Research on mode adjustment control strategy of upper limb rehabilitation robot based on fuzzy recognition of interaction force_Journal of Biomedical Engineering

Authors：

LI Guoning ^1,2,3 , TAO Liang ⁴ , MENG Jingyan ^1,2,5 , YE Sijia ^1,2,3 , FENG Guang ^1,2 , ZHAO Dazheng ^1,2,5 , HU Yang ^1,2,5 , TANG Min ⁴ , SONG Tao ^1,2,6 , FU Rongzhen ² , ZUO Guokun ^1,2,3,6 ,  ZHANG Jiaji ^1,2,3,6 ,  SHI Changcheng ^1,2,3,6

1. Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang 315201, P. R. China;
2. Cixi Institute of Biomedical Engineering, Ningbo, Zhejiang 315300, P. R. China;
3. University of Chinese Academy of Sciences, Beijing 100049, P. R. China;
4. Ningbo Rehabilitation Hospital, Ningbo, Zhejiang 315040, P. R. China;
5. College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, P. R. China;
6. Zhejiang Engineering Research Center for Biomedical Materials, Ningbo, Zhejiang 315300, P. R. China;

Corresponding author：

ZHANG Jiaji, Email: zhangjiaji@nimte.ac.cn; SHI Changcheng, Email: changchengshi@nimte.ac.cn

Keywords：

Motor function assessment for upper-limb; Assistance-as-needed; Fuzzy recognition; Degree of active participation; Correlation analysis

DOI：

10.7507/1001-5515.202207018

Video：

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In the process of robot-assisted training for upper limb rehabilitation, a passive training strategy is usually used for stroke patients with flaccid paralysis. In order to stimulate the patient’s active rehabilitation willingness, the rehabilitation therapist will use the robot-assisted training strategy for patients who gradually have the ability to generate active force. This study proposed a motor function assessment technology for human upper-limb based on fuzzy recognition on interaction force and human-robot interaction control strategy based on assistance-as-needed. A passive training mode based on the calculated torque controller and an assisted training mode combined with the potential energy field were designed, and then the interactive force information collected by the three-dimensional force sensor during the training process was imported into the fuzzy inference system, the degree of active participation σ was proposed, and the corresponding assisted strategy algorithms were designed to realize the adaptive adjustment of the two modes. The significant correlation between the degree of active participation σ and the surface electromyography signals (sEMG) was found through the experiments, and the method had a shorter response time compared to a control strategy that only adjusted the mode through the magnitude of interaction force, making the robot safer during the training process.

Citation： LI Guoning, TAO Liang, MENG Jingyan, YE Sijia, FENG Guang, ZHAO Dazheng, HU Yang, TANG Min, SONG Tao, FU Rongzhen, ZUO Guokun, ZHANG Jiaji, SHI Changcheng. Research on mode adjustment control strategy of upper limb rehabilitation robot based on fuzzy recognition of interaction force. Journal of Biomedical Engineering, 2024, 41(1): 90-97. doi: 10.7507/1001-5515.202207018 Copy

