Fatigue analysis of upper limb rehabilitation based on surface electromyography signal and motion capture_Journal of Biomedical Engineering

Authors：

XU Zhao ,  LU Jian , PAN Weijie , HE Kailun

Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guiyang 550025, P. R. China;

Corresponding?author：

LU Jian, Email: 305515940@qq.com

Keywords：

Electromyography fatigue threshold; Motion capture; Biomechanics; Upper limb fatigue analysis

DOI：

10.7507/1001-5515.202108026

Video：

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At present, fatigue state monitoring of upper limb movement generally relies solely on surface electromyographic signal (sEMG) to identify and classify fatigue, resulting in unstable results and certain limitations. This paper introduces the sEMG signal recognition and motion capture technology into the fatigue state monitoring process and proposes a fatigue analysis method combining an improved EMG fatigue threshold algorithm and biomechanical analysis. In this study, the right upper limb load elbow flexion test was used to simultaneously collect the biceps brachii sEMG signal and upper limb motion capture data, and at the same time the Borg Fatigue Subjective and Self-awareness Scale were used to record the fatigue feelings of the subjects. Then, the fatigue analysis method combining the EMG fatigue threshold algorithm and the biomechanical analysis was combined with four single types: mean power frequency (MPF), spectral moments ratio (SMR), fuzzy approximate entropy (fApEn) and Lempel-Ziv complexity (LZC). The test results of the evaluation index fatigue evaluation method were compared. The test results show that the method in this paper has a recognition rate of 98.6% for the overall fatigue state and 97%, 100%, and 99% for the three states of ease, transition and fatigue, which are more advantageous than other methods. The research results of this paper prove that the method in this paper can effectively prevent secondary injury caused by overtraining during upper limb exercises, and is of great significance for fatigue monitoring.

Citation： XU Zhao, LU Jian, PAN Weijie, HE Kailun. Fatigue analysis of upper limb rehabilitation based on surface electromyography signal and motion capture. Journal of Biomedical Engineering, 2022, 39(1): 92-102. doi: 10.7507/1001-5515.202108026 Copy

