Assist-As-Needed Exoskeleton for Hand Joint Rehabilitation Based on Muscle Effort Detection

Sensors - Tập 21 Số 13 - Trang 4372
Jenny C. Castiblanco1, Iván F. Mondragón2, Catalina Alvarado‐Rojas3, Julián Colorado3
1School of Engineering, Pontificia Universidad Javeriana Bogota, Cra. 7 No. 40-62, Bogota 110231, Colombia
2Department of Industrial Engineering, Pontificia Universidad Javeriana Bogota, Cra. 7 No. 40-62, Bogota 110231, Colombia
3Department of Electronics Engineering, Pontificia Universidad Javeriana Bogota, Cra. 7 No. 40-62, Bogota 110231, Colombia

Tóm tắt

Robotic-assisted systems have gained significant traction in post-stroke therapies to support rehabilitation, since these systems can provide high-intensity and high-frequency treatment while allowing accurate motion-control over the patient’s progress. In this paper, we tackle how to provide active support through a robotic-assisted exoskeleton by developing a novel closed-loop architecture that continually measures electromyographic signals (EMG), in order to adjust the assistance given by the exoskeleton. We used EMG signals acquired from four patients with post-stroke hand impairments for training machine learning models used to characterize muscle effort by classifying three muscular condition levels based on contraction strength, co-activation, and muscular activation measurements. The proposed closed-loop system takes into account the EMG muscle effort to modulate the exoskeleton velocity during the rehabilitation therapy. Experimental results indicate the maximum variation on velocity was 0.7 mm/s, while the proposed control system effectively modulated the movements of the exoskeleton based on the EMG readings, keeping a reference tracking error <5%.

Từ khóa


Tài liệu tham khảo

Kwakkel, 2008, Effects of Robot-Assisted Therapy on Upper Limb Recovery After Stroke: A Systematic Review, Neurorehabil. Neural Repair, 22, 111, 10.1177/1545968307305457

Chang, 2013, Robot-assisted Therapy in Stroke Rehabilitation, J. Stroke, 15, 174, 10.5853/jos.2013.15.3.174

Turolla, A. (2018). An Overall Framework for Neurorehabilitation Robotics: Implications for Recovery, Elsevier Ltd.

Lambercy, 2018, Robot-assisted rehabilitation of hand function, Rehabilitation Robotics, Volume 23, 205

Lambercy, O., Lünenburger, L., Gassert, R., and Bolliger, M. (2012). Robots for Measurement/Clinical Assessment. Neurorehabilitation Technology, Springer.

Yue, Z., Zhang, X., and Wang, J. (2017). Hand Rehabilitation Robotics on Poststroke Motor Recovery. Behav. Neurol., 2017.

Bos, 2016, A structured overview of trends and technologies used in dynamic hand orthoses, J. Neuroeng. Rehabil., 13, 62, 10.1186/s12984-016-0168-z

Troncossi, M., Mozaffari-foumashi, M., and Parenti-castelli, V. (2016). An Original Classification of Rehabilitation Hand Exoskeletons. J. Robot. Mech. Eng. Res., 1.

Surakijboworn, M., and Wannasuphoprasit, W. (2015, January 9–11). Design of a Novel Finger Exoskeleton with a Sliding Six-bar Joint Mechanism. Proceedings of the 6th Augmented Human International Conference, Singapore.

Taheri, 2014, Design and preliminary evaluation of the FINGER rehabilitation robot: Controlling challenge and quantifying finger individuation during musical computer game play, J. Neuroeng. Rehabil., 11, 10, 10.1186/1743-0003-11-10

Susanto, 2015, Efficacy of robot-assisted fingers training in chronic stroke survivors: A pilot randomized-controlled trial, J. Neuroeng. Rehabil., 12, 42, 10.1186/s12984-015-0033-5

Arata, J., Ohmoto, K., Gassert, R., Lambercy, O., Fujimoto, H., and Wada, I. (2013, January 6–10). A new hand exoskeleton device for rehabilitation using a three-layered sliding spring mechanism. Proceedings of the 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, Germany.

