Advances in digital twins technology of human skeletal muscle_Journal of Biomedical Engineering

Authors：

LIU Dan ¹ ,  HE Ling ¹ , YANG Di ² , ZHANG Jie ³ , CHEN Jiadui ¹ , YANG Guanci ¹

1. Key lab of Advanced Manufacturing Technology of Ministry of Education, Guizhou University, Guiyang 550025, P.R. China;
2. The First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang 550001, P.R. China;
3. College of Computer Science and Technology, Guizhou University, Guiyang 550025, P.R. China;

Corresponding?author：

HE Ling, Email: 529252287@qq.com

Keywords：

Skeletal muscle digital twins; Digital twins modeling; Data collection technology; Simulation analysis; Simulation platform; Human medical image database

DOI：

10.7507/1001-5515.202210007

Video：

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Abstract Full text Figures/Tables Video References Cited by

The human skeletal muscle drives skeletal movement through contraction. Embedding its functional information into the human morphological framework and constructing a digital twin of skeletal muscle for simulating physical and physiological functions of skeletal muscle are of great significance for the study of "virtual physiological humans". Based on relevant literature both domestically and internationally, this paper firstly summarizes the technical framework for constructing skeletal muscle digital twins, and then provides a review from five aspects including skeletal muscle digital twins modeling technology, skeletal muscle data collection technology, simulation analysis technology, simulation platform and human medical image database. On this basis, it is pointed out that further research is needed in areas such as skeletal muscle model generalization, accuracy improvement, and model coupling. The methods and means of constructing skeletal muscle digital twins summarized in the paper are expected to provide reference for researchers in this field, and the development direction pointed out can serve as the next focus of research.

Citation： LIU Dan, HE Ling, YANG Di, ZHANG Jie, CHEN Jiadui, YANG Guanci. Advances in digital twins technology of human skeletal muscle. Journal of Biomedical Engineering, 2023, 40(4): 784-791. doi: 10.7507/1001-5515.202210007 Copy

