Human muscle fatigue monitoring method and its application for exoskeleton interactive control_Journal of Biomedical Engineering

Authors：

NIU Huiqi ^1,2,3 , ZHANG Bi ^1,2 , LIU Ligang ^1,2,4 , ZHAO Yiwen ^1,2 ,  ZHAO Xingang ^1,2

1. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, P. R. China;
2. Institutes of Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, P. R. China;
3. College of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang 110159, P. R. China;
4. College of Information Science and Engineering, Northeastern University, Shenyang 110819, P. R. China;

Corresponding author：

ZHAO Xingang, Email: zhaoxingang@sia.cn

Keywords：

Exoskeleton rehabilitation robot; Electromyography signals; Hammerstein model; Fatigue monitoring; Impedance control

DOI：

10.7507/1001-5515.202211020

Video：

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Aiming at the human-computer interaction problem during the movement of the rehabilitation exoskeleton robot, this paper proposes an adaptive human-computer interaction control method based on real-time monitoring of human muscle state. Considering the efficiency of patient health monitoring and rehabilitation training, a new fatigue assessment algorithm was proposed. The method fully combined the human neuromuscular model, and used the relationship between the model parameter changes and the muscle state to achieve the classification of muscle fatigue state on the premise of ensuring the accuracy of the fatigue trend. In order to ensure the safety of human-computer interaction, a variable impedance control algorithm with this algorithm as the supervision link was proposed. On the basis of not adding redundant sensors, the evaluation algorithm was used as the perceptual decision-making link of the control system to monitor the muscle state in real time and carry out the robot control of fault-tolerant mechanism decision-making, so as to achieve the purpose of improving wearing comfort and improving the efficiency of rehabilitation training. Experiments show that the proposed human-computer interaction control method is effective and universal, and has broad application prospects.

Citation： NIU Huiqi, ZHANG Bi, LIU Ligang, ZHAO Yiwen, ZHAO Xingang. Human muscle fatigue monitoring method and its application for exoskeleton interactive control. Journal of Biomedical Engineering, 2023, 40(4): 654-662. doi: 10.7507/1001-5515.202211020 Copy

