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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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.	庄存波, 刘检华, 熊辉, 等. 产品数字孪生体的内涵、体系结构及其发展趋势. 计算机集成制造系统, 2017, 23(4): 753-768.
2.	刘霞. 2022年医疗健康领域五大技术趋势. 科技日报, 2022-01-21(A4).
3.	Marco V, Gordon C, Fulvia T, 等. 欧洲虚拟生理人(英文). 医用生物力学, 2008, 23(1): 19-25.
4.	王成焘, 王冬梅, 白雪岭, 等. 人体骨肌系统生物力学. 北京: 科学出版社, 2021.
5.	赵沁平, 李帅, 宋震, 等. 虚拟生理人体建模与仿真关键技术研究进展. 中国科学基金, 2022, 36(2): 187-197.
6.	陶飞, 刘蔚然, 张萌, 等. 数字孪生五维模型及十大领域应用. 计算机集成制造系统, 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. 一种人体肌肉的参数化建模及变形方法研究. 系统仿真学报, 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.	Röhrle 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, Röhrle 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. 人体骨骼肌生物力学建模与仿真. 哈尔滨: 哈尔滨理工大学, 2019.
14.	马闯. 基于肌浆网环境的骨骼肌多尺度建模与仿真. 哈尔滨: 哈尔滨理工大学, 2021.
15.	宋学官, 何西旺, 李昆鹏, 等. 人体骨骼数字孪生的构建方法及应用. 机械工程学报, 2022, 58(18): 218-228.
16.	郭建峤, 王言冰, 田强, 等. 人体肌骨的多柔体系统动力学研究进展. 力学进展, 2022, 52(2): 253-310.
17.	胡若晨. 基于模式识别和肌力估计的高密度肌电控制方法研究. 北京: 中国科学技术大学, 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.	范湘华, 贾高永, 严志强. MR在肩关节损伤的诊断价值分析. 浙江创伤外科, 2022, 27(4): 787-789.
20.	阮鹏, 孙斯琴, 杨凡, 等. 高压电击伤家兔模型肢体坏死损伤的诊断: 基于磁共振成像分析. 分子影像学杂志, 2021,44(4): 668-672.
21.	梁冬丽. MRI在肩关节冈上肌钙化性肌腱炎诊断中的应用价值. 临床医药文献电子杂志, 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, 黄凯, 等. 一种肌肉动态收缩的高密度表面肌电信号分解新方法. 生物医学工程学杂志, 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.	刘伟. 基于机器学习的人体动作局部特征点识别仿真. 计算机仿真, 2021, 38(6): 387-391.
27.	郑敏, 蔡怡瑶, 张美琴. 人体下肢骨骼CT数据的重建与步态仿真. 机械设计与制造, 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.	刘丹, 方梓涛, 路绍元, 等. 基于人体数字孪生思想的构建肩胛提肌高保真数字孪生体方法: 中国, 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, 罗睿铭, 戴瑞, 等. 基于Opensim人机耦合仿真的上骨骼设计方法. 机械设计. 2022, 39(3): 123-129.
36.	陈亮, 钱竞光. 基于OpenSim对负重蹲起动作的多体动力学仿真及验证. 辽宁体育科技, 2022, 44(3): 82-87.
37.	陈琦, 刘放, 王智政. 人机携行外骨骼双腿交替多相行走建模与仿真. 机械传动, 2022, 46(7): 80-85.
38.	王颖, 马剑雄, 柏豪豪, 等. 股骨颈骨折术后不同动作时股骨力学特性研究. 中国中西医结合外科杂志, 2021, 27(3): 477-482.
39.	唐庆玉,徐升,庄海红.可视化人体医学图像数据库.仪器仪表学报,2000,21.Suppl 1):11-14,21.
40.	李安安, 刘谦, 曾绍群, 等. 高分辨数字人体三维结构数据集的构建与可视化. 科学通报, 2008, 53(10): 1189-1195.
41.	李增惠. 中国数字化虚拟人体的科技问题——香山科学会议第174次学术讨论会. 中国基础科学, 2002(3): 37-40.
42.	张绍祥, 王平安, 刘正津, 等.首套中国男、女数字化可视人体结构数据的可视化研究. 第三军医大学学报, 2003, 25(7): 563-565.
43.	原林, 唐雷, 黄文华, 等. 虚拟中国人男性一号(VCH-M1)数据集研究. 第一军医大学学报, 2003, 23(6): 520-523.
44.	余安胜, 张海东, 李风梅, 等. 人体穴位标本断面切割方法的研究. 针刺研究, 2002(3): 224-227.
45.	吴朝晖. 加强虚拟生理人体研究-共促医工学科对话-助力解决未来医学重大问题//专题:双清论坛“虚拟生理人体与医学应用”, 北京:国家自然科学基金委员会, 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. 庄存波, 刘检华, 熊辉, 等. 产品数字孪生体的内涵、体系结构及其发展趋势. 计算机集成制造系统, 2017, 23(4): 753-768.
