Multimodal assessment of lower limb muscle fatigue and variable stiffness impedance control method for exoskeletons_Journal of Biomedical Engineering

Authors：

LI Ruigang ^1,2 ,  ZHANG Bi ¹ , LI Jiwei ^1,3 , FENG Jun ^1,4 , BAO Ruilai ^1,5 , ZHANG Tingfeng ⁵ , ZHANG Liang ⁶ , ZHAO Xingang ^1,3

1. State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, P. R. China;
2. School of Software, Shenyang University of Technology, Shenyang 110870, P. R. China;
3. University of Chinese Academy of Sciences, Beijing 100049, P. R. China;
4. School of Information Science and Engineering, Northeastern University, Shenyang 110819, P. R. China;
5. School of Electrical Engineering, Liaoning University of Technology, Jinzhou, Liaoning 121000, P. R. China;
6. Department of Rehabilitation, Liaoning Provincial People's Hospital, Shenyang 110067, P. R. China;

Corresponding?author：

ZHANG Bi, Email: zhangbi@sia.cn

Keywords：

Surface electromyograph signal; A-mode ultrasound; Multimodal fusion; Fatigue assessment; Human-exoskeleton interaction

DOI：

10.7507/1001-5515.202508046

Video：

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Aiming at the problems of insufficient real-time human-computer interaction and limited fatigue monitoring of lower limb rehabilitation exoskeleton, a multi-modal evaluation of lower limb muscle fatigue and exoskeleton variable stiffness impedance control method are proposed. This method extracts the characteristics of surface electromyography time-frequency domain and A-mode ultrasound muscle thickness, combines ventilation and subjective fatigue scale, and uses support vector machine to construct a five-classification model of fatigue level to achieve state evaluation. In addition, this study further designs a variable stiffness impedance control strategy to dynamically adjust the exoskeleton parameters in real time according to the muscle state to improve the interaction safety. Experiments show that the classification accuracy of the multi-modal fusion model proposed in this study is 92.30%, and the system delay is less than 200 ms. In the walking test, the variable stiffness control strategy can further significantly reduce the net respiratory metabolic rate of the user compared with no exoskeleton or under built-in control assistance of exoskeleton. The method proposed in this paper realizes effective fatigue assessment and adaptive assistance, and provides a new idea for intelligent interaction of lower limb rehabilitation robots.

Citation： LI Ruigang, ZHANG Bi, LI Jiwei, FENG Jun, BAO Ruilai, ZHANG Tingfeng, ZHANG Liang, ZHAO Xingang. Multimodal assessment of lower limb muscle fatigue and variable stiffness impedance control method for exoskeletons. Journal of Biomedical Engineering, 2026, 43(5): 954-963. doi: 10.7507/1001-5515.202508046 Copy

