Research on assist-as-needed control strategy of wrist function-rehabilitation robot_Journal of Biomedical Engineering

Authors：

WANG Jiajin ^1,2 , ZUO Guokun ² ,  ZHANG Jiaji ² , SHI Changcheng ² , SONG Tao ² , GUO Shuai ¹

1. Department of Mechanical Automation Engineering, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, P.R.China;
2. Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Science, Ningbo, Zhejiang 315201, P.R.China;

Corresponding author：

ZHANG Jiaji, Email: zhangjiaji@nimte.ac.cn

Keywords：

wrist function-rehabilitation; robot; assist-as-needed; impedance control; minimum jerk trajectory

DOI：

10.7507/1001-5515.201902023

Video：

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Abstract Full text Figures/Tables Video References Cited by

In order to stimulate the patients' active participation in the process of robot-assisted rehabilitation training of stroke patients, the rehabilitation robots should provide assistant torque to patients according to their rehabilitation needs. This paper proposed an assist-as-needed control strategy for wrist rehabilitation robots. Firstly, the ability evaluation rules were formulated and the patient's ability was evaluated according to the rules. Then the controller was designed. Based on the evaluation results, the controller can calculate the assistant torque needed by the patient to complete the rehabilitation training task and send commands to motor. Finally, the motor is controlled to output the commanded value, which assists the patient to complete the rehabilitation training task. The control strategy was implemented to the wrist function rehabilitation robot, which could achieve the training effect of assist-as-needed and could avoid the surge of assistance torque. In addition, therapists can adjust multiple parameters in the ability evaluation rules online to customize the difficulty of tasks for patients with different rehabilitation status. The method proposed in this paper does not rely on the information from force sensor, which reduces development costs and is easy to implement.

Citation： WANG Jiajin, ZUO Guokun, ZHANG Jiaji, SHI Changcheng, SONG Tao, GUO Shuai. Research on assist-as-needed control strategy of wrist function-rehabilitation robot. Journal of Biomedical Engineering, 2020, 37(1): 129-135. doi: 10.7507/1001-5515.201902023 Copy

1.	吴宏健, 李莉娜, 李龙, 等. 脑卒中后手功能康复机器人综合干预研究进展. 生物医学工程学杂志, 2019, 36(1): 151-156.
2.	Cao Jinghui, Xie Shengquan, Das R, et al. Control strategies for effective robot assisted gait rehabilitation: the state of art and future prospects. Med Eng Phys, 2014, 36(12): 1555-1566.
3.	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. Current Physical Medicine and Rehabilitation Reports, 2014, 2(3): 184-195.
4.	Hogan N, Krebs H I, Rohrer B, et al. Motions or muscles? Some behavioral factors underlying robotic assistance of motor recovery. J Rehabil Res Dev, 2006, 43(5): 605-618.
5.	尹贵, 张小栋, 陈江城, 等. 下肢康复机器人按需辅助自适应控制方法. 西安交通大学学报, 2017, 51(10): 39-46.
6.	Wolbrecht E T, Chan V, Reinkensmeyer D J, et al. Optimizing compliant, model-based robotic assistance to promote neurorehabilitation. IEEE Trans Neural Syst Rehabil Eng, 2008, 16(3): 286-297.
7.	Pehlivan A U, Sergi F, O'malley M K. A Subject-Adaptive controller for wrist robotic rehabilitation. IEEE-ASME Transactions on Mechatronics, 2015, 20(3): 1338-1350.
8.	Pehlivan A U, Losey D P, O'malley M K. Minimal Assist-as-Needed (mAAN) controller for upper limb robotic rehabilitation. IEEE Transactions on Robotics, 2016, 32(1): 113-124.
9.	Hogan N. Impedance control-An approach to manipulation. I-Theory. J Dyn Syst Meas Control, 1985, 107(1): 1-24.
10.	Krebs H I, Palazzolo J J, Dipietro L, et al. Rehabilitation robotics: performance-based progressive robot-assisted therapy. Auton Robots, 2003, 15(1): 7-20.
11.	Guidali M, Duschau-Wicke A, Broggi S, et al. A robotic system to train activities of daily living in a virtual environment. Med Biol Eng Comput, 2011, 49(10): 1213-1223.
12.	Duschau-Wicke A, von Zitzewitz J, Caprez A, et al. Path control: a method for patient-cooperative robot-aided gait rehabilitation. IEEE Trans Neural Syst Rehabil Eng, 2010, 18(1): 38-48.
13.	Banala S K, Agrawal S K, Kim S H, et al. Novel gait adaptation and neuromotor training results using an active leg exoskeleton. IEEE-ASME Transactions on Mechatronics, 2010, 15(2): 216-225.
14.	Vaisman L, Dipietro L, Krebs H I. A comparative analysis of speed profile models for wrist pointing movements. IEEE Trans Neural Syst Rehabil Eng, 2013, 21(5): 756-766.
15.	Flash T, Hogan N. The coordination of arm movements: an experimentally confirmed mathematical model. J Neurosci, 1985, 5(7): 1688-1703.
16.	Rahman M M, Ikeura R, Mizutani K. Investigation of the impedance characteristic of human arm for development of robots to cooperate with humans. JSME International Journal, Series C: Mechanical Systems, Machine Elements and Manufacturing, 2002, 45(2): 510-518.
17.	Vitiello N, Lenzi T, Roccella S, et al. NEUROExos: a powered elbow exoskeleton for physical rehabilitation. IEEE Transactions on Robotics, 2013, 29(1): 220-235.
18.	Brian A H, Dupont P, Wit C D. A survey of models, analysis tools and compensation methods for the control of machines with friction. Automatica, 1994, 30(7): 1083-1138.