1.	Virani S S, Alonso A, Aparicio H J, et al. Heart disease and stroke statistics-2021 update: a report from the American Heart Association. Circulation, 2021, 143(8): 254-743.
2.	Riener R, Nef T, Colombo G. Robot-aided neurorehabilitation of the upper extremities. Med Biol Eng Comput, 2005, 43(1): 2-10.
3.	Warraich Z, Kleim J A. Neural plasticity: the biological substrate for neurorehabilitation. PM R, 2010, 2(12): 208-219.
4.	Berges I M, Seale G S, Ostir G V. The role of positive affect on social participation following stroke. Disabil Rehabil, 2012, 34(25): 2119-2123.
5.	Horton S, Howell A, Humby K, et al. Engagement and learning: an exploratory study of situated practice in multi-disciplinary stroke rehabilitation. Disabil Rehabil, 2011, 33(3): 270-279.
6.	Liu X, Zhu Y, Huo H, et al. Design of virtual guiding tasks with haptic feedback for assessing the wrist motor function of patients with upper motor neuron lesions. IEEE Trans Neural Syst Rehabil Eng, 2019, 27(5): 984-994.
7.	Frisoli A, Borelli L, Montagner A, et al. Arm rehabilitation with a robotic exoskeleleton in Virtual Reality// 2007 IEEE 10th International Conference on Rehabilitation Robotics. Noordwijk: IEEE, 2007: 13-15.
8.	Guerra J, Smith L, Vicinanza D, et al. The use of sonification for physiotherapy in human movement tasks: a scoping review. Sci Sport, 2020, 35(3): 119-129.
9.	付艳, 阳交凤, 李世其, 等. 康复机器人被动训练对脑卒中患者上肢代偿运动的影响. 中国康复医学杂志, 2022, 37(5): 647-652.
10.	Podubecka J, Scheer S, Theilig S, et al. Cyclic movement training versus conventional physiotherapy for rehabilitation of hemiparetic gait after stroke: a pilot study. Fortschr Neurol Psychiatr, 2011, 79(7): 411-418.
11.	Xing L, Wang X, Wang J. A motion intention-based upper limb rehabilitation training system to stimulate motor nerve through virtual reality. Int J Adv Robot Syst, 2017, 14(6): 172988141774328.
12.	Guidali M, Keller U, Klamroth-Marganska V, et al. Estimating the patient's contribution during robot-assisted therapy. J Rehabil Res Dev, 2013, 50(3): 379-394.
13.	Lee K H, Baek S G, Lee H J, et al. Enhanced transparency for physical human-robot interaction using human hand impedance compensation. IEEE ASME Trans Mechatron, 2018, 23(6): 2662-2670.
14.	Zhang F, Lin L, Yang L, et al. Design of an active and passive control system of hand exoskeleton for rehabilitation. Appl Sci, 2019, 9(11): 2291.
15.	Kamps A, Schüle K. Cyclic movement training of the lower limb in stroke rehabilitation. Neurol Rehabil, 2005, 11(5): 143-148.
16.	王昱. 上肢康复机器人视觉与力觉反馈融合方法设计. 成都: 西南交通大学, 2021.
17.	Jones T A. Motor compensation and its effects on neural reorganization after stroke. Nat Rev Neurosci, 2017, 18(5): 267-280.
18.	Mueller A. Modern robotics: mechanics, planning, and control. IEEE Control Syst, 2019, 39(6): 100-102.
19.	Liu X, Zuo G, Zhang J, et al. Sensorless force estimation of end-effect upper limb rehabilitation robot system with friction compensation. Int J Adv Robot Syst, 2019, 16(4): 172988141985613.
20.	Najafi M, Rossa C, Adams K, et al. Using potential field function with a velocity field controller to learn and reproduce the therapist's assistance in robot-assisted rehabilitation. IEEE ASME Trans Mechatron, 2020, 25(3): 1622-1633.
21.	Khansari-Zadeh S M, Khatib O. Learning potential functions from human demonstrations with encapsulated dynamic and compliant behaviors. Auton Robot, 2015, 41(1): 45-69.
22.	Kovacic Z, Bogdan S. Fuzzy controller design: theory and applications. Panama: CRC Press, 2010.
23.	宋海燕, 张建国, 刘涛然, 等. 日常生活活动中人体上肢肌肉表面肌电特性研究. 生物医学工程学杂志, 2009, 26(6): 1177-1180.
24.	Zhang Z, Fang Q, Gu X. Fuzzy inference system based automatic brunnstrom stage classification for upper-extremity rehabilitation. Expert Syst Appl, 2014, 41(4): 1973-1980.

