LITERATURE REVIEW: USER INTERFACE OF SYSTEM FUNCTIONAL ELECTRICAL STIMULATION (FES) AND ARM ROBOTIC REHABILITATION

Eko Wahyu Abryandoko; Susy Susmartini; Pringgo Widyo Laksono; Lobes Herdiman

doi:10.5937/jaes0-44644

Eko Wahyu Abryandoko Mechanical Engineering Department, Faculty of Engineering, Brawijaya University, Malang, Indonesia https://orcid.org/0000-0003-0480-6135
Susy Susmartini Mechanical Engineering Department, Faculty of Engineering, Brawijaya University, Malang, Indonesia https://orcid.org/0000-0003-1862-6979
Pringgo Widyo Laksono Mechanical Engineering Department, Faculty of Engineering, Brawijaya University, Malang, Indonesia https://orcid.org/0000-0003-0502-5453
Lobes Herdiman Mechanical Engineering Department, Faculty of Engineering, Brawijaya University, Malang, Indonesia https://orcid.org/0000-0001-9598-8602

DOI: https://doi.org/10.5937/jaes0-44644

Keywords: interaksi manusia-robot, arm robot rehabilitasi, FES

Abstract

Interface technology development for human-robot interaction (HRI) in rehabilitation systems has increased in recent years. HRI can effectively achieve specific motor goals desired in rehabilitation, such as combining human intentions and actions with robotic devices to perform the desired stroke rehabilitation movements. Rehabilitation devices are starting to be directed towards using devices that integrate functional electrical stimulation (FES) with robotic arms because they have succeeded in providing promising interventions to restore arm function by intensively activating the muscles of post-stroke patients. However, FES requires a high level of accuracy to position the limbs for the functional tasks given because excessive electrical stimulation can cause fatigue in the patient, so it is necessary to provide electrical stimulation with an amplitude that suits the patient's needs. Unfortunately, most studies have a constant voltage amplitude and do not consider the voltage that matches the patient's muscle needs; this treatment can cause fatigue in the patient. Robotic devices as rehabilitation aids have the potential to support external power and adapt electrical stimulation needs to the voltage amplitude applied to the FES. Integrating FES with a robotic arm support system into one hybrid neuroprosthesis is attractive because the mechanical device can complement muscle action and increase rehabilitation's repeatability and accuracy rate. The integration of FES and robotic arms is a promising approach in the future. This article reviews the state of the art regarding motor rehabilitation using functional electrical stimulation (FES) devices and robotic arms for the upper limbs of post-stroke patients. A narrative review was done through a literature search using the IEEE-Xplore, Scopus, and PubMed databases. Nine different rehabilitation system articles were identified. The selected systems were compared critically by considering the design and actuators, components, technological aspects, and technological challenges that could be developed in the future. This article also examines the development of HRI and emerging research trends in HRI-based rehabilitation.

References

Santisteban, L., Térémetz, M., Bleton, J. P., Baron, J. C., Maier, M. A., & Lindberg, P. G. (2016). Upper limb outcome measures used in stroke rehabilitation studies: A systematic literature review. PLoS ONE, 11(5). https://doi.org/10.1371/journal.pone.0154792.

Carino-Escobar, R. I., & Cantillo-Negrete, J. (2020). Brain-computer interfaces for upper limb motor rehabilitation of stroke patients. Revista Mexicana de Ingenieria Biomedica, 41(1), 128–140. https://doi.org/10.17488/RMIB.41.1.10.

Du Plessis, T., Djouani, K., & Oosthuizen, C. (2021). robotics A Review of Active Hand Exoskeletons for Rehabilitation and Assistance. https://doi.org/10.3390/robotics10.

Kuçuktabak, E. B., Kim, S. J., Wen, Y., Lynch, K., & Pons, J. L. (2021). Human-machine-human interaction in motor control and rehabilitation: a review. In Journal of Neuro Engineering and Rehabilitation (Vol. 18, Issue 1). BioMed Central Ltd. https://doi.org/10.1186/s12984-021-00974-5.

Zheng, Z., Wu, Z., Zhao, R., Ni, Y., Jing, X., & Gao, S. (2022). A Review of EMG-, FMG-, and EIT-Based Biosensors and Relevant Human–Machine Interactivities and Biomedical Applications. In Biosensors (Vol. 12, Issue 7). MDPI. https://doi.org/10.3390/bios12070516.

