Mirror-type rehabilitation training with dynamic adjustment and assistance for shoulder joint_Journal of Biomedical Engineering

Authors：

 CHEN Sheng ^1,2 , YAN Yizhe ¹ , XU Guozheng ¹ , GAO Xiang ¹ , HUANG Kangjin ¹ , TAI Chun ¹

1. Institute of Robot Information Perception and Control, Nanjing University of Posts and Telecommunications, Nanjing 210023, P.R.China;
2. Provincial Key Laboratory of Precision and Micro Manufacturing Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P.R.China;

Corresponding?author：

CHEN Sheng, Email: chensheng@njupt.edu.cn

Keywords：

mirror training; shoulder joint; rehabilitation robot; adjustable assistance; surface electromyography

DOI：

10.7507/1001-5515.202001053

Video：

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Abstract Full text Figures/Tables Video References Cited by

The real physical image of the affected limb, which is difficult to move in the traditional mirror training, can be realized easily by the rehabilitation robots. During this training, the affected limb is often in a passive state. However, with the gradual recovery of the movement ability, active mirror training becomes a better choice. Consequently, this paper took the self-developed shoulder joint rehabilitation robot with an adjustable structure as an experimental platform, and proposed a mirror training system completed by next four parts. First, the motion trajectory of the healthy limb was obtained by the Inertial Measurement Units (IMU). Then the variable universe fuzzy adaptive proportion differentiation (PD) control was adopted for inner loop, meanwhile, the muscle strength of the affected limb was estimated by the surface electromyography (sEMG). The compensation force for an assisted limb of outer loop was calculated. According to the experimental results, the control system can provide real-time assistance compensation according to the recovery of the affected limb, fully exert the training initiative of the affected limb, and make the affected limb achieve better rehabilitation training effect.

Citation： CHEN Sheng, YAN Yizhe, XU Guozheng, GAO Xiang, HUANG Kangjin, TAI Chun. Mirror-type rehabilitation training with dynamic adjustment and assistance for shoulder joint. Journal of Biomedical Engineering, 2021, 38(2): 351-360. doi: 10.7507/1001-5515.202001053 Copy

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9.	Kim H J, Lee G C, Song C H. Effect of functional electrical stimulation with mirror therapy on upper extremity motor function in poststroke patients. J Stroke Cerebrovasc, 2014, 23(4): 655-661.
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13.	Tang S, Lin C, Barsotti M, et al. Kinematic synergy of multi-DoF movement in upper limb and its application for rehabilitation exoskeleton motion planning. Front Neurorobot, 2019, 13(99): 1-13.
14.	Rakesh K, Niclas B, Gutierrez-Farewik E M, et al. A survey of human shoulder functional kinematic representations. Med Biol Eng Comput, 2019, 57: 339-367.
15.	Niyetkaliyev A S, Hussain S, Ghayesh M H, et al. Review on design and control aspects of robotic shoulder rehabilitation orthoses. IEEE Trans Hum-Mach Syst, 2017, 47(6): 1134-1145.
16.	Sabatelli S, Galgani M, Fanucci L, et al. A double-stage kalman filter for orientation tracking with an integrated processor in 9-D IMU. IEEE Trans Instrum Meas, 2013, 62(3): 590-598.
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20.	邵誠, 董希文, 王曉芳. 變論域模糊控制器伸縮因子的選擇方法. 信息與控制, 2010, 39(5): 536-541.
21.	Kozuki T, Mizoguchi H, Asano Y, et al. Design methodology for the thorax and shoulder of human mimetic musculoskeletal humanoid Kenshiro—a thorax structure with rib like surface//IEEE/RSJ International Conference on Intelligent Robots & Systems. Vilamoura: IEEE, 2012: 3687-3692.
22.	吳常鋮, 宋愛國, 曾洪, 等. 基于sEMG和GRNN的手部輸出力估計. 儀器儀表學報, 2017, 38(1): 97-104.
23.	Greff K, Srivastava R K, Koutnik J, et al. LSTM: A search space odyssey. IEEE Trans Neural Netw Learn Syst, 2016, 28(10): 2222-2232.

