Abstract
This paper introduces a new closed-form formulation for the moment arm matrix corresponding to the musculotendon unit in a general computational model of the musculoskeletal system. The novel approach uses matrix calculus to define a “Generalized Musculotendon Line of Action Vector (GMLAV)” in integration with the virtual work principle, hence leveraging both conventional geometry and tendon excursion methods. In contrast to previous methods, the concepts of motion rhythm and joint constraints have been incorporated without restrictions, where each joint variable is defined as an arbitrary smooth function of the generalized coordinate vector. The validity of our formulation was established by performing the simulations on two well-known musculoskeletal models from literature and comparing the outcomes with those obtained using OpenSim. The results presented in this paper show a high degree of fidelity between our novel simulation model and OpenSim (or SIMM).
Similar content being viewed by others
References
Song, D., Lan, N., Loeb, G.E., Gordon, J.: Model-based sensorimotor integration for multi-joint control: development of a virtual arm model. Ann. Biomed. Eng. 36(6), 1033–1048 (2008). doi:10.1007/s10439-008-9461-8
de Rugy, A., Riek, S., Oytam, Y., Carroll, T.J., Davoodi, R., Carson, R.G.: Neuromuscular and biomechanical factors codetermine the solution to motor redundancy in rhythmic multijoint arm movement. Exp. Brain Res. 189(4), 421–434 (2008). doi:10.1007/s00221-008-1437-2
Tsianos, G.A., Goodner, J., Loeb, G.E.: Useful properties of spinal circuits for learning and performing planar reaches. J. Neural Eng. 11(5), 056006 (2014). doi:10.1088/1741-2560/11/5/056006
Shao, Q., Bassett, D.N., Manal, K., Buchanan, T.S.: An EMG-driven model to estimate muscle forces and joint moments in stroke patients. Comput. Biol. Med. 39(12), 1083–1088 (2009). doi:10.1016/j.compbiomed.2009.09.002
Yamaguchi, G.T., Zajac, F.E.: Restoring unassisted natural gait to paraplegics via functional neuromuscular stimulation: a computer simulation study. IEEE Trans. Biomed. Eng. 37(9), 886–902 (1990). doi:10.1109/10.58599
Wang, C.Y., Bobrow, J.E., Reinkensmeyer, D.J.: Dynamic motion planning for the design of robotic gait rehabilitation. J. Biomech. Eng. 127(4), 672–679 (2005)
Kia, M., Stylianou, A.P., Guess, T.M.: Evaluation of a musculoskeletal model with prosthetic knee through six experimental gait trials. Med. Eng. Phys. 36(3), 335–344 (2014). doi:10.1016/j.medengphy.2013.12.007
Manal, K., Gravare-Silbernagel, K., Buchanan, T.S.: A Real-time EMG-driven Musculoskeletal Model of the Ankle. Multibody Syst. Dyn. 28(1–2), 169–180 (2012). doi:10.1007/s11044-011-9285-4
Lo, J., Huang, G., Metaxas, D.: Human motion planning based on recursive dynamics and optimal control techniques. Multibody Syst. Dyn. 8(4), 433–458 (2002)
Biral, F., Bertolazzi, E., Da Lio, M.: Real-time motion planning for multibody systems—Real life application examples. Multibody Syst. Dyn. 17(2–3), 119–139 (2007). doi:10.1007/s11044-007-9037-7
Kim, J.H., Xiang, Y.J., Yang, J.Z., Arora, J.S., Abdel-Malek, K.: Dynamic motion planning of overarm throw for a biped human multibody system. Multibody Syst. Dyn. 24(1), 1–24 (2010). doi:10.1007/s11044-010-9193-z
Kim, J.H., Yang, J.Z., Abdel-Malek, K.: A novel formulation for determining joint constraint loads during optimal dynamic motion of redundant manipulators in DH representation. Multibody Syst. Dyn. 19(4), 427–451 (2008). doi:10.1007/s11044-007-9100-4