1.	莊存波, 劉檢華, 熊輝, 等. 產品數字孿生體的內涵、體系結構及其發展趨勢. 計算機集成制造系統, 2017, 23(4): 753-768.
2.	劉霞. 2022年醫療健康領域五大技術趨勢. 科技日報, 2022-01-21(A4).
3.	Marco V, Gordon C, Fulvia T, 等. 歐洲虛擬生理人(英文). 醫用生物力學, 2008, 23(1): 19-25.
4.	王成燾, 王冬梅, 白雪嶺, 等. 人體骨肌系統生物力學. 北京: 科學出版社, 2021.
5.	趙沁平, 李帥, 宋震, 等. 虛擬生理人體建模與仿真關鍵技術研究進展. 中國科學基金, 2022, 36(2): 187-197.
6.	陶飛, 劉蔚然, 張萌, 等. 數字孿生五維模型及十大領域應用. 計算機集成制造系統, 2019, 25(1): 1-18.
7.	Roussellet V, Rumman N A, Canezin F, et al. Dynamic implicit muscles for character skinning. Comput Graph, 2018, 77: 227-239.
8.	倪娜, 何坤金, 朱新成, 等. 一種人體肌肉的參數化建模及變形方法研究. 系統仿真學報, 2022, 34(5): 1109-1117.
9.	Shan X, Otsuka S, Li L, et al. Inhomogeneous and anisotropic mechanical properties of the triceps surae muscles and aponeuroses in vivo during submaximal muscle contraction. J Biomech, 2021, 121(9): 110396.
10.	Pinel S, Kelp N Y, Bugeja J M, et al. Quantity versus quality: age-related differences in muscle volume, intramuscular fat, and mechanical properties in the triceps surae. Exp Gerontol. 2021, 156: 111594.
11.	R?hrle O, Davidson J B, Pullan A J. A physiologically based, multi-scale model of skeletal muscle structure and function. Front Physiol, 2012, 3: 358.
12.	Heidlauf T, R?hrle O. Modeling the chemoelectromechanical behavior of skeletal muscle using the parallel open-source software library OpenCMISS. Comput Math Methods Med, 2013, 2013: 517287.
13.	耿慧玉. 人體骨骼肌生物力學建模與仿真. 哈爾濱: 哈爾濱理工大學, 2019.
14.	馬闖. 基于肌漿網環境的骨骼肌多尺度建模與仿真. 哈爾濱: 哈爾濱理工大學, 2021.
15.	宋學官, 何西旺, 李昆鵬, 等. 人體骨骼數字孿生的構建方法及應用. 機械工程學報, 2022, 58(18): 218-228.
16.	郭建嶠, 王言冰, 田強, 等. 人體肌骨的多柔體系統動力學研究進展. 力學進展, 2022, 52(2): 253-310.
17.	胡若晨. 基于模式識別和肌力估計的高密度肌電控制方法研究. 北京: 中國科學技術大學, 2021.
18.	Tang Xiao, Chen Maoqi, Chen Xun, et al. Hybrid encoder-decoder deep networks for decoding neural drive information towards precise muscle force estimation//2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, Canada: IEEE, 2020: 176-179.
19.	范湘華, 賈高永, 嚴志強. MR在肩關節損傷的診斷價值分析. 浙江創傷外科, 2022, 27(4): 787-789.
20.	阮鵬, 孫斯琴, 楊凡, 等. 高壓電擊傷家兔模型肢體壞死損傷的診斷: 基于磁共振成像分析. 分子影像學雜志, 2021,44(4): 668-672.
21.	梁冬麗. MRI在肩關節岡上肌鈣化性肌腱炎診斷中的應用價值. 臨床醫藥文獻電子雜志, 2020, 7(42): 182.
22.	Wang K, Wang L, Deng Z, et al. Influence of passive elements on prediction of intradiscal pressure and muscle activation in lumbar musculoskeletal models. Comput Methods Programs Biomed, 2019.177: 39-46.
23.	Mikayla Q, Daniel K. Effects of fat tissue on chronic muscle injuries. The FASEB J, 2020, 34: 63-75.
