Optimal Solution and Analysis of Muscular Force during Standing Balance_Journal of Biomedical Engineering

Authors：

WANG Hongrui ^1,2 ,  ZHENG Hui ¹ , LIU Kun ¹

1. College of Electric Engineering, Yanshan University, Qinhuangdao 066004, China;
2. College of Electronic and Information Engineering, Hebei University, Baoding 071000, China;

Corresponding author：

ZHENG Hui, Email: hui_zheng20080909@163.com

Keywords：

human standing balance; redundant muscular forces; multi-objective optimization; particle swarm optimization

DOI：

10.7507/1001-5515.20150011

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

The present study was aimed at the optimal solution of the main muscular force distribution in the lower extremity during standing balance of human. The movement musculoskeletal system of lower extremity was simplified to a physical model with 3 joints and 9 muscles. Then on the basis of this model, an optimum mathematical model was built up to solve the problem of redundant muscle forces. Particle swarm optimization (PSO) algorithm is used to calculate the single objective and multi-objective problem respectively. The numerical results indicated that the multi-objective optimization could be more reasonable to obtain the distribution and variation of the 9 muscular forces. Finally, the coordination of each muscle group during maintaining standing balance under the passive movement was qualitatively analyzed using the simulation results obtained.

Citation： WANG Hongrui, ZHENG Hui, LIU Kun. Optimal Solution and Analysis of Muscular Force during Standing Balance. Journal of Biomedical Engineering, 2015, 32(1): 59-66. doi: 10.7507/1001-5515.20150011 Copy

1.	唐刚.人体典型运动生物力学仿真分析[D].上海:上海交通大学, 2011.
2.	NEPTUNE R R. Optimization algorithm performance in determining optimal controls in human movement analyses[J]. J Biomech Eng, 1999, 121(2): 249-252.
3.	RAIKOVA R T, PRILUTSKY B I. Sensitivity of predicted muscle forces to parameters of the optimization-based human leg model revealed by analytical and numerical analyses[J]. J Biomech, 2001, 34(10): 1243-1255.
4.	SETH A, PANDY M G. A neuromusculoskeletal tracking method for estimating individual muscle forces in human movement[J]. J Biomech, 2007, 40(2): 356-366.
5.	SANTANA-QUINTERO L V, HERNANDEZ-DIAZ A G, MOLINA J, et al.DEMORS: A hybrid multi-objective optimization algorithm using differential evolution and rough set theory for constrained problems[J]. Computers & Operations Research, 2010, 37(3): 470-480.
6.	BENSGHAIER A, ROMDHANE L, BENOUEZDOU F. Multi-objective optimization to predict muscle tensions in a pinch function using genetic algorithm[J]. Comptes Rendus Mecanique, 2012, 340(3): 139-155.
7.	TAN D, LUO W, LIU Q. Multi-objective particle swarm optimization algorithm for engineering constrained optimization problems[C]//IEEE International Conference on Granular Computing, San Jose: 2009: 523-528.
8.	CARVALHO A B, POZO A. Measuring the convergence and diversity of CDAS multi-objective particle swarm optimization algorithms: a study of many-objective problems[J]. Neurocomputing, 2012, 75(1): 43-51.
9.	YANG K. An improved particle swarm optimization for multi-objective discrete optimization[C]//IEEE International Conference on Innovation Management and Industrial Engineering, Sanya: 2012: 219-222.
10.	肖金壮, 熊鹏, 王洪瑞, 等.被动运动下人体平衡调节过程线性特性分析[J].生物医学工程学杂志, 2012, 29(3): 420-423.
11.	孟红云, 张小华, 刘三阳.用于约束多目标优化问题的双群体差分进化算法[J].计算机学报, 2008, 31(2): 228-235.
12.	董旭良, 王建华.一种求解多目标优化问题的粒子群算法的研究[J].电子设计工程, 2013, 21(3): 36-39.
13.	CROWNINSHIELD R D, BRAND R A. A physiologically based criterion of muscle force prediction in locomotion[J]. J Biomech, 1981, 14(11): 793-801.
14.	YOSHIOKA S, NAGANO A, HAY D C, et al. The minimum required muscle force for a sit-to-stand task[J]. J Biomech, 2012, 45(4): 699-705.
15.	DELP S L. Surgery simulation: a computer graphics system to analyze and design musculoskeletal reconstructions of the lower limb[D]. Stanford: Stanford University, 1990.
16.	JO S, MASSAQUOI S G. A model of cerebellum stabilized and scheduled hybrid long-loop control of upright balance[J]. Biol Cybern, 2004, 91(3): 188-202.
17.	VALERO-CUEVAS F J, ZAJAC F E, BURGAR C G. Large index-fingertip forces are produced by subject-independent patterns of muscle excitation[J]. J Biomech, 1998, 31(8): 693-704.
18.	ANDERSON F C, PANDY M G. Static and dynamic optimization solutions for gait are practically equivalent[J]. J Biomech, 2001, 34(2): 153-161.