Winters, J.M.: An improved muscle-reflex actuator for use in large-scale neuro-musculoskeletal models. Ann. Biomed. Eng. 23(4), 359–374 (1995)
Winters, J.M., Stark, L.: Muscle models: what is gained and what is lost by varying model complexity. Biol. Cybern. 55(6), 403–420 (1987)
Blajer, W., Czaplicki, A., Dziewiecki, K., Mazur, Z.: Influence of selected modeling and computational issues on muscle force estimates. Multibody Syst. Dyn. 24(4), 473–492 (2010). doi:10.1007/s11044-010-9216-9
Ackland, D.C., Lin, Y.C., Pandy, M.G.: Sensitivity of model predictions of muscle function to changes in moment arms and muscle-tendon properties: a Monte-Carlo analysis. J. Biomech. 45(8), 1463–1471 (2012). doi:10.1016/j.jbiomech.2012.02.023
Nagano, A., Komura, T.: Longer moment arm results in smaller joint moment development, power and work outputs in fast motions. J. Biomech. 36(11), 1675–1681 (2003). doi:10.1016/S0021-9290(03)00171-4
Schache, A.G., Ackland, D.C., Fok, L., Koulouris, G., Pandy, M.G.: Three-dimensional geometry of the human biceps femoris long head measured in vivo using magnetic resonance imaging. Clin. Biomech. (Bristol, Avon) 28(3), 278–284 (2013). doi:10.1016/j.clinbiomech.2012.12.010
Dostal, W.F., Andrews, J.G.: A three-dimensional biomechanical model of hip musculature. J. Biomech. 14(11), 803–812 (1981)
Maganaris, C.N.: Imaging-based estimates of moment arm length in intact human muscle-tendons. Eur. J. Appl. Physiol. 91(2–3), 130–139 (2004). doi:10.1007/s00421-003-1033-x
Manal, K., Cowder, J.D., Buchanan, T.S.: A hybrid method for computing achilles tendon moment arm using ultrasound and motion analysis. J. Appl. Biomech. 26(2), 224–228 (2010)
Arnold, A.S., Salinas, S., Asakawa, D.J., Delp, S.L.: Accuracy of muscle moment arms estimated from MRI-based musculoskeletal models of the lower extremity. Comput. Aided Surg. 5(2), 108–119 (2000). doi:10.1002/1097-0150(2000)5:2<108::AID-IGS5>3.0.CO;2-2
Ackland, D.C., Pandy, M.G.: Moment arms of the shoulder muscles during axial rotation. J. Orthop. Res. 29(5), 658–667 (2011). doi:10.1002/jor.21269
Ackland, D.C., Pak, P., Richardson, M., Pandy, M.G.: Moment arms of the muscles crossing the anatomical shoulder. J. Anat. 213(4), 383–390 (2008). doi:10.1111/j.1469-7580.2008.00965.x
An, K.N., Ueba, Y., Chao, E.Y., Cooney, W.P., Linscheid, R.L.: Tendon excursion and moment arm of index finger muscles. J. Biomech. 16(6), 419–425 (1983). doi:10.1016/0021-9290(83)90074-X
Pandy, M.G.: Moment arm of a muscle force. Exerc. Sport Sci. Rev. 27, 79–118 (1999)
Holzbaur, K.R., Murray, W.M., Delp, S.L.: A model of the upper extremity for simulating musculoskeletal surgery and analyzing neuromuscular control. Ann. Biomed. Eng. 33(6), 829–840 (2005)
Blemker, S.S., Delp, S.L.: Three-dimensional representation of complex muscle architectures and geometries. Ann. Biomed. Eng. 33(5), 661–673 (2005)
Garner, B.A., Pandy, M.G.: Musculoskeletal model of the upper limb based on the visible human male dataset. Comput. Methods Biomech. Biomed. Eng. 4(2), 93–126 (2001). doi:10.1080/10255840008908000
Garner, B.A., Pandy, M.G.: The Obstacle-Set Method for Representing Muscle Paths in Musculoskeletal Models. Comput. Methods Biomech. Biomed. Eng. 3(1), 1–30 (2000). doi:10.1080/10255840008915251
Delp, S.L., Loan, J.P., Hoy, M.G., Zajac, F.E., Topp, E.L., Rosen, J.M.: An interactive graphics-based model of the lower extremity to study orthopaedic surgical procedures. IEEE Trans. Biomed. Eng. 37(8), 757–767 (1990). doi:10.1109/10.102791
Carvalho, A., Suleman, A.: Multibody simulation of the musculoskeletal system of the human hand. Multibody Syst. Dyn. 29(3), 271–288 (2013)