Agarwal, P., Fernandez, B.R., and Deshpande, A.D. (2015, January 28–30). Assist-as-Needed Controllers for Index Finger Module of a Hand Exoskeleton for Rehabilitation. Proceedings of the ASME 2015 Dynamic Systems and Control Conference, Columbus, OH, USA.

Agarwal, P., and Deshpande, A.D. (2015, January 11–14). Impedance and force-field control of the index finger module of a hand exoskeleton for rehabilitation. Proceedings of the 2015 IEEE International Conference on Rehabilitation Robotics (ICORR), Singapore.

Anam, 2012, Active exoskeleton control systems: State of the art, Procedia Eng., 41, 988, 10.1016/j.proeng.2012.07.273

Gopura, 2016, Developments in hardware systems of active upper-limb exoskeleton robots: A review, Rob. Auton. Syst., 75, 203, 10.1016/j.robot.2015.10.001

Cui, L., Phan, A., and Allison, G. (2015, January 25–29). Design and fabrication of a three dimensional printable non-assembly articulated hand exoskeleton for rehabilitation. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Milan, Italy.

Basteris, 2014, Training modalities in robot-mediated upper limb rehabilitation in stroke: A framework for classification based on a systematic review, J. Neuroeng. Rehabil., 11, 1, 10.1186/1743-0003-11-111

Carmichael, M.G. (2013). A Musculoskeletal Model-based Assistance-As-Needed Paradigm for Assistive Robotics. [Ph.D. Thesis, University of Technology Sydney].

Agarwal, 2018, Subject-Specific Assist-as-Needed Controllers for a Hand Exoskeleton for Rehabilitation, IEEE Robot. Autom. Lett., 3, 508, 10.1109/LRA.2017.2768124

Teramae, 2017, EMG-based model predictive control for physical human–robot interaction: Application for assist-as-needed control, IEEE Robot. Autom. Lett., 3, 210, 10.1109/LRA.2017.2737478

Peternel, L., Noda, T., Petrič, T., Ude, A., Morimoto, J., and Babič, J. (2016). Adaptive Control of Exoskeleton Robots for Periodic Assistive Behaviours Based on EMG Feedback Minimisation. PLoS ONE, 11.

Kiguchi, 2012, An EMG-Based Control for an Upper-Limb Power-Assist Exoskeleton Robot, IEEE Trans. Syst. Man Cybern. Part B (Cybern.), 42, 1064, 10.1109/TSMCB.2012.2185843

Kim, 2017, An EMG-based variable impedance control for elbow exercise: Preliminary study, Adv. Robot., 31, 809, 10.1080/01691864.2017.1353440

Meattini, 2020, sEMG-Based Human-in-the-Loop Control of Elbow Assistive Robots for Physical Tasks and Muscle Strength Training, IEEE Robot. Autom. Lett., 5, 5795, 10.1109/LRA.2020.3010741

Castiblanco, J.C., Ortmann, S., Mondragon, I.F., Alvarado-Rojas, C., Jöbges, M., and Colorado, J.D. (2020). Myoelectric pattern recognition of hand motions for stroke rehabilitation. Biomed. Signal Process. Control, 57.

Arteaga, M.V., Castiblanco, J.C., Mondragon, I.F., Colorado, J.D., and Alvarado-Rojas, C. (December, January 29). EMG-based adaptive trajectory generation for an exoskeleton model during hand rehabilitation exercises. Proceedings of the 2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob), New York, NY, USA.

Castiblanco, J.C., Arteaga, M.V., Mondragon, I.F., Ortmann, S., Alvarado-Rojas, C., and Colorado, J.D. (December, January 29). Velocity modulation assistance for stroke rehabilitation based on EMG muscular condition. Proceedings of the 2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob), New York, NY, USA.

Thông tin
Thông tin xuất bản

Sensors

Tập 21 Số 13

4372

Thông tin tác giả