Lee, Y. Y., Lin, K. C., Cheng, H. J., Wu, C. Y., Hsieh, Y. W., & Chen, C. K. (2015). Effects of combining robot-assisted therapy with neuromuscular electrical stimulation on motor impairment, motor and daily function, and quality of life in patients with chronic stroke: A double-blinded randomized controlled trial. Journal of NeuroEngineering and Rehabilitation, 12(1). https://doi.org/10.1186/s12984-015-0088-3.

Kapadia, N., Moineau, B., & Popovic, M. R. (2020). Functional Electrical Stimulation Therapy for Retraining Reaching and Grasping After Spinal Cord Injury and Stroke. Frontiers in Neuroscience, 14. https://doi.org/10.3389/fnins.2020.00718.

Jung, S., Bong, J. H., Kim, S. J., & Park, S. (2021). DNN-Based FES control for gait rehabilitation of hemiplegic patients. Applied Sciences (Switzerland), 11(7). https://doi.org/10.3390/app11073163.

Vafadar, A. K., Côté, J. N., & Archambault, P. S. (2015). Effectiveness of functional electrical stimulation in improving clinical outcomes in the upper arm following stroke: A systematic review and meta-analysis. In BioMed Research International (Vol. 2015). Hindawi Limited. https://doi.org/10.1155/2015/729768.

Thrasher, T. A., Zivanovic, V., McIlroy, W., & Popovic, M. R. (2008). Rehabilitation of reaching and grasping function in severe hemiplegic patients using functional electrical stimulation therapy. Neurorehabilitation and Neural Repair, 22(6), 706–714. https://doi.org/10.1177/1545968308317436.

Lynch, C. L., & Popovic, M. R. (2008). Functional Electrical Stimulation. IEEE Control Systems, 28(2), 40–50. https://doi.org/10.1109/MCS.2007.914689.

Maffiuletti, N. A., Minetto, M. A., Farina, D., & Bottinelli, R. (2011). Electrical stimulation for neuromuscular testing and training: State-of-the art and unresolved issues. In European Journal of Applied Physiology (Vol. 111, Issue 10, pp. 2391–2397). https://doi.org/10.1007/s00421-011-2133-7.

Bickel, C. S., Gregory, C. M., & Dean, J. C. (2011). Motor unit recruitment during neuromuscular electrical stimulation: A critical appraisal. In European Journal of Applied Physiology (Vol. 111, Issue 10, pp. 2399–2407). https://doi.org/10.1007/s00421-011-2128-4.

Qian, Q., Hu, X., Lai, Q., Ng, S. C., Zheng, Y., & Poon, W. (2017). Early stroke rehabilitation of the upper limb assisted with an electromyography-driven neuromuscular electrical stimulation-robotic arm. Frontiers in Neurology, 8(SEP). https://doi.org/10.3389/fneur.2017.00447.

Ajiboye, A. B., Willett, F. R., Young, D. R., Memberg, W. D., Murphy, B. A., Miller, J. P., Walter, B. L., Sweet, J. A., Hoyen, H. A., Keith, M. W., Peckham, P. H., Simeral, J. D., Donoghue, J. P., Hochberg, L. R., & Kirsch, R. F. (2017). Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: a proof-of-concept demonstration. The Lancet, 389(10081), 1821–1830. https://doi.org/10.1016/S0140-6736(17)30601-3.

Akbari, A., Haghverd, F., & Behbahani, S. (2021). Robotic Home-Based Rehabilitation Systems Design: From a Literature Review to a Conceptual Framework for Community-Based Remote Therapy During COVID-19 Pandemic. Frontiers in Robotics and AI, 8. https://doi.org/10.3389/frobt.2021.612331.

Huang, V. S., & Krakauer, J. W. (2009). Robotic neurorehabilitation: A computational motor learning perspective. In Journal of NeuroEngineering and Rehabilitation (Vol. 6, Issue 1). https://doi.org/10.1186/1743-0003-6-5.

Muratori, L. M., Lamberg, E. M., Quinn, L., & Duff, S. V. (2013). Applying principles of motor learning and control to upper extremity rehabilitation. Journal of Hand Therapy, 26(2), 94–103. https://doi.org/10.1016/j.jht.2012.12.007.

Nussbaum, E. L., Houghton, P., Anthony, J., Rennie, S., Shay, B. L., & Hoens, A. M. (2017). Neuromuscular electrical stimulation for treatment of muscle impairment: Critical review and recommendations for clinical practice. In Physiotherapy Canada (Vol. 69, Issue 5 Special Issue, pp. 1–76). University of Toronto Press Inc. https://doi.org/10.3138/ptc.2015-88.