1.	Roelofs J M B, Zandvliet S B, Schut I M, et al. Mild stroke, serious problems: limitations in balance and gait capacity and the impact on fall rate, and physical activity[J]. Neurorehabil Neural Repair, 2023, 37(11-12): 786-798.
2.	GBD 2016 Lifetime Risk of Stroke Collaborators. Global, regional, and country-specific lifetime risks of stroke, 1990 and 2016[J]. N Engl J Med, 2018, 379(25): 2429-2437.
3.	王隴德, 彭斌, 張鴻祺, 等. 《中國腦卒中防治報告2020》概要[J]. 中國腦血管病雜志, 2022, 19(2): 136-144.
4.	寧吉喆. 第七次全國人口普查主要數據情況[J]. 中國統計, 2021(5): 4-5.
5.	李素姣, 吳坤, 孟巧玲, 等. 腦卒中患者上肢功能智能評估系統研究進展[J]. 生物醫學工程學雜志, 2022, 39(3): 620-626, 632.
6.	趙新剛, 談曉偉, 張弼. 柔性下肢外骨骼機器人研究進展及關鍵技術分析[J]. 機器人, 2020, 42(3): 365-384.
7.	Baud R, Manzoori A R, Ijspeert A, et al. Review of control strategies for lower-limb exoskeletons to assist gait[J]. J Neuroeng Rehabil, 2021, 18: 119.
8.	de Miguel-Fernández J, Lobo-Prat J, Prinsen E, et al. Control strategies used in lower limb exoskeletons for gait rehabilitation after brain injury: a systematic review and analysis of clinical effectiveness[J]. J Neuroeng Rehabil, 2023, 20: 23.
9.	鐘旭, 張弼, 李紀桅, 等. 面向外骨骼的下肢多模態協同量化分析與康復評估方法[J]. 生物醫學工程學雜志, 2023, 40(5): 959-961.
10.	鄒鋒, 胡慶龍, 趙祥欣, 等. 基于sEMG信號的人體下肢空間運動意圖識別[J]. 機械設計與研究, 2024, 40(6): 84-87, 96.
11.	Mendez J, Murray R, Gabert L, et al. Continuous A-mode ultrasound-based prediction of transfemoral amputee prosthesis kinematics across different ambulation tasks[J]. IEEE Trans Biomed Eng, 2023, 71(1): 56-67.
12.	Zeng J, Zhou Y, Yang Y, et al. Fatigue-sensitivity comparison of sEMG and A-mode ultrasound based hand gesture recognition[J]. IEEE J Biomed Health Inform, 2021, 26(4): 1718-1725.
13.	Poyil A T, Steuber V, Amirabdollahian F. Adaptive robot mediated upper limb training using electromyogram-based muscle fatigue indicators[J]. PLoS One, 2020, 15(5): e0233545.
14.	Barsotti A, Khalaf K, Gan D. Muscle fatigue evaluation with emg and acceleration data: a case study[C]//2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal: IEEE, 2020: 3138-3141.
15.	Guo Y, Yang K, Wu Y. A multi-modality attention network for driver fatigue detection based on frontal EEG, EDA and PPG signals[J]. IEEE J Biomed Health Inform, 2025, 29(6): 4009-4022.
16.	Punitha N, Divya Bharathi K, Manuskandan S R, et al. Analysis of muscle fatigue progression using geometric features of surface electromyography signals and explainable XGBoost classifier: N. Punitha et al.[J]. J Med Biol Eng, 2024, 44(2): 191-197.
17.	Sun J, Zhang C, Liu G, et al. Detecting muscle fatigue during lower limb isometric contractions tasks: a machine learning approach[J]. Front Physiol, 2025, 16: 1547257.
18.	Alvarez J T, Jin Y, Choe D K, et al. Stimulation-induced muscle deformation measured with a-mode ultrasound correlates with muscle fatigue[J]. IEEE Trans Neural Syst Rehabil Eng, 2024, 33: 10-21.
19.	Lee J W, Hong S U, Lee J H, et al. Estimation of validity of a-mode ultrasound for measurements of muscle thickness and muscle quality[J]. Bioengineering (Basel), 2024, 11(2): 149.
20.	李明, 李慧, 喻洪流. 下肢外骨骼康復機器人的分類及其應用現狀[J]. 生物醫學工程學雜志, 2024, 41(4): 833-839.
21.	Etana B B, Malengier B, Timothy K, et al. A review on the recent developments in design and integration of electromyography textile electrodes for biosignal monitoring[J]. J Ind Text, 2023, 53: 15280837231175062.
22.	Peterman J E, Arena R, Myers J, et al. Reference standards for peak rating of perceived exertion during cardiopulmonary exercise testing: data from FRIEND[J]. Med Sci Sports Exerc, 2023, 55(1): 74-79.
23.	Zhao H, Qiu Z, Peng D, et al. Prediction of joint angles based on human lower limb surface electromyography[J]. Sensors, 2023, 23(12): 5404.
24.	陳玲玲, 張存, 宋曉偉, 等. 面向外骨骼機器人的肌肉功能網絡構建及分析[J]. 生物醫學工程學雜志, 2019, 36(4): 565-572.
25.	Yang X, Sun X, Zhou D, et al. Towards wearable A-mode ultrasound sensing for real-time finger motion recognition[J]. IEEE Trans Neural Syst Rehabil Eng, 2018, 26(6): 1199-1208.
26.	Sid'El Moctar S M, Rida I, Boudaoud S. Comprehensive review of feature extraction techniques for semg signal classification: from handcrafted features to deep learning approaches[J]. IRBM, 2024, 45(6): 100866.
27.	Muceli S, Merletti R. Tutorial. Frequency analysis of the surface EMG signal: best practices[J]. J Electromyogr Kinesiol, 2024, 79: 102937.
28.	張亞龍, 張世武, 孫帥帥, 等. 融合肌電信號與A型超聲的新型肌肉疲勞檢測方法[J]. 電子測量與儀器學報, 2022, 36(6): 13-21.
29.	Li H, Li D. Research on the recognition model of exercise fatigue based on the fusion of sEMG and ECG signals[J]. iScience, 2024, 27(4): 109365.
30.	Borg G A. Psychophysical bases of perceived exertion[J]. Med Sci Sports Exerc, 1982, 14(5): 377-381.
31.	Cerezuela-Espejo V, Courel-Ibá?ez J, Morán-Navarro R, et al. The relationship between lactate and ventilatory thresholds in runners: validity and reliability of exercise test performance parameters[J]. Front Physiol, 2018, 9: 1320.
32.	Welch J F, Kipp S, Sheel A W. Respiratory muscles during exercise: mechanics, energetics, and fatigue[J]. Curr Opin Physiol, 2019, 10: 102-109.
33.	Chen W, Zhang B, Tan X, et al. Hip-knee-ankle rehabilitation exoskeleton with compliant actuators: from human-robot interaction control to clinical evaluation[J]. IEEE Trans Robot, 2024, 41: 269-288.
34.	牛惠祺, 張弼, 劉麗剛, 等. 人體肌肉狀態疲勞監測及其在外骨骼交互控制中的應用[J]. 生物醫學工程學雜志, 2023, 40(4): 654-662.
35.	Sattari P, Ravanshid D, Nasiri R. Designing for practicality: a personalized and adaptive framework for real-time emg-based hand motor decoding[J]. J Neural Eng, 2025, 22(2): 026040.