1. Virani S S, Alonso A, Aparicio H J, et al. Heart disease and stroke statistics-2021 update: a report from the American Heart Association. Circulation, 2021, 143(8): 254-743.
2. Riener R, Nef T, Colombo G. Robot-aided neurorehabilitation of the upper extremities. Med Biol Eng Comput, 2005, 43(1): 2-10.
3. Warraich Z, Kleim J A. Neural plasticity: the biological substrate for neurorehabilitation. PM R, 2010, 2(12): 208-219.
4. Berges I M, Seale G S, Ostir G V. The role of positive affect on social participation following stroke. Disabil Rehabil, 2012, 34(25): 2119-2123.
5. Horton S, Howell A, Humby K, et al. Engagement and learning: an exploratory study of situated practice in multi-disciplinary stroke rehabilitation. Disabil Rehabil, 2011, 33(3): 270-279.
6. Liu X, Zhu Y, Huo H, et al. Design of virtual guiding tasks with haptic feedback for assessing the wrist motor function of patients with upper motor neuron lesions. IEEE Trans Neural Syst Rehabil Eng, 2019, 27(5): 984-994.
7. Frisoli A, Borelli L, Montagner A, et al. Arm rehabilitation with a robotic exoskeleleton in Virtual Reality// 2007 IEEE 10th International Conference on Rehabilitation Robotics. Noordwijk: IEEE, 2007: 13-15.
8. Guerra J, Smith L, Vicinanza D, et al. The use of sonification for physiotherapy in human movement tasks: a scoping review. Sci Sport, 2020, 35(3): 119-129.
9. 付艳, 阳交凤, 李世其, 等. 康复机器人被动训练对脑卒中患者上肢代偿运动的影响. 中国康复医学杂志, 2022, 37(5): 647-652.
10. Podubecka J, Scheer S, Theilig S, et al. Cyclic movement training versus conventional physiotherapy for rehabilitation of hemiparetic gait after stroke: a pilot study. Fortschr Neurol Psychiatr, 2011, 79(7): 411-418.
11. Xing L, Wang X, Wang J. A motion intention-based upper limb rehabilitation training system to stimulate motor nerve through virtual reality. Int J Adv Robot Syst, 2017, 14(6): 172988141774328.
12. Guidali M, Keller U, Klamroth-Marganska V, et al. Estimating the patient's contribution during robot-assisted therapy. J Rehabil Res Dev, 2013, 50(3): 379-394.
13. Lee K H, Baek S G, Lee H J, et al. Enhanced transparency for physical human-robot interaction using human hand impedance compensation. IEEE ASME Trans Mechatron, 2018, 23(6): 2662-2670.
14. Zhang F, Lin L, Yang L, et al. Design of an active and passive control system of hand exoskeleton for rehabilitation. Appl Sci, 2019, 9(11): 2291.
15. Kamps A, Schüle K. Cyclic movement training of the lower limb in stroke rehabilitation. Neurol Rehabil, 2005, 11(5): 143-148.
16. 王昱. 上肢康复机器人视觉与力觉反馈融合方法设计. 成都: 西南交通大学, 2021.
17. Jones T A. Motor compensation and its effects on neural reorganization after stroke. Nat Rev Neurosci, 2017, 18(5): 267-280.
18. Mueller A. Modern robotics: mechanics, planning, and control. IEEE Control Syst, 2019, 39(6): 100-102.
19. Liu X, Zuo G, Zhang J, et al. Sensorless force estimation of end-effect upper limb rehabilitation robot system with friction compensation. Int J Adv Robot Syst, 2019, 16(4): 172988141985613.
20. Najafi M, Rossa C, Adams K, et al. Using potential field function with a velocity field controller to learn and reproduce the therapist's assistance in robot-assisted rehabilitation. IEEE ASME Trans Mechatron, 2020, 25(3): 1622-1633.
21. Khansari-Zadeh S M, Khatib O. Learning potential functions from human demonstrations with encapsulated dynamic and compliant behaviors. Auton Robot, 2015, 41(1): 45-69.
22. Kovacic Z, Bogdan S. Fuzzy controller design: theory and applications. Panama: CRC Press, 2010.
23. 宋海燕, 张建国, 刘涛然, 等. 日常生活活动中人体上肢肌肉表面肌电特性研究. 生物医学工程学杂志, 2009, 26(6): 1177-1180.
24. Zhang Z, Fang Q, Gu X. Fuzzy inference system based automatic brunnstrom stage classification for upper-extremity rehabilitation. Expert Syst Appl, 2014, 41(4): 1973-1980.

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