24.	何金保, 管冰蕾, 黃凱, 等. 一種肌肉動態收縮的高密度表面肌電信號分解新方法. 生物醫學工程學雜志, 2021, 38(6):1081-1086.
25.	Chen Zhiyong, Wang Qingsuo, Bi Yunce, et.al. Analyzing human muscle state with flexible sensors. J Sensors, 2022, 2022: 5227955.
26.	劉偉. 基于機器學習的人體動作局部特征點識別仿真. 計算機仿真, 2021, 38(6): 387-391.
27.	鄭敏, 蔡怡瑤, 張美琴. 人體下肢骨骼CT數據的重建與步態仿真. 機械設計與制造, 2021(11): 220-224.
28.	Tao Qing, Li Zhaobo, Lai Quanbao, et al. A dynamic evaluation mechanism of human upper limb muscle forces. Advances in Artificial Intelligence, Computation, and Data Science, Computational Biology, Springer, 2021: 303-317.
29.	Glitsch U, Heinrich K, Ellegast RP. Estimation of tibiofemoral and patellofemoral joint forces during squatting and kneeling. Appl Sci, 2022, 12(1): 255-268.
30.	Ma X, Xu J, Fang H, et al. Adaptive neural control for gait coordination of a lower limb prosthesis. Int J Mech Sci. 2022, 215: 106942.
31.	劉丹, 方梓濤, 路紹元, 等. 基于人體數字孿生思想的構建肩胛提肌高保真數字孿生體方法: 中國, CN114723886A. 2022-07-08.
32.	Corrado C, Razeghi O, Roney C, et al. Quantifying atrial anatomy uncertain from clinical data and its impact on electro-physiology simulation predictions. Med Image Anal, 2020, 61: 101626.
33.	Amuzescu B, Airini R, Epureanu F B, et al. Evolution of mathematical models of cardiomyocyte electrophysiology. Math Biosci, 2021, 334: 108567.
34.	Hao W, Sun P, Xu J, et al. Multiscale and monolithic arbitrary Lagrangian–Eulerian finite element method for a hemodynamic fluid-structure interaction problem involving aneurysms. J Comput Phys, 2021(433): 110181.
35.	范子珍, 羅睿銘, 戴瑞, 等. 基于Opensim人機耦合仿真的上骨骼設計方法. 機械設計. 2022, 39(3): 123-129.
36.	陳亮, 錢競光. 基于OpenSim對負重蹲起動作的多體動力學仿真及驗證. 遼寧體育科技, 2022, 44(3): 82-87.
37.	陳琦, 劉放, 王智政. 人機攜行外骨骼雙腿交替多相行走建模與仿真. 機械傳動, 2022, 46(7): 80-85.
38.	王穎, 馬劍雄, 柏豪豪, 等. 股骨頸骨折術后不同動作時股骨力學特性研究. 中國中西醫結合外科雜志, 2021, 27(3): 477-482.
39.	唐慶玉,徐升,莊海紅.可視化人體醫學圖像數據庫.儀器儀表學報,2000,21.Suppl 1):11-14,21.
40.	李安安, 劉謙, 曾紹群, 等. 高分辨數字人體三維結構數據集的構建與可視化. 科學通報, 2008, 53(10): 1189-1195.
41.	李增惠. 中國數字化虛擬人體的科技問題——香山科學會議第174次學術討論會. 中國基礎科學, 2002(3): 37-40.
42.	張紹祥, 王平安, 劉正津, 等.首套中國男、女數字化可視人體結構數據的可視化研究. 第三軍醫大學學報, 2003, 25(7): 563-565.
43.	原林, 唐雷, 黃文華, 等. 虛擬中國人男性一號(VCH-M1)數據集研究. 第一軍醫大學學報, 2003, 23(6): 520-523.
44.	余安勝, 張海東, 李風梅, 等. 人體穴位標本斷面切割方法的研究. 針刺研究, 2002(3): 224-227.
45.	吳朝暉. 加強虛擬生理人體研究-共促醫工學科對話-助力解決未來醫學重大問題//專題:雙清論壇“虛擬生理人體與醫學應用”, 北京:國家自然科學基金委員會, 2021:186-283.
46.	Jorge R N. Digital twins for personalized treatment development and clinical trials //Virtual Physiological Human Conference (VPH2022), Porto: VPH Institute, 2022.