1. Roelofs J M B, Zandvliet S B, Schut I M, et al. Mild stroke, serious problems: limitations in balance and gait capacity and the impact on fall rate, and physical activity[J]. Neurorehabil Neural Repair, 2023, 37(11-12): 786-798.
2. GBD 2016 Lifetime Risk of Stroke Collaborators. Global, regional, and country-specific lifetime risks of stroke, 1990 and 2016[J]. N Engl J Med, 2018, 379(25): 2429-2437.
3. 王隴德, 彭斌, 張鴻祺, 等. 《中國腦卒中防治報告2020》概要[J]. 中國腦血管病雜志, 2022, 19(2): 136-144.
4. 寧吉喆. 第七次全國人口普查主要數據情況[J]. 中國統計, 2021(5): 4-5.
5. 李素姣, 吳坤, 孟巧玲, 等. 腦卒中患者上肢功能智能評估系統研究進展[J]. 生物醫學工程學雜志, 2022, 39(3): 620-626, 632.
6. 趙新剛, 談曉偉, 張弼. 柔性下肢外骨骼機器人研究進展及關鍵技術分析[J]. 機器人, 2020, 42(3): 365-384.
7. Baud R, Manzoori A R, Ijspeert A, et al. Review of control strategies for lower-limb exoskeletons to assist gait[J]. J Neuroeng Rehabil, 2021, 18: 119.
8. de Miguel-Fernández J, Lobo-Prat J, Prinsen E, et al. Control strategies used in lower limb exoskeletons for gait rehabilitation after brain injury: a systematic review and analysis of clinical effectiveness[J]. J Neuroeng Rehabil, 2023, 20: 23.
9. 鐘旭, 張弼, 李紀桅, 等. 面向外骨骼的下肢多模態協同量化分析與康復評估方法[J]. 生物醫學工程學雜志, 2023, 40(5): 959-961.
10. 鄒鋒, 胡慶龍, 趙祥欣, 等. 基于sEMG信號的人體下肢空間運動意圖識別[J]. 機械設計與研究, 2024, 40(6): 84-87, 96.
11. Mendez J, Murray R, Gabert L, et al. Continuous A-mode ultrasound-based prediction of transfemoral amputee prosthesis kinematics across different ambulation tasks[J]. IEEE Trans Biomed Eng, 2023, 71(1): 56-67.
12. Zeng J, Zhou Y, Yang Y, et al. Fatigue-sensitivity comparison of sEMG and A-mode ultrasound based hand gesture recognition[J]. IEEE J Biomed Health Inform, 2021, 26(4): 1718-1725.
13. Poyil A T, Steuber V, Amirabdollahian F. Adaptive robot mediated upper limb training using electromyogram-based muscle fatigue indicators[J]. PLoS One, 2020, 15(5): e0233545.
14. Barsotti A, Khalaf K, Gan D. Muscle fatigue evaluation with emg and acceleration data: a case study[C]//2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal: IEEE, 2020: 3138-3141.
15. Guo Y, Yang K, Wu Y. A multi-modality attention network for driver fatigue detection based on frontal EEG, EDA and PPG signals[J]. IEEE J Biomed Health Inform, 2025, 29(6): 4009-4022.
16. Punitha N, Divya Bharathi K, Manuskandan S R, et al. Analysis of muscle fatigue progression using geometric features of surface electromyography signals and explainable XGBoost classifier: N. Punitha et al.[J]. J Med Biol Eng, 2024, 44(2): 191-197.
17. Sun J, Zhang C, Liu G, et al. Detecting muscle fatigue during lower limb isometric contractions tasks: a machine learning approach[J]. Front Physiol, 2025, 16: 1547257.
18. Alvarez J T, Jin Y, Choe D K, et al. Stimulation-induced muscle deformation measured with a-mode ultrasound correlates with muscle fatigue[J]. IEEE Trans Neural Syst Rehabil Eng, 2024, 33: 10-21.
