.

Chowdhury R. H., Reaz M. B. I., Mohd Ali M. A. B., et al. (2013). Surface Electromyography Signal Processing and Classification Techniques. Sensors, Vol. 13(9), pp. 12431-12466; DOI: 10.3390/s130912431.

Farago E., Chan A. D. C. (2023). Detection and Reconstruction of Poor-Quality Channels in High-Density EMG Array Measurements. Sensors, Vol. 23(10), 4759; DOI: 10.3390/s23104759.

Downloads

Published

2024-06-30

How to Cite

Kotsiubailo , A. V., Savchuk , A. V., Omelkyna, D. V., Shoferystov , S. Y., Lavrenko , I. I., Tretiak , I. B., Yakovenko , S. M., Lysenko , O. M. and Popov , A. O. (2024) “Designing Minimalistic Powered Arm Orthosis for Brachial Plexus Injuries”, Visnyk NTUU KPI Seriia - Radiotekhnika Radioaparatobuduvannia, (96), pp. 50-61. doi: 10.20535/RADAP.2024.96.50-61.

Issue

Section

Radioelectronics Medical Technologies