Zhang, D., Ren, Y., Gui, K., Jia, J., & Xu, W. (2017). Cooperative control for a hybrid rehabilitation system combining functional electrical stimulation and robotic exoskeleton. Frontiers in Neuroscience, 11(DEC). https://doi.org/10.3389/fnins.2017.00725.

Stewart, A. M., Pretty, C. G., Adams, M., & Chen, X. Q. (2017). Review of Upper Limb Hybrid Exoskeletons. IFAC-PapersOnLine, 50(1), 15169–15178. https://doi.org/10.1016/j.ifacol.2017.08.2266.

Rong, W., Li, W., Pang, M., Hu, J., Wei, X., Yang, B., Wai, H., Zheng, X., & Hu, X. (2017). A Neuromuscular Electrical Stimulation (NMES) and robot hybrid system for multi-joint coordinated upper limb rehabilitation after stroke. Journal of NeuroEngineering and Rehabilitation, 14(1). https://doi.org/10.1186/s12984-017-0245-y.

Urrea, C., & Kern, J. (2011). A New Model for Analog Servo Motors. Simulations and Experimental Results. In Canadian Journal on Automation, Control and Intelligent Systems (Vol. 2, Issue 2). https://www.researchgate.net/publication/228906444.

Gonzalez, A., Garcia, L., Kilby, J., & McNair, P. (2021). Robotic devices for paediatric rehabilitation: a review of design features. In BioMedical Engineering Online (Vol. 20, Issue 1). BioMed Central Ltd. https://doi.org/10.1186/s12938-021-00920-5.

Su, D., Hu, Z., Wu, J., Shang, P., & Luo, Z. (2023). Review of adaptive control for stroke lower limb exoskeleton rehabilitation robot based on motion intention recognition. In Frontiers in Neurorobotics (Vol. 17). Frontiers Media SA. https://doi.org/10.3389/fnbot.2023.1186175.

Bouteraa, Y., Ben Abdallah, I., & Elmogy, A. (2020). Design and control of an exoskeleton robot with EMG-driven electrical stimulation for upper limb rehabilitation. Industrial Robot, 47(4), 489–501. https://doi.org/10.1108/IR-02-2020-0041.

Guo, Z., Zhou, S., Ji, K., Zhuang, Y., Song, J., Nam, C., Hu, X., & Zheng, Y. (2022). Corticomuscular integrated representation of voluntary motor effort in robotic control for wrist-hand rehabilitation after stroke. Journal of Neural Engineering, 19(2). https://doi.org/10.1088/1741-2552/ac5757.

Tu, X., Han, H., Huang, J., Li, J., Su, C., Jiang, X., & He, J. (2017). Upper Limb Rehabilitation Robot Powered by PAMs Cooperates with FES Arrays to Realize Reach-to-Grasp Trainings. Journal of Healthcare Engineering, 2017. https://doi.org/10.1155/2017/1282934.

Nam, C., Rong, W., Li, W., Cheung, C., Ngai, W., Cheung, T., Pang, M., Li, L., Hu, J., Wai, H., & Hu, X. (2022). An Exoneuromusculoskeleton for Self-Help Upper Limb Rehabilitation after Stroke. Soft Robotics, 9(1), 14–35. https://doi.org/10.1089/soro.2020.0090.

Gassert, R., & Dietz, V. (2018). Rehabilitation robots for the treatment of sensorimotor deficits: A neurophysiological perspective. Journal of NeuroEngineering and Rehabilitation, 15(1), 1–15. https://doi.org/10.1186/s12984-018-0383-x.

Meyer-Rachner, P., Passon, A., Klauer, C., & Schauer, T. (2017). Compensating the effects of FES-induced muscle fatigue by rehabilitation robotics during arm weight support. Current Directions in Biomedical Engineering, 3(1), 31–34. https://doi.org/10.1515/cdbme-2017-0007.

Ambrosini, E., Russold, M., Gfoehler, M., Puchinger, M., Weber, M., Becker, S., Krakow, K., Immick, N., Augsten, A., Rossini, M., Proserpio, D., Zajc, J., Gasperini, G., Molteni, F., Pedrocchi, A., Ferrante, S., Ferrigno, G., Gasperina, S. D., Bulgheroni, M., … Wiesener, C. (2019). A Hybrid robotic system for arm training of stroke survivors: Concept and first evaluation. IEEE Transactions on Biomedical Engineering, 66(12), 3290–3300. https://doi.org/10.1109/TBME.2019.2900525.