Hainisch, R., Gfoehler, M., Zubayer-Ul-Karim, M., Pandy, M.G.: Method for determining musculotendon parameters in subject-specific musculoskeletal models of children developed from MRI data. Multibody Syst. Dyn. 28(1–2), 143–156 (2012)
Quental, C., Folgado, J., Ambrósio, J., Monteiro, J.: A multibody biomechanical model of the upper limb including the shoulder girdle. Multibody Syst. Dyn. 28(1–2), 83–108 (2012)
de Groot, J.H.: The variability of shoulder motions recorded by means of palpation. Clin. Biomech. (Bristol, Avon) 12(7–8), 461–472 (1997). doi:10.1016/S0268-0033(97)00031-4
de Groot, J.H., Brand, R.: A three-dimensional regression model of the shoulder rhythm. Clin. Biomech. (Bristol, Avon) 16(9), 735–743 (2001). doi:10.1016/S0268-0033(01)00065-1
Moissenet, F., Chèze, L., Dumas, R.: Anatomical kinematic constraints: consequences on musculo-tendon forces and joint reactions. Multibody Syst. Dyn. 28(1–2), 125–141 (2012)
Grewal, T.J., Dickerson, C.R.: A novel three-dimensional shoulder rhythm definition that includes overhead and axially rotated humeral postures. J. Biomech. 46(3), 608–611 (2013). doi:10.1016/j.jbiomech.2012.09.028
Xu, X., Lin, J.H., McGorry, R.W.: A regression-based 3-D shoulder rhythm. J. Biomech. 47(5), 1206–1210 (2014). doi:10.1016/j.jbiomech.2014.01.043
Delp, S.L., Anderson, F.C., Arnold, A.S., Loan, P., Habib, A., John, C.T., Guendelman, E., Thelen, D.G.: OpenSim: open-source software to create and analyze dynamic simulations of movement. IEEE Trans. Biomed. Eng. 54(11), 1940–1950 (2007). doi:10.1109/TBME.2007.901024
De Sapio, V., Holzbaur, K., Khatib, O.: The control of kinematically constrained shoulder complexes: physiological and humanoid examples. In: Robotics and Automation, 15–19 May 2006. ICRA 2006. Proceedings 2006 IEEE International Conference on, pp. 2952–2959 (2006)
Featherstone, R.: Rigid Body Dynamics Algorithms. Springer, New York (2008)
Brewer, J.: Kronecker products and matrix calculus in system theory. IEEE Trans. Circuits Syst. 25(9), 772–781 (1978). doi:10.1109/tcs.1978.1084534
Vetter, W.J.: Matrix calculus operations and Taylor expansions. SIAM Rev. 15(2), 352–369 (1973)
Shabana, A.A.: Dynamics of Multibody Systems, 3rd edn. Cambridge University Press, Cambridge (2005)
Delp, S.L.: Surgery simulation: A computer graphics system to analyze and design musculoskeletal reconstructions of the lower limb. Ph.D. dissertation, Stanford University (1990)
Holzbaur, K.R.S.: Upper limb biomechanics: Musculoskeletal modeling, surgical simulation, and scaling of muscle size and strength. Ph.D. dissertation, Stanford University (2005)
Acknowledgements
We gratefully acknowledge Dr. Kinda Khalaf for her help in reviewing the manuscript. Thanks are also due to Dr. Bahman Nasseroleslami, Professor Richard Hughes and the anonymous referees for their constructive comments on the manuscript.
Author information
Authors and Affiliations
Corresponding author
Appendices
Appendix A
In this study, by employing the derivative of a matrix with respect to vectors, we managed to derive a closed-form formula for the moment arm matrix of a general musculoskeletal system in the presence of joint constraints and motion rhythm. However, as emphasized in the previous sections, the definition of matrix derivative used is different from that used by other studies such as Brewer [43] and Vetter [44]. In this appendix, we first introduce our definition for the derivative of a matrix with respect to a vector; and then, we have expatiated on this definition by stating and proving its properties.
