Using Animatlab for Neuromechanical Analysis
413
15. Szczecinski, N.S., Martin, J.P., Ritzmann, R.E., Quinn, R.D.: Neuromechanical mantis model
replicates animal postures via biological neural models. In: Duff, A., Lepora, N.F., Mura, A.,
Prescott, T.J., Verschure, P.F.M.J. (eds.) Living Machines 2014. LNCS (LNAI), vol. 8608,
pp. 296–307. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-09435-9_26
16. Szczecinski, N.S., Getsy, A.P., Martin, J.P., et al.: Mantisbot is a robotic model of visually
guided motion in the praying mantis. Arthropod. Struct. Dev. 46, 736–751 (2017). https://doi.
org/10.1016/j.asd.2017.03.001
17. Young, F., Hunt, A.J., Quinn, R.D.: A neuromechanical rat model with a complete set of
hind limb muscles. In: Vouloutsi, V., et al. (eds.) Living Machines 2018. LNCS (LNAI), vol.
10928, pp. 527–537. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95972-6_57
18. Thelen, D.G.: Adjustment of muscle mechanics model parameters to simulate dynamic contractions in older adults. J. Biomech. Eng. 125, 70–77 (2003). https://doi.org/10.1115/1.153
1112
19. Brown, I.E., Scott, S.H., Loeb, G.E.: Mechanics of feline soleus: II design and validation of a
mathematical model. J. Muscle Res. Cell Motil. 17, 221–233 (1996). https://doi.org/10.1007/
BF00124244
20. Lloyd, D.G., Besier, T.F.: An EMG-driven musculoskeletal model to estimate muscle forces
and knee joint moments in vivo. J. Biomech. 36, 765–776 (2003). https://doi.org/10.1016/
S0021-9290(03)00010-1
21. Buchanan, T.S., Lloyd, D.G., Manal, K., Besier, T.F.: Neuromusculoskeletal modeling: estimation of muscle forces and joint moments and movements from measurements of neural
command. J. Appl. Biomech. 20, 367–395 (2004)
22. Rode, C., Siebert, T., Herzog, W., Blickhan, R.: The effects of parallel and series elastic
components on the active cat soleus force-length relationship. J. Mech. Med. Biol. 09, 105–122
(2009). https://doi.org/10.1142/S0219519409002870
23. Hoy, M.G., Zajac, F.E., Gordon, M.E.: A musculoskeletal model of the human lower extremity: the effect of muscle, tendon, and moment arm on the moment-angle relationship of
musculotendon actuators at the hip, knee, and ankle. J. Biomech. 23, 157–169 (1990). https://
doi.org/10.1016/0021-9290(90)90349-8
24. Hill, A.V.: The heat of shortening and the dynamic constants of muscle. Proc. R Soc. Lond.
Ser. B – Biol. Sci. 126, 136–195 (1938). https://doi.org/10.1098/rspb.1938.0050
25. Shadmehr, R., Arbib, M.A.: A mathematical analysis of the force-stiffness characteristics of
muscles in control of a single joint system. Biol. Cybern. 66, 463–477 (1992). https://doi.org/
10.1007/BF00204111
26. Johnson, W.L., Jindrich, D.L., Zhong, H., et al.: Application of a rat hindlimb model: a
prediction of force spaces reachable through stimulation of nerve fascicles. IEEE Trans.
Biomed. Eng. 58, 3328–3338 (2011). https://doi.org/10.1109/TBME.2011.2106784
27. Zajac, F.E.: Muscle and tendon: properties, models, scaling, and application to biomechanics
and motor control. Crit. Rev. Biomed. Eng. 17, 359–411 (1989)
28. Szczecinski, N.S., Hunt, A.J., Quinn, R.D.: A functional subnetwork approach to designing
synthetic nervous systems that control legged robot locomotion. Front. Neurorobot. 11 (2017).
https://doi.org/10.3389/fnbot.2017.00037
29. Torelli, L.: Stability of numerical methods for delay differential equations. J. Comput. Appl.
Math. 25, 15–26 (1989). https://doi.org/10.1016/0377-0427(89)90071-X
30. Winters, J.M., Stark, L.: Muscle models: what is gained and what is lost by varying model
complexity. Biol. Cybern. 55, 403–420 (1987). https://doi.org/10.1007/BF00318375
31. Tang, C.Y., Stojanovic, B., Tsui, C.P., Kojic, M.: Modeling of muscle fatigue using Hill’s
model. Biomed. Mater. Eng. 15, 341–348 (2005)
32. Armstrong, R.B., Phelps, R.O.: Muscle fiber type composition of the rat hindlimb. Am. J.
Anat. 171, 259–272 (1984). https://doi.org/10.1002/aja.1001710303
413
15. Szczecinski, N.S., Martin, J.P., Ritzmann, R.E., Quinn, R.D.: Neuromechanical mantis model
replicates animal postures via biological neural models. In: Duff, A., Lepora, N.F., Mura, A.,
Prescott, T.J., Verschure, P.F.M.J. (eds.) Living Machines 2014. LNCS (LNAI), vol. 8608,
pp. 296–307. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-09435-9_26
16. Szczecinski, N.S., Getsy, A.P., Martin, J.P., et al.: Mantisbot is a robotic model of visually
guided motion in the praying mantis. Arthropod. Struct. Dev. 46, 736–751 (2017). https://doi.