Ambrosini, E., Gasperini, G., Zajc, J., Immick, N., Augsten, A., Rossini, M., Ballarati, R., Russold, M., Ferrante, S., Ferrigno, G., Bulgheroni, M., Baccinelli, W., Schauer, T., Wiesener, C., Gfoehler, M., Puchinger,M., Weber, M., Weber, S., Pedrocchi, A., … Krakow, K. (2021). A Robotic System with EMG-Triggered Functional Eletrical Stimulation for Restoring Arm Functions in Stroke Survivors. Neurorehabilitation and Neural Repair, 35(4), 334–345. https://doi.org/10.1177/1545968321997769.

Scano, A., Chiavenna, A., Malosio, M., Tosatti, L. M., & Molteni, F. (2017). Muscle synergies-based characterization and clustering of poststroke patients in reaching movements. Frontiers in Bioengineering and Biotechnology, 5(OCT). https://doi.org/10.3389/fbioe.2017.00062.

Resquín, F., Cuesta Gómez, A., Gonzalez-Vargas, J., Brunetti, F., Torricelli, D., Molina Rueda, F., Cano de la Cuerda, R., Miangolarra, J. C., & Pons, J. L. (2016). Hybrid robotic systems for upper limb rehabilitation after stroke: A review. Medical Engineering and Physics, 38(11), 1279–1288. https://doi.org/10.1016/j.medengphy.2016.09.001.

Ibitoye, M. O., Hamzaid, N. A., Hasnan, N., Wahab, A. K. A., & Davis, G. M. (2016a). Strategies for rapid muscle fatigue reduction during FES exercise in individuals with spinal cord injury: A systematic review. PLoS ONE, 11(2). https://doi.org/10.1371/journal.pone.0149024.

Frazão, M., Cipriano, G., & Silva, P. E. (2024). Does inflammation and altered metabolism impede efficacy of functional electrical stimulation in critically ill patients? Unleashing the potential of individualized functional electrical stimulation-cycling in critical illness. In Critical Care (Vol. 28, Issue 1). BioMed Central Ltd. https://doi.org/10.1186/s13054-023-04788-w.

Arpin, D. J., Ugiliweneza, B., Forrest, G., Harkema, S. J., & Rejc, E. (2019). Optimizing neuromuscular electrical stimulation pulse width and amplitude to promote central activation in individuals with severe spinal cord injury. Frontiers in Physiology, 10(OCT). https://doi.org/10.3389/fphys.2019.01310.

Bubenzer, L. J., Konsolke, L., Enax-Krumova, E., Eberhardt, F., Tegenthoff, M., Höffken, O., & Özgül, Ö. S. (2023). Pain-related evoked potentials with concentric surface electrodes in patients and healthy subjects: a systematic review. In Brain Structure and Function (Vol. 228, Issue 7, pp. 1581–1594). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/s00429-023-02690-3.

Y Abe, Y., & Nishizawa, M. (2021). Electrical aspects of skin as a pathway to engineering skin devices. In APL Bioengineering (Vol. 5, Issue 4). American Institute of Physics Inc. https://doi.org/10.1063/5.0064529.

Li, Z., Chen, G., & Zhang, C. (2022). Research on Position and Torque Loading System with Velocity‐Sensitive and Adaptive Robust Control. Sensors, 22(4). https://doi.org/10.3390/s22041329.

Iandolo, R., Marini, F., Semprini, M., Laffranchi, M., Mugnosso, M., Cherif, A., De Michieli, L., Chiappalone, M., & Zenzeri, J. (2019). Perspectives and challenges in robotic neurorehabilitation. In Applied Sciences (Switzerland) (Vol. 9, Issue 15). MDPI AG. https://doi.org/10.3390/app9153183.

Li, Z., Chen, G., & Zhang, C. (2022). Research on Position and Torque Loading System with Velocity‐Sensitive and Adaptive Robust Control. Sensors, 22(4). https://doi.org/10.3390/s22041329.

Chalh, A., El Hammoumi, A., Motahhir, S., El Ghzizal, A., Subramaniam, U., & Derouich, A. (2020). Trusted simulation using proteus model for a PV system: Test case of an improved HC MPPT algorithm. Energies, 13(8). https://doi.org/10.3390/en13081943.

Shao, N., Zhang, S., & Liang, H. (2017). Model-based safety analysis of a control system using Simulink and Simscape extended models. MATEC Web of Conferences, 139. https://doi.org/10.1051/matecconf/201713900219.

Dunkelberger, N., Schearer, E. M., & O’Malley, M. K. (2020). A review of methods for achieving upper limb movement following spinal cord injury through hybrid muscle stimulation and robotic assistance. In Experimental Neurology (Vol. 328). Academic Press Inc. https://doi.org/10.1016/j.expneurol.2020.113274.