1.	Li X, Wu X, Liu B, et al. Improving access to assistive technologies for persons with disabilities in China: Practice, opportunities and challenges. Global Perspectives On Assistive Technology, 2019: 480-494.
2.	程洪, 黄瑞, 邱静, 等. 康复机器人及其临床应用综述. 机器人, 2021, 43(5): 606-619.
3.	Esquenazi A, Talaty M. Robotics for lower limb rehabilitation. Phys Med Rehabil Clin N Am, 2019, 30(2): 385-397.
4.	赵新刚, 谈晓伟, 张弼. 柔性下肢外骨骼机器人研究进展及关键技术分析. 机器人, 2020, 42(3): 365-384.
5.	韩稷钰, 王衍鸿, 万大千. 下肢外骨骼康复机器人的研究进展及发展趋势. 上海交通大学学报(医学版), 2022, 42(2): 241-246.
6.	Thacham Poyil A, Steuber V, Amirabdollahian F. Adaptive robot mediated upper limb training using electromyogram-based muscle fatigue indicators. PLoS One, 2020, 15(5): e0233545.
7.	Enoka R M, Duchateau J. Muscle fatigue: what, why and how it influences muscle function. J Physiol, 2008, 586(1): 11-23.
8.	Barsotti A, Khalaf K, Gan D. Muscle fatigue evaluation with EMG and Acceleration data: a case study// Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Piscataway: IEEE, 2020: 3138-3141.
9.	Peternel L, Tsagarakis N, Caldwell D, et al. Robot adaptation to human physical fatigue in human–robot co-manipulation. Auton Robot, 2017, 42(5): 1011-1021.
10.	Artemiadis P K, Kyriakopoulos K J. An EMG-based robot control scheme robust to time-varying EMG signal features. IEEE Trans Inf Technol Biomed, 2010, 14(3): 582-588.
11.	Li Z, Zhang M, Zhang X, et al. Assessment of cerebral oxygenation during prolonged simulated driving using near infrared spectroscopy: its implications for fatigue development. Eur J Appl Physiol, 2009, 107(3): 281-287.
12.	Huang X, Ai Q. A comparison of assessment methods for muscle fatigue in muscle fatigue contraction// Balas V, Jain L, Zhao X. Information technology and intelligent transportation systems. Berlin: Springer, 2017: 491-501.
13.	Cifrek M, Medved V, Tonković S, et al. Surface EMG based muscle fatigue evaluation in biomechanics. Clin Biomech, 2009, 24(4): 327-340.
14.	Fernando J B, Yoshioka M, Ozawa J. Estimation of muscle fatigue by ratio of mean frequency to average rectified value from surface electromyography// Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Piscataway: IEEE, 2016: 5303-5306.
15.	Nagamine K, Iwasawa Y, Matsuo Y, et al. An estimation of wheelchair user’s muscle fatigue by accelerometers on smart devices// Proceedings of the Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers. New York: ACM, 2015: 57-60.
16.	Meeusen R, Watson P, Hasegawa H, et al. Central fatigue. Sports Med, 2006, 36(10): 881-909.
17.	Ramos G, Vaz J R, Mendonça G V, et al. Fatigue evaluation through machine learning and a global fatigue descriptor. J Healthc Eng, 2020, 2020: 6484129.
18.	Chandra S, Hayashibe M, Thondiyath A. Muscle fatigue induced hand tremor clustering in dynamic laparoscopic manipulation. IEEE T Syst Man Cy-S, 2020, 50(12): 5420-5431.
19.	Aryal A, Ghahramani A, Becerik-Gerber B. Monitoring fatigue in construction workers using physiological measurements. Automat Constr, 2017, 82: 154-165.
20.	张弼. 基于块状结构模型的非线性自适应控制方法研究. 沈阳: 东北大学, 2017.
21.	Mehmood A, Raja M A Z, Shi P, et al. Weighted differential evolution-based heuristic computing for identification of Hammerstein systems in electrically stimulated muscle modeling. Soft Comput, 2022, 26: 8929-8945.
22.	Zhang Z, Chu B, Liu Y, et al. Multimuscle functional-electrical-stimulation-based wrist tremor suppression using repetitive control. IEEE-ASME T Mech, 2022, 27(5): 3988-3998.
23.	Li Y, Chen W, Chen J, et al. Neural network based modeling and control of elbow joint motion under functional electrical stimulation. Neurocomputing, 2019, 340: 171-179.
24.	Le F, Markovsky I, Freeman C T, et al. Identification of electrically stimulated muscle models of stroke patients. Control Eng Prac, 2010, 18(4): 396-407.
25.	Le F, Markovsky I, Freeman C T, et al. Recursive identification of Hammerstein systems with application to electrically stimulated muscle. Control Eng Prac, 2012, 20(4): 386-396.
26.	Buchanan T S, Lloyd D G, Manal K, et al. Neuromusculoskeletal modeling: estimation of muscle forces and joint moments and movements from measurements of neural command. J Appl Biomech, 2004, 20(4): 367-395.
27.	Hunt K J, Munih M, Donaldson N D N, et al. Investigation of the Hammerstein hypothesis in the modeling of electrically stimulated muscle. IEEE Trans Biomed Eng, 1998, 45(8): 998-1009.
28.	Wu A R, Dzeladini F, Brug T J H, et al. An adaptive neuromuscular controller for assistive lower-limb exoskeletons: A preliminary study on subjects with spinal cord injury. Front Neurorobot, 2017, 11: 30.
29.	Markowitz J, Krishnaswamy P, Eilenberg M F, et al. Speed adaptation in a powered transtibial prosthesis controlled with a neuromuscular model. Philos Trans R Soc B Biol Sci, 2011, 366(1570): 1621-1631.
30.	Lloyd D G, Besier T F. An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo. J Biomech, 2003, 36(6): 765-776.
31.	Han J, Ding Q, Xiong A, et al. A state-space EMG model for the estimation of continuous joint movements. IEEE Trans Indus Electron, 2015, 62(7): 4267-4275.
32.	Borg G A. Psychophysical bases of perceived exertion. Med Sci Sports Exerc, 1982, 14(5): 377-381.
33.	方崇智, 萧德云. 过程辨识. 北京: 清华大学出版杜, 1988: 160-165.
34.	Golub G H, Van Loan C F. Matrix computations. Second edition. London: The Johns Hopkins University Press, 1989.
35.	Hogan N. Impedance control: An approach to manipulation: Part II—Implementation. J Dyn Sys Meas Control, 1985, 107(1): 8-16.
36.	蔡自兴, 徐光祐. 人工智能及其应用. 北京: 清华大学出版社, 2020.
37.	Hermens H J, Freriks B, Disselhorst-Klug C, et al. Development of recommendations for SEMG sensors and sensor placement procedures. J Electromyogr Kinesiol, 2000, 10(5): 361-374.
38.	Tan X, Zhang B, Liu G, et al. Phase variable based recognition of human locomotor activities across diverse gait patterns. IEEE T Hum-Mach Syst, 2021, 51(6): 684-695.
39.	Tan X, Zhang B, Liu G, et al. Cadence-insensitive soft exoskeleton design with adaptive gait state detection and iterative force control. IEEE T Autom Sci Eng, 2022, 19(3): 2108-2121.
40.	Chen W, Li G, Li N, et al. Soft exoskeleton with fully actuated thumb movements for grasping assistance. IEEE T Robot, 2022, 38(4): 2194-2207.