1. 吴宏健, 李莉娜, 李龙, 等. 脑卒中后手功能康复机器人综合干预研究进展. 生物医学工程学杂志, 2019, 36(1): 151-156.
2. Cao Jinghui, Xie Shengquan, Das R, et al. Control strategies for effective robot assisted gait rehabilitation: the state of art and future prospects. Med Eng Phys, 2014, 36(12): 1555-1566.
3. 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. Current Physical Medicine and Rehabilitation Reports, 2014, 2(3): 184-195.
4. Hogan N, Krebs H I, Rohrer B, et al. Motions or muscles? Some behavioral factors underlying robotic assistance of motor recovery. J Rehabil Res Dev, 2006, 43(5): 605-618.
5. 尹贵, 张小栋, 陈江城, 等. 下肢康复机器人按需辅助自适应控制方法. 西安交通大学学报, 2017, 51(10): 39-46.
6. Wolbrecht E T, Chan V, Reinkensmeyer D J, et al. Optimizing compliant, model-based robotic assistance to promote neurorehabilitation. IEEE Trans Neural Syst Rehabil Eng, 2008, 16(3): 286-297.
7. Pehlivan A U, Sergi F, O'malley M K. A Subject-Adaptive controller for wrist robotic rehabilitation. IEEE-ASME Transactions on Mechatronics, 2015, 20(3): 1338-1350.
8. Pehlivan A U, Losey D P, O'malley M K. Minimal Assist-as-Needed (mAAN) controller for upper limb robotic rehabilitation. IEEE Transactions on Robotics, 2016, 32(1): 113-124.
9. Hogan N. Impedance control-An approach to manipulation. I-Theory. J Dyn Syst Meas Control, 1985, 107(1): 1-24.
10. Krebs H I, Palazzolo J J, Dipietro L, et al. Rehabilitation robotics: performance-based progressive robot-assisted therapy. Auton Robots, 2003, 15(1): 7-20.
11. Guidali M, Duschau-Wicke A, Broggi S, et al. A robotic system to train activities of daily living in a virtual environment. Med Biol Eng Comput, 2011, 49(10): 1213-1223.
12. Duschau-Wicke A, von Zitzewitz J, Caprez A, et al. Path control: a method for patient-cooperative robot-aided gait rehabilitation. IEEE Trans Neural Syst Rehabil Eng, 2010, 18(1): 38-48.
13. Banala S K, Agrawal S K, Kim S H, et al. Novel gait adaptation and neuromotor training results using an active leg exoskeleton. IEEE-ASME Transactions on Mechatronics, 2010, 15(2): 216-225.
14. Vaisman L, Dipietro L, Krebs H I. A comparative analysis of speed profile models for wrist pointing movements. IEEE Trans Neural Syst Rehabil Eng, 2013, 21(5): 756-766.
15. Flash T, Hogan N. The coordination of arm movements: an experimentally confirmed mathematical model. J Neurosci, 1985, 5(7): 1688-1703.
16. Rahman M M, Ikeura R, Mizutani K. Investigation of the impedance characteristic of human arm for development of robots to cooperate with humans. JSME International Journal, Series C: Mechanical Systems, Machine Elements and Manufacturing, 2002, 45(2): 510-518.
17. Vitiello N, Lenzi T, Roccella S, et al. NEUROExos: a powered elbow exoskeleton for physical rehabilitation. IEEE Transactions on Robotics, 2013, 29(1): 220-235.
18. Brian A H, Dupont P, Wit C D. A survey of models, analysis tools and compensation methods for the control of machines with friction. Automatica, 1994, 30(7): 1083-1138.

Journal of Biomedical Engineering

Research on assist-as-needed control strategy of wrist function-rehabilitation robot

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