1,218
Views
0
CrossRef citations to date
0
Altmetric
Articles

The impact of anatomical uncertainties on the predictions of a musculoskeletal hand model – a sensitivity study

ORCID Icon, &
Pages 156-164 | Received 29 Jan 2021, Accepted 07 Jun 2021, Published online: 28 Jun 2021

References

  • Bailly F, Ceglia A, Michaud B, Rouleau DM, Begon M. 2021. Real-time and dynamically consistent estimation of muscle forces using a moving horizon EMG-marker tracking algorithm-application to upper limb biomechanics. Front Bioeng Biotechnol. 9:642742.
  • Barbero M, Merletti R, Rainoldi A, Jull GA. 2012. Atlas of muscle innervation zones. Understanding surface electromyography and its applications. Milan: Springer.
  • Bergmann G, editor. 2008. OrthoLoad. Charité Universitaetsmedizin Berlin. February 1. https://orthoload.com.
  • Cook D, Julias M, Nauman E. 2014. Biological variability in biomechanical engineering research: significance and meta-analysis of current modeling practices. J Biomech. 47(6):1241–1250.
  • Criswell E, Cram JR. 2011. Cram's introduction to surface electromyography. 2nd ed. Sudbury (MA): Jones and Bartlett.
  • Engelhardt L, Melzner M, Havelkova L, Fiala P, Christen P, Dendorfer S, Simon U. 2020. A new musculoskeletal AnyBody™ detailed hand model. Comput Methods Biomech Biomed Eng. :1–11.
  • Gagnon D, Arjmand N, Plamondon A, Shirazi-Adl A, Larivière C. 2011. An improved multi-joint EMG-assisted optimization approach to estimate joint and muscle forces in a musculoskeletal model of the lumbar spine. J Biomech. 44(8):1521–1529.
  • Gustafsson E, Johnson PW, Hagberg M. 2010. Thumb postures and physical loads during mobile phone use - a comparison of young adults with and without musculoskeletal symptoms. J Electromyogr Kinesiol. 20(1):127–135.
  • Havelková L, Zítka T, Fiala P, Rybarova M, Tupý R, Kalis V, Ismail KM. 2020. Data for: hand muscles attachments: a geometrical model. https://zenodo.org/record/3953592#.Xxb4YufgphE.
  • Helton JC, Davis FJ. 2003. Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems. Reliab Eng Syst Saf. 81(1):23–69.
  • Herzog W. 1992. Sensitivity of muscle force estimations to changes in muscle input parameters using nonlinear optimization approaches. J Biomech Eng. 114(2):267–268.
  • Kendall FP. 2010. Muscles. Testing and function with posture and pain. 5th ed., International ed. Baltimore (MD): Lippincott Williams & Wilkins.
  • Killick R, Fearnhead P, Eckley IA. 2012. Optimal detection of changepoints with a linear computational cost. J Am Stat Assoc. 107(500):1590–1598.
  • Konrad P. 2005. The ABC of EM: A practical introduction to kinesiological electromyography, Noraxon Scottsdale. Version 1.0. Scottsdale, AZ: Noraxon.
  • Lund M, Rasmussen J, Andersen M. 2019. AnyPyTools: a Python package for reproducible research with the AnyBody modeling system. JOSS. 4(33):1108.
  • Myers CA, Laz PJ, Shelburne KB, Davidson BS. 2015. A probabilistic approach to quantify the impact of uncertainty propagation in musculoskeletal simulations. Ann Biomed Eng. 43(5):1098–1111.
  • Nussbaum MA, Chaffin DB, Rechtien CJ. 1995. Muscle lines-of-action affect predicted forces in optimization-based spine muscle modeling. J Biomech. 28(4):401–409.
  • Pieri Ed, Lund ME, Gopalakrishnan A, Rasmussen KP, Lunn DE, Ferguson SJ. 2018. Refining muscle geometry and wrapping in the TLEM 2 model for improved hip contact force prediction. PLoS One. 13(9):e0204109.
  • Raikova RT, Prilutsky BI. 2001. Sensitivity of predicted muscle forces to parameters of the optimization-based human leg model revealed by analytical and numerical analyses. J Biomech. 34(10):1243–1255.
  • Rebba R, Mahadevan S. 2008. Computational methods for model reliability assessment. Reliab Eng Syst Saf. 93(8):1197–1207.
  • Reinbolt JA, Schutte JF, Fregly BJ, Koh BI, Haftka RT, George AD, Mitchell KH. 2005. Determination of patient-specific multi-joint kinematic models through two-level optimization. J Biomech. 38(3):621–626.
  • Scheys L, Loeckx D, Spaepen A, Suetens P, Jonkers I. 2009. Atlas-based non-rigid image registration to automatically define line-of-action muscle models: a validation study. J Biomech. 42(5):565–572.
  • Scheys L, Van Campenhout A, Spaepen A, Suetens P, Jonkers I. 2008. Personalized MR-based musculoskeletal models compared to rescaled generic models in the presence of increased femoral anteversion: effect on hip moment arm lengths. Gait Posture. 28(3):358–365.
  • Seth A, Pandy MG. 2007. A neuromusculoskeletal tracking method for estimating individual muscle forces in human movement. J Biomech. 40(2):356–366.
  • Smith TJ, Henning RA, Wade MG, Fisher T. 2014. Variability in human performance. Hoboken: Taylor and Francis (Human Factors and Ergonomics).
  • Taddei F, Martelli S, Valente G, Leardini A, Benedetti MG, Manfrini M, Viceconti M. 2012. Femoral loads during gait in a patient with massive skeletal reconstruction. Clin Biomech (Bristol, Avon). 27(3):273–280.
  • Valente G, Pitto L, Testi D, Seth A, Delp SL, Stagni R, Viceconti M, Taddei F. 2014. Are subject-specific musculoskeletal models robust to the uncertainties in parameter identification? PLoS One. 9(11):e112625.
  • Valero-Cuevas FJ, Johanson M, Elise, Towles JD. 2003. Towards a realistic biomechanical model of the thumb: the choice of kinematic description may be more critical than the solution method or the variability/uncertainty of musculoskeletal parameters. J Biomech. 36(7):1019–1030.
  • Żuk M, Pezowicz C. 2016. The influence of uncertainty in body segment mass on calculated joint moments and muscle forces. In Piętka E, Badura P, Kawa J, Wieclawek W, editors. Information Technologies in Medicine. 5th International Conference, ITIB 2016; Kamień Śląski, Poland; June 20–22; 2016 Proceedings, Volume 2. Cham, 2016. Cham: Springer International Publishing (Advances in Intelligent Systems and Computing, 472), p. 349–359.