Abstract
Bilateral rehabilitation training robotic systems have potential to promote the upper limb motor recovery of post-stroke hemiparesis patients through providing the synchronization motion between the impaired limb and contralateral limb. The active rehabilitation training based on patients’ intention can also promote the recovery effect by stimulating the activity of the ipsilateral hemisphere and contralateral hemisphere. In this paper, a novel intention-based bilateral training system using biomedical signals which represents the muscle activity information and active motion intention was proposed to promote the rehabilitation training effect. The proposed system can provide the synchronization motion to the impaired limb by the exoskeleton device according to the sEMG signals from the contralateral intact limb. A BPNN model using a novel multi-features input vector was employed for establishing the relationship between the sEMG signals and the motion intention. To verify the intention prediction performance, the comparison experiments involving both the offline phase and online phase were carried out using three different kinds of feature input vectors of sEMG. Furthermore, the real-time bilateral control experiments were conducted to verify the feasibility and effectiveness of the proposed bilateral rehabilitation system, in terms of motion synchronization tracking and the real-time characteristics.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ameri A, Akhaee M, Scheme E (2019) Regression convolutional neural network for improved simultaneous EMG control. J Neural Eng. https://doi.org/10.1088/1741-2552/ab0e2e
Balasubramanian S, Garcia-cossio E, Birbaumer N, Burdet E (2018) Is EMG a viable alternative to BCI for detecting movement intention in severe stroke? IEEE Trans Biomed Eng. https://doi.org/10.1109/TBME.2018.2817688
Benjamin EJ, Emelia J, Paul M, Alvaro A, Marcio SB, Clifton WC, Carson AP, Alanna MC et al (2019) Heart disease and stroke statistics-2019 update: a report from the American Heart Association. Circulation. https://doi.org/10.1161/CIR.0000000000000659
Bi L, Feleke A, Guan C (2019) A review on EMG-based motor intention prediction of continuous human upper limb motion for human-robot collaboration. Biomed Signal Process Control. https://doi.org/10.1016/j.bspc.2019.02.011
Guo S, Yang Z, Liu Y (2019) EMG-based continuous prediction of the upper limb elbow joint angle using GRNN. Proc 2019 IEEE Int Conf Mech Autom ICMA 2019, pp. 2168–2173.
Huang C, Klein C, Meng Z, Zhang Y, Li S, Zhou P (2019) Innervation zone distribution of the biceps brachii muscle examined using voluntary and electrically-evoked high-density surface EMG. J NeuroEng Rehabil. https://doi.org/10.1186/s12984-019-0544-6
Igual C, Pardo LA, Hahne M, Igual J (2019) Myoelectric control for upper limb prostheses. Electronics. https://doi.org/10.3390/electronics8111244
James H, Iqbal N (2019) Review on feature extraction and classification of neuromuscular disorders. Int J Modern Trends Sci Technol ISSN 5(7):2455–3778
Krebs HI (2018) Twenty+ years of robotics for upper-extremity rehabilitation following a stroke. In Rehabilitation robotics. Elsevier Ltd, London. https://doi.org/10.1016/b978-0-12-811995-2.00013-8.
Kundu AS, Mazumder O, Lenka PK, Bhaumik S (2017) Omnidirectional assistive wheelchair: design and control with isometric myoelectric based intention classification. Proc Proc Comput Sci. https://doi.org/10.1016/j.procs.2017.01.200
Lee MJ, Jung HL, Hyun MK, Sun ML (2017) Effectiveness of bilateral arm training for improving extremity function and activities of daily living performance in hemiplegic patients. J Stroke Cerebrovasc Dis 26:1020
Leung M, Rantalainen T, Teo WP, Kidgell D (2018) The ipsilateral corticospinal responses to cross-education are dependent upon the motor-training intervention. Exper Brain Res. https://doi.org/10.1007/s00221-018-5224-4
Li M, Xu G, Xie J, Chen C (2018) A review: motor rehabilitation after stroke with control based on human intent. Proc Inst Mech Eng Part H J Eng Med. https://doi.org/10.1177/0954411918755828
Li G, Li J, Ju Z, Sun Y, Kong J (2019) A novel feature extraction method for machine learning based on surface electromyography from healthy brain. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04147-3
Liu Y, Guo S, Hirata H, Ishihara H, Tamiya T (2018) Development of a powered variable-stiffness exoskeleton device for elbow rehabilitation. Biomed Microdevice. https://doi.org/10.1007/s10544-018-0312-6
Mazlan S et al (2020) Kinematic variables for upper limb rehabilitation robot and correlations with clinical scales: a review. Bull Electric Eng Inf. https://doi.org/10.11591/eei.v9i1.1856, https://doi.org/10.1016/j.jstrokecerebrovasdis.2016.12.008.
