Neural network based patient recovery estimation of a PAM-based rehabilitation robot

Van-Vuong Dinh; Minh-Chien Trinh; Tien-Dat Bui; Minh-Duc Duong; Quy-Thinh Dao

doi:10.14311/AP.2023.63.0179

Authors

Van-Vuong Dinh Hanoi University of Science and Technology, School of Electrical and Electronic Engineering, 11615 Hanoi, Vietnam
Minh-Chien Trinh Hanoi University of Science and Technology, School of Electrical and Electronic Engineering, 11615 Hanoi, Vietnam
Tien-Dat Bui Hanoi University of Science and Technology, School of Electrical and Electronic Engineering, 11615 Hanoi, Vietnam
Minh-Duc Duong Hanoi University of Science and Technology, School of Electrical and Electronic Engineering, 11615 Hanoi, Vietnam
Quy-Thinh Dao Hanoi University of Science and Technology, School of Electrical and Electronic Engineering, 11615 Hanoi, Vietnam

DOI:

https://doi.org/10.14311/AP.2023.63.0179

Keywords:

pneumatic artificial muscle, rehabilitation robot, neural network, patient recovery

Abstract

Rehabilitation robots have shown a promise in aiding patient recovery by supporting them in repetitive, systematic training sessions. A critical factor in the success of such training is the patient’s recovery progress, which can guide suitable treatment plans and reduce recovery time. In this study, a neural network-based approach is proposed to estimate the patient’s recovery, which can aid in the development of an assist-as-needed training strategy for the gait training system. Experimental results show that the proposed method can accurately estimate the external torques generated by the patient to determine their recovery. The estimated patient recovery is used for an impedance control of a 2-DOF robotic orthosis powered by pneumatic artificial muscles, which improves the robot joint compliance coefficients and makes the patient more comfortable and confident during rehabilitation exercises.

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References

M. Kyrarini, F. Lygerakis, A. Rajavenkatanarayanan, et al. A survey of robots in healthcare. Technologies 9(1):8, 2021. https://doi.org/10.3390/technologies9010008

G. Morone, S. Paolucci, A. Cherubini, et al. Robot assisted gait training for stroke patients: Current state of the art and perspectives of robotics. Neuropsychiatric disease and treatment 13(1):1303–1311, 2017. https://doi.org/10.2147/NDT.S114102

D. B. Reynolds, D. W. Repperger, C. A. Phillips, G. Bandry. Modeling the dynamic characteristics of pneumatic muscle. Annals of Biomedical Engineering 31(3):317–319, 2003. https://doi.org/10.1114/1.1554921

C.-P. Chou, B. Hannaford. Measurement and modeling of McKibben pneumatic artificial muscles. IEEE Transactions on Robotics and Automation 12(1):90–102, 1996. https://doi.org/10.1109/70.481753

T.-Y. Choi, J.-J. Lee. Control of manipulator using pneumatic muscles for enhanced safety. IEEE Transactions on Industrial Electronics 57(8):2815–2825, 2010. https://doi.org/10.1109/TIE.2009.2036632

X. Cheng, Y. Zhou, C. Zuo, X. Fan. Design of an upper limb rehabilitation robot based on medical theory. Procedia Engineering 15:688–692, 2011. https://doi.org/10.1016/j.proeng.2011.08.128

L. Zhao, H. Cheng, Y. Xia, B. Liu. Angle tracking adaptive backstepping control for a mechanism of pneumatic muscle actuators via an AESO. IEEE Transactions on Industrial Electronics 66(6):4566–4576, 2019. https://doi.org/10.1109/TIE.2018.2860527

Q.-T. Dao, S.-i. Yamamoto. Assist-as-needed control of a robotic orthosis actuated by pneumatic artificial muscle for gait rehabilitation. Applied Sciences 8(4):499, 2018. https://doi.org/10.3390/app8040499

M.-C. Trinh, T.-H. Do, Q.-T. Dao. Development of a rehabilitation robot: Modeling and trajectory tracking control. ASEAN Engineering Journal 12(4):121–129, 2022. https://doi.org/10.11113/aej.v12.17196

P. Beyl, M. Van Damme, R. Van Ham, et al. Pleated pneumatic artificial muscle-based actuator system as a torque source for compliant lower limb exoskeletons. IEEE/ASME Transactions on Mechatronics 19(3):1046–1056, 2014. https://doi.org/10.1109/TMECH.2013.2268942

C.-T. Chen, W.-Y. Lien, C.-T. Chen, et al. Dynamic modeling and motion control of a cable-driven robotic exoskeleton with pneumatic artificial muscle actuators. IEEE Access 8:149796–149807, 2020. https://doi.org/10.1109/ACCESS.2020.3016726