1.	湯緯地. 基于表面肌電的上肢運動分析關健技術研究. 合肥: 中國科學技術大學, 2021.
2.	張政委. 基于表面肌電的短視頻用戶上肢肌肉疲勞特性研究. 天津: 天津科技大學, 2020.
3.	劉彬. 基于肌肉疲勞屬性的個體最大耐久時間建模與實驗研究. 北京: 清華大學, 2018.
4.	Matsumoto T, Ito K, Moritani T. The relationship between anaerobic threshold and electromyographic fatigue threshold in college-women. European Journal of Applied Physiology and Occupational Physiology, 1991, 63(1): 1-5.
5.	Kang S K, Kim J, Kwon M, et al. Objectivity and validity of EMG method in estimating anaerobic threshold. Int J Sports Med, 2014, 35(9): 737-742.
6.	王琳, 付榮榮, 張陳, 等. 基于生物力學分析Q值對頸肌疲勞的反映效果. 儀器儀表學報, 2017, 38(4): 878-885.
7.	劉曉光, 李夢楠, 王立玲, 等. 基于表面肌電信號的康復過程中肌疲勞有效性分析. 生物醫學工程學雜志, 2019, 36(1): 80-84, 100.
8.	李成程. 基于sEMG的不同負載下手腕角度預測研究. 武漢:武漢科技大學, 2020.
9.	劉春波. 人體解剖生理學. 北京: 人民衛生出版社, 2010: 89-103.
10.	趙文華, 叢琳. 體力活動劃分: 不同類型體力活動的代謝體力活動劃分: 不同類型體力活動的代謝. 衛生研究, 2004, 33(2): 246-249.
11.	Yan A F, Voorhees C C, Beck K H, et al. A social ecological assessment of physical activity among urban adolescents. Am J Health Behav, 2014, 38(3): 379-391.
12.	Lewis Z H, Markides K S, Ottenbacher K J, et al. The role of physical activity and physical function on the risk of falls in older Mexican americans. J Aging Phys Act, 2016, 24(3): 342-349.
13.	李玉章. 表面肌電在體育中的應用. 上海: 復旦大學出版社, 2015: 275.
14.	郭伏, 人因工程學[M]. 北京: 機械工業出版社, 2005: 125-165.
15.	Promsri A, Mohr M, Federolf P. Principal postural acceleration and myoelectric activity: Interrelationship and relevance for characterizing neuromuscular function in postural control. Hum Mov Sci, 2021, 77(5): 102792.
16.	Li L. Mirror motion recognition method about upper limb rehabilitation robot based on sEMG. Journal of Computational Methods in Sciences and Engineering, 2021, 21(4): 1021-1029.
17.	Li K, Zhang J, Wang L, et al. A review of the key technologies for sEMG-based human-robot interaction systems. Biomedical Signal Processing and Control, 2020, 62: 1145-1169.
18.	Luo G F, Chang C M, Shih Y F. The effects of muscle fatigue on scapulothoracic joint position sense and neuromuscular performance. Musculoskelet Sci Pract, 2021, 56: 102461.
19.	Akizuki K, Yazaki S, Echizenya Y, et al. Anaerobic threshold and salivary α-amylase during incremental exercise. The Society of Physical Therapy Science, 2014, 26(7): 1059-1063.
20.	Al Harrach M, Afsharipour B, Boudaoud S, et al. Extraction of the brachialis muscle activity using HD-sEMG technique and canonical correlation analysis. Annu Int Conf IEEE Eng Med Biol Soc, 2016, 2016: 2378-2381.
21.	張冰, 楊錫讓, 練碧貞, 等. 臨界負荷、肌電圖疲勞閾值與無氧閾值的關系研究. 體育科學, 1995, 1995(6): 57-60.
22.	趙琦. 體能訓練理論與方法. 南京: 東南大學出版社, 2017: 263.
23.	Guo Q H, Maggi K. Interpretation of scale in paired quadrat variance methods. Journal of Vegetation Science, 2004, 15(6): 763-770.
24.	Wei P F, Lu Z Z, Ruan W B, et al. Regional sensitivity analysis using revised mean and variance ratio functions. Reliability Engineering and System Safety, 2014, 121: 121-135.
25.	Valentin S, Zsoldos R R. Surface electromyography in animal biomechanics: a systematic review. J Electromyogr Kinesiol, 2016, 28: 167-183.
26.	De Nooij R, Kallenberg L A, Hermens H J. Evaluating the effect of electrode location on surface EMG amplitude of the m. erector spinae p. longissimus dorsi. Journal of Electromyography and Kinesiology, 2009, 19(4): e257-e266.
27.	Kiran M, Ramakrishnan S. Classification of muscle fatigue in dynamic contraction using surface electromyography signals and multifractal singularity spectral analysis. J Dyn Syst Meas Control, 2016, 138(11): 138-149.
28.	Rodriguez-Falces J, Izquierdo M, González-Izal M, et al. Comparison of the power spectral changes of the voluntary surface electromyogram and M wave during intermittent maximal voluntary contractions. Eur J Appl Physiol, 2014, 114(9): 1943-1954.
29.	Kahl L, Hofmann U G. Comparison of algorithms to quantify muscle fatigue in upper limb muscles based on sEMG signals. Med Eng Phys, 2016, 38(11): 1260-1269.
30.	Shi J, Cai Y, Zhu J, et al. SEMG-based hand motion recognition using cumulative residual entropy and extreme learning machine. Med Biol Eng Comput, 2013, 51(4): 417-427.
31.	Wang Lejun, Wang Yuting, Ma Aidi, et al. A comparative study of EMG indices in muscle fatigue evaluation based on grey relational analysis during all-out cycling exercise. Biomed Res Int, 2018, 2018: 9341215.