2. 刘霞. 2022年医疗健康领域五大技术趋势. 科技日报, 2022-01-21(A4).
3. Marco V, Gordon C, Fulvia T, 等. 欧洲虚拟生理人(英文). 医用生物力学, 2008, 23(1): 19-25.
4. 王成焘, 王冬梅, 白雪岭, 等. 人体骨肌系统生物力学. 北京: 科学出版社, 2021.
5. 赵沁平, 李帅, 宋震, 等. 虚拟生理人体建模与仿真关键技术研究进展. 中国科学基金, 2022, 36(2): 187-197.
6. 陶飞, 刘蔚然, 张萌, 等. 数字孪生五维模型及十大领域应用. 计算机集成制造系统, 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. 一种人体肌肉的参数化建模及变形方法研究. 系统仿真学报, 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. Röhrle 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, Röhrle 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. 人体骨骼肌生物力学建模与仿真. 哈尔滨: 哈尔滨理工大学, 2019.
14. 马闯. 基于肌浆网环境的骨骼肌多尺度建模与仿真. 哈尔滨: 哈尔滨理工大学, 2021.
15. 宋学官, 何西旺, 李昆鹏, 等. 人体骨骼数字孪生的构建方法及应用. 机械工程学报, 2022, 58(18): 218-228.
16. 郭建峤, 王言冰, 田强, 等. 人体肌骨的多柔体系统动力学研究进展. 力学进展, 2022, 52(2): 253-310.
17. 胡若晨. 基于模式识别和肌力估计的高密度肌电控制方法研究. 北京: 中国科学技术大学, 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. 范湘华, 贾高永, 严志强. MR在肩关节损伤的诊断价值分析. 浙江创伤外科, 2022, 27(4): 787-789.
20. 阮鹏, 孙斯琴, 杨凡, 等. 高压电击伤家兔模型肢体坏死损伤的诊断: 基于磁共振成像分析. 分子影像学杂志, 2021,44(4): 668-672.
21. 梁冬丽. MRI在肩关节冈上肌钙化性肌腱炎诊断中的应用价值. 临床医药文献电子杂志, 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, 黄凯, 等. 一种肌肉动态收缩的高密度表面肌电信号分解新方法. 生物医学工程学杂志, 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. 刘伟. 基于机器学习的人体动作局部特征点识别仿真. 计算机仿真, 2021, 38(6): 387-391.
27. 郑敏, 蔡怡瑶, 张美琴. 人体下肢骨骼CT数据的重建与步态仿真. 机械设计与制造, 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. 刘丹, 方梓涛, 路绍元, 等. 基于人体数字孪生思想的构建肩胛提肌高保真数字孪生体方法: 中国, 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, 罗睿铭, 戴瑞, 等. 基于Opensim人机耦合仿真的上骨骼设计方法. 机械设计. 2022, 39(3): 123-129.
36. 陈亮, 钱竞光. 基于OpenSim对负重蹲起动作的多体动力学仿真及验证. 辽宁体育科技, 2022, 44(3): 82-87.
37. 陈琦, 刘放, 王智政. 人机携行外骨骼双腿交替多相行走建模与仿真. 机械传动, 2022, 46(7): 80-85.
38. 王颖, 马剑雄, 柏豪豪, 等. 股骨颈骨折术后不同动作时股骨力学特性研究. 中国中西医结合外科杂志, 2021, 27(3): 477-482.
39. 唐庆玉,徐升,庄海红.可视化人体医学图像数据库.仪器仪表学报,2000,21.Suppl 1):11-14,21.
40. 李安安, 刘谦, 曾绍群, 等. 高分辨数字人体三维结构数据集的构建与可视化. 科学通报, 2008, 53(10): 1189-1195.
41. 李增惠. 中国数字化虚拟人体的科技问题——香山科学会议第174次学术讨论会. 中国基础科学, 2002(3): 37-40.
42. 张绍祥, 王平安, 刘正津, 等.首套中国男、女数字化可视人体结构数据的可视化研究. 第三军医大学学报, 2003, 25(7): 563-565.
43. 原林, 唐雷, 黄文华, 等. 虚拟中国人男性一号(VCH-M1)数据集研究. 第一军医大学学报, 2003, 23(6): 520-523.
44. 余安胜, 张海东, 李风梅, 等. 人体穴位标本断面切割方法的研究. 针刺研究, 2002(3): 224-227.
45. 吴朝晖. 加强虚拟生理人体研究-共促医工学科对话-助力解决未来医学重大问题//专题:双清论坛“虚拟生理人体与医学应用”, 北京:国家自然科学基金委员会, 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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