1. Li X, Wu X, Liu B, et al. Improving access to assistive technologies for persons with disabilities in China: Practice, opportunities and challenges. Global Perspectives On Assistive Technology, 2019: 480-494.
2. 程洪, 黄瑞, 邱静, 等. 康复机器人及其临床应用综述. 机器人, 2021, 43(5): 606-619.
3. Esquenazi A, Talaty M. Robotics for lower limb rehabilitation. Phys Med Rehabil Clin N Am, 2019, 30(2): 385-397.
4. 赵新刚, 谈晓伟, 张弼. 柔性下肢外骨骼机器人研究进展及关键技术分析. 机器人, 2020, 42(3): 365-384.
5. 韩稷钰, 王衍鸿, 万大千. 下肢外骨骼康复机器人的研究进展及发展趋势. 上海交通大学学报(医学版), 2022, 42(2): 241-246.
6. Thacham Poyil A, Steuber V, Amirabdollahian F. Adaptive robot mediated upper limb training using electromyogram-based muscle fatigue indicators. PLoS One, 2020, 15(5): e0233545.
7. Enoka R M, Duchateau J. Muscle fatigue: what, why and how it influences muscle function. J Physiol, 2008, 586(1): 11-23.
8. Barsotti A, Khalaf K, Gan D. Muscle fatigue evaluation with EMG and Acceleration data: a case study// Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Piscataway: IEEE, 2020: 3138-3141.
9. Peternel L, Tsagarakis N, Caldwell D, et al. Robot adaptation to human physical fatigue in human–robot co-manipulation. Auton Robot, 2017, 42(5): 1011-1021.
10. Artemiadis P K, Kyriakopoulos K J. An EMG-based robot control scheme robust to time-varying EMG signal features. IEEE Trans Inf Technol Biomed, 2010, 14(3): 582-588.
11. Li Z, Zhang M, Zhang X, et al. Assessment of cerebral oxygenation during prolonged simulated driving using near infrared spectroscopy: its implications for fatigue development. Eur J Appl Physiol, 2009, 107(3): 281-287.
12. Huang X, Ai Q. A comparison of assessment methods for muscle fatigue in muscle fatigue contraction// Balas V, Jain L, Zhao X. Information technology and intelligent transportation systems. Berlin: Springer, 2017: 491-501.
13. Cifrek M, Medved V, Tonković S, et al. Surface EMG based muscle fatigue evaluation in biomechanics. Clin Biomech, 2009, 24(4): 327-340.
14. Fernando J B, Yoshioka M, Ozawa J. Estimation of muscle fatigue by ratio of mean frequency to average rectified value from surface electromyography// Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Piscataway: IEEE, 2016: 5303-5306.
15. Nagamine K, Iwasawa Y, Matsuo Y, et al. An estimation of wheelchair user’s muscle fatigue by accelerometers on smart devices// Proceedings of the Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers. New York: ACM, 2015: 57-60.
16. Meeusen R, Watson P, Hasegawa H, et al. Central fatigue. Sports Med, 2006, 36(10): 881-909.
17. Ramos G, Vaz J R, Mendonça G V, et al. Fatigue evaluation through machine learning and a global fatigue descriptor. J Healthc Eng, 2020, 2020: 6484129.
18. Chandra S, Hayashibe M, Thondiyath A. Muscle fatigue induced hand tremor clustering in dynamic laparoscopic manipulation. IEEE T Syst Man Cy-S, 2020, 50(12): 5420-5431.
19. Aryal A, Ghahramani A, Becerik-Gerber B. Monitoring fatigue in construction workers using physiological measurements. Automat Constr, 2017, 82: 154-165.
20. 张弼. 基于块状结构模型的非线性自适应控制方法研究. 沈阳: 东北大学, 2017.
21. Mehmood A, Raja M A Z, Shi P, et al. Weighted differential evolution-based heuristic computing for identification of Hammerstein systems in electrically stimulated muscle modeling. Soft Comput, 2022, 26: 8929-8945.