Mekki M, Delgado D, Fry A, Putrino D, Huang V (2018) Robotic rehabilitation and spinal cord injury: a narrative review. Neurotherapeutics. https://doi.org/10.1007/s13311-018-0642-3
Miao Q, Zhang M, McDaid A, Peng Y, Xie S (2020) A robot-assisted bilateral upper limb training strategy with subject-specific workspace: a pilot study. Robot Auton Syst 20:20. https://doi.org/10.1016/j.robot.2019.103334
Orand A, Aksoy E, Miyasaka H, Levy C, Zhang X, Menon C (2019) Bilateral tactile feedback-enabled training for stroke survivors using Microsoft KinectTM. Sensors. https://doi.org/10.3390/s19163474
Pan L, Crouch DL, Huang H (2019) Comparing EMG-based human-machine interfaces for estimating continuous, coordinated movements. IEEE Trans Neural Syst Rehabil Eng 27(10):2145–2154
Phinyomark A, Khushaba R, Scheme E (2019) Feature extraction and selection for myoelectric control based on wearable EMG sensors. Sensors. https://doi.org/10.3390/s18051615
Robertson JW, Englehart KB, Scheme EJ (2018) Effects of confidence-based rejection on usability and error in pattern recognition- based myoelectric control. IEEE J Biomed Health Inf. https://doi.org/10.1109/JBHI.2018.2878907
Rodríguez-Tapia B, Soto I, Marínez DM (2019) Myoelectric interfaces and related applications: current state of EMG signal processing—a systematic review. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2963881
Sheng B, Xie S, Tang L, Deng C, Zhang Y (2019) An industrial robot-based rehabilitation system for bilateral exercises. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2948162
Song Z, Guo S (2012) Design process of exoskeleton rehabilitation device and implementation of bilateral upper limb motor movement. J Med Biol Eng. https://doi.org/10.5405/jmbe.987
Song Z, Guo S, Pang M, Zhang S, Xiao N, Gao B, Shi L (2014) Implementation of resistance training using an upper-limb exoskeleton rehabilitation device for elbow joint. J Med Biol Eng. https://doi.org/10.5405/jmbe.1337
Vujaklija I, Shalchyan V, Kamavuako E, Jiang N, Marateb H, Farina D (2018) Online mapping of EMG signals into kinematics by autoencoding. J NeuroEng Rehabil. https://doi.org/10.1186/s12984-018-0363-1
Xiao F, Wang Y, Gao Y, Zhu Y, Zhao J (2018) Continuous estimation of joint angle from electromyography using multiple time-delayed features and random forests. Biomed Signal Process Control. https://doi.org/10.1016/j.bspc.2017.08.015
Zhang L (2019) An upper limb movement estimation from electromyography by using BP neural network. Biomed Signal Process Control. https://doi.org/10.1016/j.bspc.2018.12.020
Zhang S, Fu Q, Guo S, Fu Y (2019) Coordinative motion-based bilateral rehabilitation training system with exoskeleton and haptic devices for biomedical application. Micromachines. https://doi.org/10.3390/mi10010008
Acknowledgements
This work was supported in part by National High Tech. Research and Development Program of China under Grant 2015AA043202, and in part by SPS KAKENHI under Grant 15K2120.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Yang, Z., Guo, S., Liu, Y. et al. An intention-based online bilateral training system for upper limb motor rehabilitation. Microsyst Technol 27, 211–222 (2021). https://doi.org/10.1007/s00542-020-04939-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00542-020-04939-x