1. 莊存波, 劉檢華, 熊輝, 等. 產品數字孿生體的內涵、體系結構及其發展趨勢. 計算機集成制造系統, 2017, 23(4): 753-768.
2. 劉霞. 2022年醫療健康領域五大技術趨勢. 科技日報, 2022-01-21(A4).
3. Marco V, Gordon C, Fulvia T, 等. 歐洲虛擬生理人(英文). 醫用生物力學, 2008, 23(1): 19-25.
4. 王成燾, 王冬梅, 白雪嶺, 等. 人體骨肌系統生物力學. 北京: 科學出版社, 2021.
5. 趙沁平, 李帥, 宋震, 等. 虛擬生理人體建模與仿真關鍵技術研究進展. 中國科學基金, 2022, 36(2): 187-197.
6. 陶飛, 劉蔚然, 張萌, 等. 數字孿生五維模型及十大領域應用. 計算機集成制造系統, 2019, 25(1): 1-18.
7. Roussellet V, Rumman N A, Canezin F, et al. Dynamic implicit muscles for character skinning. Comput Graph, 2018, 77: 227-239.
8. 倪娜, 何坤金, 朱新成, 等. 一種人體肌肉的參數化建模及變形方法研究. 系統仿真學報, 2022, 34(5): 1109-1117.
9. Shan X, Otsuka S, Li L, et al. Inhomogeneous and anisotropic mechanical properties of the triceps surae muscles and aponeuroses in vivo during submaximal muscle contraction. J Biomech, 2021, 121(9): 110396.
10. Pinel S, Kelp N Y, Bugeja J M, et al. Quantity versus quality: age-related differences in muscle volume, intramuscular fat, and mechanical properties in the triceps surae. Exp Gerontol. 2021, 156: 111594.
11. R?hrle O, Davidson J B, Pullan A J. A physiologically based, multi-scale model of skeletal muscle structure and function. Front Physiol, 2012, 3: 358.
12. Heidlauf T, R?hrle O. Modeling the chemoelectromechanical behavior of skeletal muscle using the parallel open-source software library OpenCMISS. Comput Math Methods Med, 2013, 2013: 517287.
13. 耿慧玉. 人體骨骼肌生物力學建模與仿真. 哈爾濱: 哈爾濱理工大學, 2019.
14. 馬闖. 基于肌漿網環境的骨骼肌多尺度建模與仿真. 哈爾濱: 哈爾濱理工大學, 2021.
15. 宋學官, 何西旺, 李昆鵬, 等. 人體骨骼數字孿生的構建方法及應用. 機械工程學報, 2022, 58(18): 218-228.
16. 郭建嶠, 王言冰, 田強, 等. 人體肌骨的多柔體系統動力學研究進展. 力學進展, 2022, 52(2): 253-310.
17. 胡若晨. 基于模式識別和肌力估計的高密度肌電控制方法研究. 北京: 中國科學技術大學, 2021.
18. Tang Xiao, Chen Maoqi, Chen Xun, et al. Hybrid encoder-decoder deep networks for decoding neural drive information towards precise muscle force estimation//2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, Canada: IEEE, 2020: 176-179.
19. 范湘華, 賈高永, 嚴志強. MR在肩關節損傷的診斷價值分析. 浙江創傷外科, 2022, 27(4): 787-789.
20. 阮鵬, 孫斯琴, 楊凡, 等. 高壓電擊傷家兔模型肢體壞死損傷的診斷: 基于磁共振成像分析. 分子影像學雜志, 2021,44(4): 668-672.
21. 梁冬麗. MRI在肩關節岡上肌鈣化性肌腱炎診斷中的應用價值. 臨床醫藥文獻電子雜志, 2020, 7(42): 182.
22. Wang K, Wang L, Deng Z, et al. Influence of passive elements on prediction of intradiscal pressure and muscle activation in lumbar musculoskeletal models. Comput Methods Programs Biomed, 2019.177: 39-46.
23. Mikayla Q, Daniel K. Effects of fat tissue on chronic muscle injuries. The FASEB J, 2020, 34: 63-75.
24. 何金保, 管冰蕾, 黃凱, 等. 一種肌肉動態收縮的高密度表面肌電信號分解新方法. 生物醫學工程學雜志, 2021, 38(6):1081-1086.
25. Chen Zhiyong, Wang Qingsuo, Bi Yunce, et.al. Analyzing human muscle state with flexible sensors. J Sensors, 2022, 2022: 5227955.
26. 劉偉. 基于機器學習的人體動作局部特征點識別仿真. 計算機仿真, 2021, 38(6): 387-391.
27. 鄭敏, 蔡怡瑤, 張美琴. 人體下肢骨骼CT數據的重建與步態仿真. 機械設計與制造, 2021(11): 220-224.
28. Tao Qing, Li Zhaobo, Lai Quanbao, et al. A dynamic evaluation mechanism of human upper limb muscle forces. Advances in Artificial Intelligence, Computation, and Data Science, Computational Biology, Springer, 2021: 303-317.
29. Glitsch U, Heinrich K, Ellegast RP. Estimation of tibiofemoral and patellofemoral joint forces during squatting and kneeling. Appl Sci, 2022, 12(1): 255-268.
30. Ma X, Xu J, Fang H, et al. Adaptive neural control for gait coordination of a lower limb prosthesis. Int J Mech Sci. 2022, 215: 106942.
31. 劉丹, 方梓濤, 路紹元, 等. 基于人體數字孿生思想的構建肩胛提肌高保真數字孿生體方法: 中國, CN114723886A. 2022-07-08.
32. Corrado C, Razeghi O, Roney C, et al. Quantifying atrial anatomy uncertain from clinical data and its impact on electro-physiology simulation predictions. Med Image Anal, 2020, 61: 101626.
33. Amuzescu B, Airini R, Epureanu F B, et al. Evolution of mathematical models of cardiomyocyte electrophysiology. Math Biosci, 2021, 334: 108567.
34. Hao W, Sun P, Xu J, et al. Multiscale and monolithic arbitrary Lagrangian–Eulerian finite element method for a hemodynamic fluid-structure interaction problem involving aneurysms. J Comput Phys, 2021(433): 110181.
35. 范子珍, 羅睿銘, 戴瑞, 等. 基于Opensim人機耦合仿真的上骨骼設計方法. 機械設計. 2022, 39(3): 123-129.
36. 陳亮, 錢競光. 基于OpenSim對負重蹲起動作的多體動力學仿真及驗證. 遼寧體育科技, 2022, 44(3): 82-87.
37. 陳琦, 劉放, 王智政. 人機攜行外骨骼雙腿交替多相行走建模與仿真. 機械傳動, 2022, 46(7): 80-85.
38. 王穎, 馬劍雄, 柏豪豪, 等. 股骨頸骨折術后不同動作時股骨力學特性研究. 中國中西醫結合外科雜志, 2021, 27(3): 477-482.
39. 唐慶玉,徐升,莊海紅.可視化人體醫學圖像數據庫.儀器儀表學報,2000,21.Suppl 1):11-14,21.
40. 李安安, 劉謙, 曾紹群, 等. 高分辨數字人體三維結構數據集的構建與可視化. 科學通報, 2008, 53(10): 1189-1195.
41. 李增惠. 中國數字化虛擬人體的科技問題——香山科學會議第174次學術討論會. 中國基礎科學, 2002(3): 37-40.
42. 張紹祥, 王平安, 劉正津, 等.首套中國男、女數字化可視人體結構數據的可視化研究. 第三軍醫大學學報, 2003, 25(7): 563-565.
43. 原林, 唐雷, 黃文華, 等. 虛擬中國人男性一號(VCH-M1)數據集研究. 第一軍醫大學學報, 2003, 23(6): 520-523.
44. 余安勝, 張海東, 李風梅, 等. 人體穴位標本斷面切割方法的研究. 針刺研究, 2002(3): 224-227.
45. 吳朝暉. 加強虛擬生理人體研究-共促醫工學科對話-助力解決未來醫學重大問題//專題:雙清論壇“虛擬生理人體與醫學應用”, 北京:國家自然科學基金委員會, 2021:186-283.
46. Jorge R N. Digital twins for personalized treatment development and clinical trials //Virtual Physiological Human Conference (VPH2022), Porto: VPH Institute, 2022.

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