1. 湯緯地. 基于表面肌電的上肢運動分析關健技術研究. 合肥: 中國科學技術大學, 2021.
2. 張政委. 基于表面肌電的短視頻用戶上肢肌肉疲勞特性研究. 天津: 天津科技大學, 2020.
3. 劉彬. 基于肌肉疲勞屬性的個體最大耐久時間建模與實驗研究. 北京: 清華大學, 2018.
4. Matsumoto T, Ito K, Moritani T. The relationship between anaerobic threshold and electromyographic fatigue threshold in college-women. European Journal of Applied Physiology and Occupational Physiology, 1991, 63(1): 1-5.
5. Kang S K, Kim J, Kwon M, et al. Objectivity and validity of EMG method in estimating anaerobic threshold. Int J Sports Med, 2014, 35(9): 737-742.
6. 王琳, 付榮榮, 張陳, 等. 基于生物力學分析Q值對頸肌疲勞的反映效果. 儀器儀表學報, 2017, 38(4): 878-885.
7. 劉曉光, 李夢楠, 王立玲, 等. 基于表面肌電信號的康復過程中肌疲勞有效性分析. 生物醫學工程學雜志, 2019, 36(1): 80-84, 100.
8. 李成程. 基于sEMG的不同負載下手腕角度預測研究. 武漢:武漢科技大學, 2020.
9. 劉春波. 人體解剖生理學. 北京: 人民衛生出版社, 2010: 89-103.
10. 趙文華, 叢琳. 體力活動劃分: 不同類型體力活動的代謝體力活動劃分: 不同類型體力活動的代謝. 衛生研究, 2004, 33(2): 246-249.
11. Yan A F, Voorhees C C, Beck K H, et al. A social ecological assessment of physical activity among urban adolescents. Am J Health Behav, 2014, 38(3): 379-391.
12. Lewis Z H, Markides K S, Ottenbacher K J, et al. The role of physical activity and physical function on the risk of falls in older Mexican americans. J Aging Phys Act, 2016, 24(3): 342-349.
13. 李玉章. 表面肌電在體育中的應用. 上海: 復旦大學出版社, 2015: 275.
14. 郭伏, 人因工程學[M]. 北京: 機械工業出版社, 2005: 125-165.
15. Promsri A, Mohr M, Federolf P. Principal postural acceleration and myoelectric activity: Interrelationship and relevance for characterizing neuromuscular function in postural control. Hum Mov Sci, 2021, 77(5): 102792.
16. Li L. Mirror motion recognition method about upper limb rehabilitation robot based on sEMG. Journal of Computational Methods in Sciences and Engineering, 2021, 21(4): 1021-1029.
17. Li K, Zhang J, Wang L, et al. A review of the key technologies for sEMG-based human-robot interaction systems. Biomedical Signal Processing and Control, 2020, 62: 1145-1169.
18. Luo G F, Chang C M, Shih Y F. The effects of muscle fatigue on scapulothoracic joint position sense and neuromuscular performance. Musculoskelet Sci Pract, 2021, 56: 102461.
19. Akizuki K, Yazaki S, Echizenya Y, et al. Anaerobic threshold and salivary α-amylase during incremental exercise. The Society of Physical Therapy Science, 2014, 26(7): 1059-1063.
20. Al Harrach M, Afsharipour B, Boudaoud S, et al. Extraction of the brachialis muscle activity using HD-sEMG technique and canonical correlation analysis. Annu Int Conf IEEE Eng Med Biol Soc, 2016, 2016: 2378-2381.
21. 張冰, 楊錫讓, 練碧貞, 等. 臨界負荷、肌電圖疲勞閾值與無氧閾值的關系研究. 體育科學, 1995, 1995(6): 57-60.
22. 趙琦. 體能訓練理論與方法. 南京: 東南大學出版社, 2017: 263.
23. Guo Q H, Maggi K. Interpretation of scale in paired quadrat variance methods. Journal of Vegetation Science, 2004, 15(6): 763-770.
24. Wei P F, Lu Z Z, Ruan W B, et al. Regional sensitivity analysis using revised mean and variance ratio functions. Reliability Engineering and System Safety, 2014, 121: 121-135.
25. Valentin S, Zsoldos R R. Surface electromyography in animal biomechanics: a systematic review. J Electromyogr Kinesiol, 2016, 28: 167-183.
26. De Nooij R, Kallenberg L A, Hermens H J. Evaluating the effect of electrode location on surface EMG amplitude of the m. erector spinae p. longissimus dorsi. Journal of Electromyography and Kinesiology, 2009, 19(4): e257-e266.
27. Kiran M, Ramakrishnan S. Classification of muscle fatigue in dynamic contraction using surface electromyography signals and multifractal singularity spectral analysis. J Dyn Syst Meas Control, 2016, 138(11): 138-149.
28. Rodriguez-Falces J, Izquierdo M, González-Izal M, et al. Comparison of the power spectral changes of the voluntary surface electromyogram and M wave during intermittent maximal voluntary contractions. Eur J Appl Physiol, 2014, 114(9): 1943-1954.
29. Kahl L, Hofmann U G. Comparison of algorithms to quantify muscle fatigue in upper limb muscles based on sEMG signals. Med Eng Phys, 2016, 38(11): 1260-1269.
30. Shi J, Cai Y, Zhu J, et al. SEMG-based hand motion recognition using cumulative residual entropy and extreme learning machine. Med Biol Eng Comput, 2013, 51(4): 417-427.
31. Wang Lejun, Wang Yuting, Ma Aidi, et al. A comparative study of EMG indices in muscle fatigue evaluation based on grey relational analysis during all-out cycling exercise. Biomed Res Int, 2018, 2018: 9341215.

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