Z. Q. Tang, H. L. Heung, X. Q. Shi, et al. Probabilistic model-based learning control of a soft pneumatic glove for hand rehabilitation. IEEE Transactions on Biomedical Engineering 69(2):1016–1028, 2022. https://doi.org/10.1109/TBME.2021.3111891

C. M. Thalman, M. Debeurre, H. Lee. Entrainment during human locomotion using a soft wearable ankle robot. IEEE Robotics and Automation Letters 6(3):4265–4272, 2021. https://doi.org/10.1109/LRA.2021.3066961

Q.-T. Dao, V.-V. Dinh, M.-C. Trinh, et al. Nonlinear extended observer-based ADRC for a lower-limb PAM-based exoskeleton. Actuators 11(12):369, 2022. https://doi.org/10.3390/act11120369

Y. Wang, Q. Xu. Design and testing of a soft parallel robot based on pneumatic artificial muscles for wrist rehabilitation. Scientific Reports 11:1273, 2021. https://doi.org/10.1038/s41598-020-80411-0

P. Ohta, L. Valle, J. King, et al. Design of a lightweight soft robotic arm using pneumatic artificial muscles and inflatable sleeves. Soft Robotics 5(2):204–215, 2018. https://doi.org/10.1089/soro.2017.0044

A. Colomé, D. Pardo, G. Alenyà, C. Torras. External force estimation during compliant robot manipulation. In 2013 IEEE International Conference on Robotics and Automation, pp. 3535–3540. 2013. https://doi.org/10.1109/ICRA.2013.6631072

E. Berger, S. Grehl, D. Vogt, et al. Experience-based torque estimation for an industrial robot. In 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 144–149. 2016. https://doi.org/10.1109/ICRA.2016.7487127

A. Wahrburg, E. Morara, G. Cesari, et al. Cartesian contact force estimation for robotic manipulators using Kalman filters and the generalized momentum. In 2015 IEEE International Conference on Automation Science and Engineering (CASE), pp. 1230–1235. 2015. https://doi.org/10.1109/CoASE.2015.7294266

Y. Lu, Y. Shen, C. Zhuang. External force estimation for industrial robots using configuration optimization. Automatika 64(2):365–388, 2023. https://doi.org/10.1080/00051144.2023.2166451

A. C. Smith, F. Mobasser, K. Hashtrudi-Zaad. Neural-network-based contact force observers for haptic applications. IEEE Transactions on Robotics 22(6):1163–1175, 2006. https://doi.org/10.1109/TRO.2006.882923

N. Yilmaz, J. Y. Wu, P. Kazanzides, U. Tumerdem. Neural network based inverse dynamics identification and external force estimation on the da Vinci research kit. In 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 1387–1393. 2020. https://doi.org/10.1109/ICRA40945.2020.9197445

Z. Chua, A. M. Okamura. Characterization of real-time haptic feedback from multimodal neural network-based force estimates during teleoperation. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1471–1478. 2022. https://doi.org/10.1109/IROS47612.2022.9981662

J. Y. Wu, N. Yilmaz, U. Tumerdem, P. Kazanzides. Robot force estimation with learned intraoperative correction. In 2021 International Symposium on Medical Robotics (ISMR), pp. 1–7. 2021. https://doi.org/10.1109/ISMR48346.2021.9661568

M. Chiako, B. Mahdi, A. Vahid. Muscle force estimation from lower limb EMG signals using novel optimised machine learning techniques. Medical and Biological Engineering and Computing 60(3):683–699, 2022. https://doi.org/10.1007/s11517-021-02466-z

B. Siciliano, L. Sciavicco, L. Villani, G. Oriolo. Robotics: Modelling, Planning and Control. Springer London, London, 1st edn., 2009.

S. Hussain, S. Q. Xie, P. K. Jamwal. Adaptive impedance control of a robotic orthosis for gait rehabilitation. IEEE Transactions on Cybernetics 43(3):1025–1034, 2013. https://doi.org/10.1109/TSMCB.2012.2222374

R. Uhrig. Introduction to artificial neural networks. In Proceedings of IECON ’95 – 21st Annual Conference on IEEE Industrial Electronics, vol. 1, pp. 33–37. 1995. https://doi.org/10.1109/IECON.1995.483329

T.-Y. Choi, J.-J. Lee. Control of manipulator using pneumatic muscles for enhanced safety. IEEE Transactions on Industrial Electronics 57(8):2815–2825, 2010. https://doi.org/10.1109/TIE.2009.2036632

Q.-T. Dao, V. V. Dinh, C. T. Vu, et al. An adaptive sliding mode controller for a PAM-based actuator. Engineering, Technology and Applied Science Research 13(1):10086–10092, 2023. https://doi.org/10.48084/etasr.5539