22. Zhang Z, Chu B, Liu Y, et al. Multimuscle functional-electrical-stimulation-based wrist tremor suppression using repetitive control. IEEE-ASME T Mech, 2022, 27(5): 3988-3998.
23. Li Y, Chen W, Chen J, et al. Neural network based modeling and control of elbow joint motion under functional electrical stimulation. Neurocomputing, 2019, 340: 171-179.
24. Le F, Markovsky I, Freeman C T, et al. Identification of electrically stimulated muscle models of stroke patients. Control Eng Prac, 2010, 18(4): 396-407.
25. Le F, Markovsky I, Freeman C T, et al. Recursive identification of Hammerstein systems with application to electrically stimulated muscle. Control Eng Prac, 2012, 20(4): 386-396.
26. Buchanan T S, Lloyd D G, Manal K, et al. Neuromusculoskeletal modeling: estimation of muscle forces and joint moments and movements from measurements of neural command. J Appl Biomech, 2004, 20(4): 367-395.
27. Hunt K J, Munih M, Donaldson N D N, et al. Investigation of the Hammerstein hypothesis in the modeling of electrically stimulated muscle. IEEE Trans Biomed Eng, 1998, 45(8): 998-1009.
28. Wu A R, Dzeladini F, Brug T J H, et al. An adaptive neuromuscular controller for assistive lower-limb exoskeletons: A preliminary study on subjects with spinal cord injury. Front Neurorobot, 2017, 11: 30.
29. Markowitz J, Krishnaswamy P, Eilenberg M F, et al. Speed adaptation in a powered transtibial prosthesis controlled with a neuromuscular model. Philos Trans R Soc B Biol Sci, 2011, 366(1570): 1621-1631.
30. Lloyd D G, Besier T F. An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo. J Biomech, 2003, 36(6): 765-776.
31. Han J, Ding Q, Xiong A, et al. A state-space EMG model for the estimation of continuous joint movements. IEEE Trans Indus Electron, 2015, 62(7): 4267-4275.
32. Borg G A. Psychophysical bases of perceived exertion. Med Sci Sports Exerc, 1982, 14(5): 377-381.
33. 方崇智, 萧德云. 过程辨识. 北京: 清华大学出版杜, 1988: 160-165.
34. Golub G H, Van Loan C F. Matrix computations. Second edition. London: The Johns Hopkins University Press, 1989.
35. Hogan N. Impedance control: An approach to manipulation: Part II—Implementation. J Dyn Sys Meas Control, 1985, 107(1): 8-16.
36. 蔡自兴, 徐光祐. 人工智能及其应用. 北京: 清华大学出版社, 2020.
37. Hermens H J, Freriks B, Disselhorst-Klug C, et al. Development of recommendations for SEMG sensors and sensor placement procedures. J Electromyogr Kinesiol, 2000, 10(5): 361-374.
38. Tan X, Zhang B, Liu G, et al. Phase variable based recognition of human locomotor activities across diverse gait patterns. IEEE T Hum-Mach Syst, 2021, 51(6): 684-695.
39. Tan X, Zhang B, Liu G, et al. Cadence-insensitive soft exoskeleton design with adaptive gait state detection and iterative force control. IEEE T Autom Sci Eng, 2022, 19(3): 2108-2121.
40. Chen W, Li G, Li N, et al. Soft exoskeleton with fully actuated thumb movements for grasping assistance. IEEE T Robot, 2022, 38(4): 2194-2207.

Journal of Biomedical Engineering

Human muscle fatigue monitoring method and its application for exoskeleton interactive control

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