1. 唐刚.人体典型运动生物力学仿真分析[D].上海:上海交通大学, 2011.
2. NEPTUNE R R. Optimization algorithm performance in determining optimal controls in human movement analyses[J]. J Biomech Eng, 1999, 121(2): 249-252.
3. RAIKOVA R T, PRILUTSKY B I. Sensitivity of predicted muscle forces to parameters of the optimization-based human leg model revealed by analytical and numerical analyses[J]. J Biomech, 2001, 34(10): 1243-1255.
4. SETH A, PANDY M G. A neuromusculoskeletal tracking method for estimating individual muscle forces in human movement[J]. J Biomech, 2007, 40(2): 356-366.
5. SANTANA-QUINTERO L V, HERNANDEZ-DIAZ A G, MOLINA J, et al.DEMORS: A hybrid multi-objective optimization algorithm using differential evolution and rough set theory for constrained problems[J]. Computers & Operations Research, 2010, 37(3): 470-480.
6. BENSGHAIER A, ROMDHANE L, BENOUEZDOU F. Multi-objective optimization to predict muscle tensions in a pinch function using genetic algorithm[J]. Comptes Rendus Mecanique, 2012, 340(3): 139-155.
7. TAN D, LUO W, LIU Q. Multi-objective particle swarm optimization algorithm for engineering constrained optimization problems[C]//IEEE International Conference on Granular Computing, San Jose: 2009: 523-528.
8. CARVALHO A B, POZO A. Measuring the convergence and diversity of CDAS multi-objective particle swarm optimization algorithms: a study of many-objective problems[J]. Neurocomputing, 2012, 75(1): 43-51.
9. YANG K. An improved particle swarm optimization for multi-objective discrete optimization[C]//IEEE International Conference on Innovation Management and Industrial Engineering, Sanya: 2012: 219-222.
10. 肖金壮, 熊鹏, 王洪瑞, 等.被动运动下人体平衡调节过程线性特性分析[J].生物医学工程学杂志, 2012, 29(3): 420-423.
11. 孟红云, 张小华, 刘三阳.用于约束多目标优化问题的双群体差分进化算法[J].计算机学报, 2008, 31(2): 228-235.
12. 董旭良, 王建华.一种求解多目标优化问题的粒子群算法的研究[J].电子设计工程, 2013, 21(3): 36-39.
13. CROWNINSHIELD R D, BRAND R A. A physiologically based criterion of muscle force prediction in locomotion[J]. J Biomech, 1981, 14(11): 793-801.
14. YOSHIOKA S, NAGANO A, HAY D C, et al. The minimum required muscle force for a sit-to-stand task[J]. J Biomech, 2012, 45(4): 699-705.
15. DELP S L. Surgery simulation: a computer graphics system to analyze and design musculoskeletal reconstructions of the lower limb[D]. Stanford: Stanford University, 1990.
16. JO S, MASSAQUOI S G. A model of cerebellum stabilized and scheduled hybrid long-loop control of upright balance[J]. Biol Cybern, 2004, 91(3): 188-202.
17. VALERO-CUEVAS F J, ZAJAC F E, BURGAR C G. Large index-fingertip forces are produced by subject-independent patterns of muscle excitation[J]. J Biomech, 1998, 31(8): 693-704.
18. ANDERSON F C, PANDY M G. Static and dynamic optimization solutions for gait are practically equivalent[J]. J Biomech, 2001, 34(2): 153-161.

Journal of Biomedical Engineering

Optimal Solution and Analysis of Muscular Force during Standing Balance

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content