Although the matrix calculus we use is different from the aforementioned studies, we have borrowed the definition of an elementary matrix from Brewer’s work [43]: \(\mathcal{E}_{ik}^{m \times n}\) is a matrix which belongs to \(\mathbb{R}^{m \times n}\) and is zero everywhere except at its \(i\)–\(k\) element that is “1”. Using this matrix an arbitrary matrix like \(\mathbf{A} \in \mathbb{R}^{m \times n}\), can be written as
where \(A_{ik}\) is an element of \(\mathbf{A}\) located at its \(i\)th row and \(k\)th column.
We define the derivative of matrix \(\mathbf{A}\) with respect to an arbitrary vector \(\mathbf{q} \in \mathbb{R}^{r}\) as follows:
The following includes the various properties of the defined matrix calculus and associated proofs:
Property 1
Proof
According to the definition of matrix multiplication, one can deduce
hence,
Since (see Table 1 of Brewer [43])
and
Eq. (A.3) transforms as follows:
Applying the mixed-product property of the Kronecker multiplication on Eq. (A.4) yields
Since \(\mathbf{I}_{m} = \sum_{i = 1}^{m} \mathcal{E}_{ii}^{m \times m}\) and \(\mathbf{I}_{t} = \sum_{k = 1}^{t} \mathcal{E}_{kk}^{t \times t}\), the first property has been proven. □
Property 2
Proof
According to (A.2), we have
therefore,
In order to obtain this relation, we have used the mixed-product property of the Kronecker multiplication. □
Property 3
Proof
(a) Using Eq. (A.1), we have
moreover,
Substituting this relation into Eq. (A.5) results in
Since \(d\mathbf{q}^{T}\frac{\partial A_{ik}}{\partial \mathbf{q}}\) is a scalar, one may rewrite the aforementioned equation into what follows:
Utilizing the mixed-product property, this relation is rearranged into the following structure:
therefore,
(b) The main process of proving this claim is similar to that of part (a). However, for computing \(dA_{ik}\) we write
therefore,
Employing the same plan as we did in part (a), we obtain the following result:
Moreover, since \(( \mathcal{E}_{ik}^{m \times n} )^{T} = \mathcal{E}_{ki}^{n \times m}\), one may reformulate this equation into the following one:
hence,
Using this property, one can easily extract similar formulae for \(\frac{d\mathbf{A}}{dt}\) and \(\delta \mathbf{A}\). □
Appendix B
The purpose of this section is to substantiate the following equality:
where \(\mathbf{D}_{i} \in \mathbb{R}^{4n_{q} \times 4}\) which in Sect. 2.3.1 has been defined as
Starting from the left-hand side of Eq. (B.1), we have
Since \(\mathbf{I}_{4n_{q}} = \mathbf{I}_{n_{q}} \otimes \mathbf{I}_{4}\), one may rewrite the right-hand side of this equation into the following form:
Expanding the right-hand side of this equation results in
Now we prove that the first part on the right-hand side of this relation vanishes. To this end, we use the mixed-product property of the Kronecker multiplication:
hence,
Since
we can rewrite this equation into the following one:
Employing the distributive property of the Kronecker multiplication, we have
where \(\mathbf{C} = \bar{\mathbf{0}}^{ T} \otimes \mathbf{I}_{n_{q}}\).
Since \(\mathbf{D}_{i} = 1 \otimes \mathbf{D}_{i}\),
Taking the mixed-product property of the Kronecker multiplication into consideration results in
Since \(\bar{\mathbf{0}}^{ T}\bar{\mathbf{0}} = 1\), we come into the following conclusion:
Rights and permissions
About this article
Cite this article
Ehsani, H., Rostami, M. & Parnianpour, M. A closed-form formula for the moment arm matrix of a general musculoskeletal model with considering joint constraint and motion rhythm. Multibody Syst Dyn 36, 377–403 (2016). https://doi.org/10.1007/s11044-015-9469-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11044-015-9469-4