19. Lee J W, Hong S U, Lee J H, et al. Estimation of validity of a-mode ultrasound for measurements of muscle thickness and muscle quality[J]. Bioengineering (Basel), 2024, 11(2): 149.
20. 李明, 李慧, 喻洪流. 下肢外骨骼康復機器人的分類及其應用現狀[J]. 生物醫學工程學雜志, 2024, 41(4): 833-839.
21. Etana B B, Malengier B, Timothy K, et al. A review on the recent developments in design and integration of electromyography textile electrodes for biosignal monitoring[J]. J Ind Text, 2023, 53: 15280837231175062.
22. Peterman J E, Arena R, Myers J, et al. Reference standards for peak rating of perceived exertion during cardiopulmonary exercise testing: data from FRIEND[J]. Med Sci Sports Exerc, 2023, 55(1): 74-79.
23. Zhao H, Qiu Z, Peng D, et al. Prediction of joint angles based on human lower limb surface electromyography[J]. Sensors, 2023, 23(12): 5404.
24. 陳玲玲, 張存, 宋曉偉, 等. 面向外骨骼機器人的肌肉功能網絡構建及分析[J]. 生物醫學工程學雜志, 2019, 36(4): 565-572.
25. Yang X, Sun X, Zhou D, et al. Towards wearable A-mode ultrasound sensing for real-time finger motion recognition[J]. IEEE Trans Neural Syst Rehabil Eng, 2018, 26(6): 1199-1208.
26. Sid'El Moctar S M, Rida I, Boudaoud S. Comprehensive review of feature extraction techniques for semg signal classification: from handcrafted features to deep learning approaches[J]. IRBM, 2024, 45(6): 100866.
27. Muceli S, Merletti R. Tutorial. Frequency analysis of the surface EMG signal: best practices[J]. J Electromyogr Kinesiol, 2024, 79: 102937.
28. 張亞龍, 張世武, 孫帥帥, 等. 融合肌電信號與A型超聲的新型肌肉疲勞檢測方法[J]. 電子測量與儀器學報, 2022, 36(6): 13-21.
29. Li H, Li D. Research on the recognition model of exercise fatigue based on the fusion of sEMG and ECG signals[J]. iScience, 2024, 27(4): 109365.
30. Borg G A. Psychophysical bases of perceived exertion[J]. Med Sci Sports Exerc, 1982, 14(5): 377-381.
31. Cerezuela-Espejo V, Courel-Ibá?ez J, Morán-Navarro R, et al. The relationship between lactate and ventilatory thresholds in runners: validity and reliability of exercise test performance parameters[J]. Front Physiol, 2018, 9: 1320.
32. Welch J F, Kipp S, Sheel A W. Respiratory muscles during exercise: mechanics, energetics, and fatigue[J]. Curr Opin Physiol, 2019, 10: 102-109.
33. Chen W, Zhang B, Tan X, et al. Hip-knee-ankle rehabilitation exoskeleton with compliant actuators: from human-robot interaction control to clinical evaluation[J]. IEEE Trans Robot, 2024, 41: 269-288.
34. 牛惠祺, 張弼, 劉麗剛, 等. 人體肌肉狀態疲勞監測及其在外骨骼交互控制中的應用[J]. 生物醫學工程學雜志, 2023, 40(4): 654-662.
35. Sattari P, Ravanshid D, Nasiri R. Designing for practicality: a personalized and adaptive framework for real-time emg-based hand motor decoding[J]. J Neural Eng, 2025, 22(2): 026040.

Journal of Biomedical Engineering

Multimodal assessment of lower limb muscle fatigue and variable stiffness impedance control method for exoskeletons

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