1. 樊瑜波. 可穿戴醫療/健康技術——生物醫學工程的機遇和挑戰. 生物醫學工程學雜志, 2016, 33(1): 1.
2. Blank A A, French J A, Pehlivan A U, et al. Current trends in robot-assisted upper-limb stroke rehabilitation: Promoting patient engagement in therapy. Curr Phys Med Rehabil Rep, 2014, 2(3): 184-195.
3. 黃小海, 喻洪流, 王金超, 等. 中央驅動式多自由度上肢康復訓練機器人研究. 生物醫學工程學雜志, 2018, 35(3): 452-459.
4. 王露露, 胡鑫, 胡杰, 等. 一種上肢外骨骼康復機器人的控制系統研究. 生物醫學工程學雜志, 2016, 33(6): 1168-1175.
5. 李夢曉, 馮麗娟, 張福蓉, 等. 鏡像視覺反饋療法在康復訓練中的研究進展. 中國康復理論與實踐, 2017, 23(12): 1403-1406.
6. Sarasola-Sanz A, Irastorza-Landa N, López-Larraz E, et al. Design and efectiveness evaluation of mirror myoelectric interfaces: a novel method to restore movement in hemiplegic patients. Sci Rep, 2018, 8(1): 16688.
7. Wang W W, Fu L C. Mirror therapy with an exoskeleton upper-limb robot based on IMU measurement system//IEEE International Workshop on Medical Measurements & Applications. Bari: IEEE, 2011: 370-375.
8. Gao Y, Su Y, Dong W, et al. Intention detection in upper limb kinematics rehabilitation using a GP-based control strategy//IEEE/RSJ International Conference on Intelligent Robots & Systems(IROS). Hamburg: IEEE, 2015: 5032-5038.
9. Kim H J, Lee G C, Song C H. Effect of functional electrical stimulation with mirror therapy on upper extremity motor function in poststroke patients. J Stroke Cerebrovasc, 2014, 23(4): 655-661.
10. Kojima K, Ikuno K, Morii Y, et al. Feasibility study of a combined treatment of electromyography-triggered neuromuscular stimulation and mirror therapy in stroke patients: A randomized crossover trial. Neurorehabilitation, 2014, 34(2): 235.
11. Stevens J A, Stoykov M E P. Using motor imagery in the rehabilitation of hemiparesis. Arch Phys Med Rehabil, 2003, 84(7): 1090-1092.
12. Ding L, Wang X, Guo X, et al. Camera-based mirror visual feedback: Potential to improve motor preparation in stroke patients. IEEE Trans Neural Syst Rehabil Eng, 2018, 26(9): 1897-1905.
13. Tang S, Lin C, Barsotti M, et al. Kinematic synergy of multi-DoF movement in upper limb and its application for rehabilitation exoskeleton motion planning. Front Neurorobot, 2019, 13(99): 1-13.
14. Rakesh K, Niclas B, Gutierrez-Farewik E M, et al. A survey of human shoulder functional kinematic representations. Med Biol Eng Comput, 2019, 57: 339-367.
15. Niyetkaliyev A S, Hussain S, Ghayesh M H, et al. Review on design and control aspects of robotic shoulder rehabilitation orthoses. IEEE Trans Hum-Mach Syst, 2017, 47(6): 1134-1145.
16. Sabatelli S, Galgani M, Fanucci L, et al. A double-stage kalman filter for orientation tracking with an integrated processor in 9-D IMU. IEEE Trans Instrum Meas, 2013, 62(3): 590-598.
17. Watanabe T, Miyazawa T, Shibasaki J. A study on IMU-based stride length estimation for motor disabled subjects: A comparison under different calculation methods of rotation matrix//IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). Las Vegas: IEEE, 2018: 70-73.
18. 李洪興. 變論域自適應模糊控制器. 中國科學: E輯, 1999, 25(1): 32-42.
19. 郭海剛, 李洪興, 胡凱. 一類變論域自適應模糊控制器. 模糊系統與數學, 2011, 25(6): 1-9.
20. 邵誠, 董希文, 王曉芳. 變論域模糊控制器伸縮因子的選擇方法. 信息與控制, 2010, 39(5): 536-541.
21. Kozuki T, Mizoguchi H, Asano Y, et al. Design methodology for the thorax and shoulder of human mimetic musculoskeletal humanoid Kenshiro—a thorax structure with rib like surface//IEEE/RSJ International Conference on Intelligent Robots & Systems. Vilamoura: IEEE, 2012: 3687-3692.
22. 吳常鋮, 宋愛國, 曾洪, 等. 基于sEMG和GRNN的手部輸出力估計. 儀器儀表學報, 2017, 38(1): 97-104.
23. Greff K, Srivastava R K, Koutnik J, et al. LSTM: A search space odyssey. IEEE Trans Neural Netw Learn Syst, 2016, 28(10): 2222-2232.

Journal of Biomedical Engineering

Mirror-type rehabilitation training with dynamic adjustment and assistance for shoulder joint

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