org/10.1016/j.asd.2017.03.001
17. Young, F., Hunt, A.J., Quinn, R.D.: A neuromechanical rat model with a complete set of
hind limb muscles. In: Vouloutsi, V., et al. (eds.) Living Machines 2018. LNCS (LNAI), vol.
10928, pp. 527–537. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95972-6_57
18. Thelen, D.G.: Adjustment of muscle mechanics model parameters to simulate dynamic contractions in older adults. J. Biomech. Eng. 125, 70–77 (2003). https://doi.org/10.1115/1.153
1112
19. Brown, I.E., Scott, S.H., Loeb, G.E.: Mechanics of feline soleus: II design and validation of a
mathematical model. J. Muscle Res. Cell Motil. 17, 221–233 (1996). https://doi.org/10.1007/
BF00124244
20. Lloyd, D.G., Besier, T.F.: An EMG-driven musculoskeletal model to estimate muscle forces
and knee joint moments in vivo. J. Biomech. 36, 765–776 (2003). https://doi.org/10.1016/
S0021-9290(03)00010-1
21. Buchanan, T.S., Lloyd, D.G., Manal, K., Besier, T.F.: Neuromusculoskeletal modeling: estimation of muscle forces and joint moments and movements from measurements of neural
command. J. Appl. Biomech. 20, 367–395 (2004)
22. Rode, C., Siebert, T., Herzog, W., Blickhan, R.: The effects of parallel and series elastic
components on the active cat soleus force-length relationship. J. Mech. Med. Biol. 09, 105–122
(2009). https://doi.org/10.1142/S0219519409002870
23. Hoy, M.G., Zajac, F.E., Gordon, M.E.: A musculoskeletal model of the human lower extremity: the effect of muscle, tendon, and moment arm on the moment-angle relationship of
musculotendon actuators at the hip, knee, and ankle. J. Biomech. 23, 157–169 (1990). https://
doi.org/10.1016/0021-9290(90)90349-8
24. Hill, A.V.: The heat of shortening and the dynamic constants of muscle. Proc. R Soc. Lond.
Ser. B – Biol. Sci. 126, 136–195 (1938). https://doi.org/10.1098/rspb.1938.0050
25. Shadmehr, R., Arbib, M.A.: A mathematical analysis of the force-stiffness characteristics of
muscles in control of a single joint system. Biol. Cybern. 66, 463–477 (1992). https://doi.org/
10.1007/BF00204111
26. Johnson, W.L., Jindrich, D.L., Zhong, H., et al.: Application of a rat hindlimb model: a
prediction of force spaces reachable through stimulation of nerve fascicles. IEEE Trans.
Biomed. Eng. 58, 3328–3338 (2011). https://doi.org/10.1109/TBME.2011.2106784
27. Zajac, F.E.: Muscle and tendon: properties, models, scaling, and application to biomechanics
and motor control. Crit. Rev. Biomed. Eng. 17, 359–411 (1989)
28. Szczecinski, N.S., Hunt, A.J., Quinn, R.D.: A functional subnetwork approach to designing
synthetic nervous systems that control legged robot locomotion. Front. Neurorobot. 11 (2017).
https://doi.org/10.3389/fnbot.2017.00037
29. Torelli, L.: Stability of numerical methods for delay differential equations. J. Comput. Appl.
Math. 25, 15–26 (1989). https://doi.org/10.1016/0377-0427(89)90071-X
30. Winters, J.M., Stark, L.: Muscle models: what is gained and what is lost by varying model
complexity. Biol. Cybern. 55, 403–420 (1987). https://doi.org/10.1007/BF00318375
31. Tang, C.Y., Stojanovic, B., Tsui, C.P., Kojic, M.: Modeling of muscle fatigue using Hill’s
model. Biomed. Mater. Eng. 15, 341–348 (2005)
32. Armstrong, R.B., Phelps, R.O.: Muscle fiber type composition of the rat hindlimb. Am. J.
Anat. 171, 259–272 (1984). https://doi.org/10.1002/aja.1001710303