Optimising muscle parameters in musculoskeletal models using Monte Carlo simulation

Computer Methods in Biomechanics and Biomedical Engineering

Volumn 18, Issue 6, 2015, Pages 607-617

  Reed, Erik B ^a   Hanson, Andrea M ^a   Cavanagh, Peter R ^a  

^a UNIVERSITY OF WASHINGTON SCHOOL OF MEDICINE   (United States)

Author keywords

computer simulation; joint force; modelling; Monte Carlo; muscle activation; parametric testing

Indexed keywords

CHEMICAL ACTIVATION; COMPUTER SIMULATION; COMPUTER SOFTWARE; INTELLIGENT SYSTEMS; MODELS; MUSCLE; PHYSIOLOGICAL MODELS;

ACTIVATION PATTERNS; DEFAULT PARAMETERS; JOINT FORCES; MUSCLE ACTIVATION; MUSCULOSKELETAL MODEL; MUSCULOSKELETAL SIMULATION; PARAMETRIC TESTING; SIMULATION SOFTWARE;

MONTE CARLO METHODS;

ALGORITHM; ARTICLE; FORCE; HIP; HUMAN; KNOWLEDGE; MOLECULAR DYNAMICS; MONTE CARLO METHOD; MUSCLE STRENGTH; MUSCULOSKELETAL MODEL; MUSCULOSKELETAL SYSTEM PARAMETERS; NEUROPHYSIOLOGICAL RECRUITMENT; PHYSICAL MODEL; RECTUS FEMORIS MUSCLE; RIGIDITY; SENSITIVITY ANALYSIS; SIMULATION; VASTUS MEDIALIS MUSCLE; BIOMECHANICS; COMPUTER PROGRAM; COMPUTER SIMULATION; EXERCISE; JOINT; MOVEMENT (PHYSIOLOGY); MUSCLE; PATHOLOGY; PHYSIOLOGY; QUADRICEPS FEMORIS MUSCLE;

ALGORITHMS; BIOMECHANICAL PHENOMENA; COMPUTER SIMULATION; EXERCISE; HIP JOINT; HUMANS; JOINTS; MONTE CARLO METHOD; MOVEMENT; MUSCLES; QUADRICEPS MUSCLE; SOFTWARE;

EID: 84911987333     PISSN: 10255842     EISSN: 14768259     Source Type: Journal    
DOI: 10.1080/10255842.2013.822489     Document Type: Article

References (26)

1. BertsekasD, TsitsiklisJ. 1996. Neuro-dynamic programming. Belmont, MA: Athena Scientific.
2. BlackR. 2007. Pragmatic software testing: becoming an effective and efficient test professional. Hoboken, NJ: J. Wiley.
3. BuchananTS. 1995. Evidence that maximum muscle stress is not a constant: differences in specific tension in elbow flexors and extensors. Med Eng Phys.17(7):529–536.
4. CavanaghPR, GencKO, GopalakrishnanR, KuklisMM, MaenderCC, RiceAJ. 2010. Foot forces during typical days on the international space station. J Biomech.43(11):2182–2188.
5. ChengH, ObergefellL, RizerA. 1994. Generator of body data (GEBOD), manual. Dayton, OH: Systems Research Labs, Inc.
6. DelpSL, AndersonFC, ArnoldAS, LoanP, HabibA, JohnCT. 2007. OpenSim: open-source software to create and analyze dynamic simulations of movement. IEEE Trans Biomed Eng.54(11):1940–1950.
7. ErdemirA, McLeanS, HerzogW, van den BogertAJ. 2007. Model-based estimation of muscle forces exerted during movements. Clin Biomech (Bristol, Avon). 22(2):131–154.
8. FishmanG. 1996. Monte Carlo: concepts, algorithms, and applications. New York: Springer-Verlag.
9. FittsRH, TrappeSW, CostillDL, GallagherPM, CreerAC, CollotonPA, PetersJR, RomatowskiJG, BainJL, RileyDA. 2010. Prolonged space flight-induced alterations in the structure and function of human skeletal muscle fibres. J Physiol.588(18):3567–3592.
10. GullettJ, TillmanM, GutierrezG, ChowJ. 2009. A biomechanical comparison of back and front squats in healthy trained individuals. J Strength Cond Res.23(1):284–292
11. IsearJA, EricksonJC, WorrellTW. 1997. EMG analysis of lower extremity muscle recruitment patterns during an unloaded squat. Med Sci Sports Exerc.29(4):532–539.
12. KuhnR, LeiY, KackerR. 2008. Practical combinatorial testing: beyond pairwise. IT Prof.10(3):19–23.
13. KuhnD, WallaceD, GalloA, Jr. 2004. Software fault interactions and implications for software testing. IEEE Trans Software Eng.30(6):418–421.
14. LangTF, LeBlancAD, EvansHJ, LuY, GenantH, YuA. 2004. Cortical and trabecular bone mineral loss from the spine and hip in long-duration spaceflight. J Bone Miner Res.19(6):1006–1012.
15. LiacourasPC, WayneJS. 2007. Computational modeling to predict mechanical function of joints: application to the lower leg with simulation of two cadaver studies. J Biomech Eng.129(6):811–817.
16. ManlyBFJ. 2007. Randomization, bootstrap and Monte Carlo methods in biology. London: Chapman & Hall/CRC.
17. McLachlanG, KrishnanT. 2008. The EM algorithm and extensions. Hoboken, NJ: Wiley-Interscience.
18. NelsonES, LewandowskiB, LicataA, MyersJG. 2009. Development and validation of a predictive bone fracture risk model for astronauts. Ann Biomed Eng.37(11):2337–2359.
19. Ozada N, Esat I. 2010. Computational musculoskeletal joint modelling. Paper presented at: International Conference on Orthopaedic Surgery, Biomechanics and Clinical Applications; London, UK.
20. Reed E, Schumann J, Mengshoel O. 2011. Verification and validation of system health management models using parametric testing. Paper presented at the AIAA Infotech@Aerospace; St. Louis, MO.
21. SuttonR, BartoA. 1998. Reinforcement learning: an introduction. Cambridge, MA: MIT.
22. TrappeS, CostillD, GallagherP, CreerA, PetersJR, EvansH, RileyDA, FittsRH. 2009. Exercise in space: human skeletal muscle after 6 months aboard the International Space Station. J Appl Physiol. (Bethesda, MD). 106(4):1159–1168.
23. Valero-CuevasFJ, JohansonME, TowlesJD. 2003. Towards a realistic biomechanical model of the thumb. J Biomech.36(6):1019–1030.
24. WallM, RechtsteinerA, RochaL. 2003. Singular value decomposition and principal component analysis. In: BerrarDP, DubitzkyW, GranzowM, editors. A practical approach to microarray data analysis. 15th ed. Norwell, MA: Kluwer. p. 91.
25. WallaceD, KuhnD. 2001. Failure modes in medical device software: an analysis of 15 years of recall data. Int J Reliab Qual Safety Eng.8(4):351–371.
26. WardS, EngC, SmallwoodL, LieberR. 2009. Are current measurements of lower extremity muscle architecture accurate?Clin Orthop Relat Res.467(4):1074–1082.