Abstract
Musculoskeletal modeling is an important step in understanding the behavior of a body part for postural and motion control. A simple but reliable model is preferred over more complex models. Most of the musculoskeletal models that have been developed so far involved a number of parameters that sometimes, some of the parameters are not easily identified or require difficult or expensive procedures. In this paper, a simplification strategy of the musculoskeletal model in 1 degree of freedom (DOF) hand tremulous motion is presented. The key idea of the complexity reduction mainly on the combination of the two inputs into single input using a sequence of signal processing. 3 models (first order, second order and second order with one zero) plus time delay are considered to represent the simplified musculoskeletal model. The best fit was represented by a second order plus time delay and one zero.
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References
Hof, A.L.: EMG and muscle force: An introduction. Human Movement Science 3, 119–153 (1984)
Langenderfer, J., LaScalza, S., Mell, A., Carpenter, J.E., Kuhn, J.E., Hughes, R.E.: An EMG-driven model of the upper extremity and estimation of long head biceps force. Computers in Biology and Medicine 35, 25–39 (2005)
Hof, A.L., Van den Berg, J.: EMG to force processing I: An electrical analogue of the hill muscle model. Journal of Biomechanics 14, 747–753, 755-758 (1981)
Bestel, J., Clément, F., Sorine, M.: A Biomechanical Model of Muscle Contraction. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 1159–1161. Springer, Heidelberg (2001)
Dijkstra, S., Denier van der Gon, J.J., Blangé, T., Karemaker, J.M., Kramer, A.E.J.L.: A simplified sliding-filament muscle model for simulation purposes. Biological Cybernetics 12, 94–101 (1973)
Hayashibe, M., Guiraud, D., Poignet, P.: EMG-based neuromuscular modeling with full physiological dynamics and its comparison with modified hill model. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2009, pp. 6530–6533 (2009)
Bean, J.C., Chaffin, D.B., Schultz, A.B.: Biomechanical model calculation of muscle contraction forces: A double linear programming method. Journal of Biomechanics 21, 59–66 (1988)
Wexler, A.S., Jun, D., Binder-Macleod, S.A.: A mathematical model that predicts skeletal muscle force. IEEE Transactions on Biomedical Engineering 44, 337–348 (1997)
Terry, K.K.K., Arthur, F.T.M.: Feasibility of using EMG driven neuromusculoskeletal model for prediction of dynamic movement of the elbow. Journal of electromyography and kinesiology: official journal of the International Society of Electrophysiological Kinesiology 15, 12–26 (2005)
David, G.L., Thor, F.B.: An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo. Journal of Biomechanics 36, 765–776 (2003)
Burkhard, P.R., Langston, J.W., Tetrud, J.W.: Voluntarily simulated tremor in normal subjects. Neurophysiologie Clinique/Clinical Neurophysiology 32, 119–126 (2002)
Conforto, S., Mathieu, P., Schmid, M., Bibbo, D., Florestal, J.R., D’Alessio, T.: How much can we trust the electromechanical delay estimated by using electromyography? In: 28th Annual International Conference of the IEEE in Engineering in Medicine and Biology Society, EMBS 2006, pp. 1256–1259 (2006)
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Ruwadi, Yap, S.C., Poignet, P., Tech, A.W. (2010). Model Complexity Reduction of the Hand Musculoskeletal System in Tremulous Motion. In: Liu, H., Ding, H., Xiong, Z., Zhu, X. (eds) Intelligent Robotics and Applications. ICIRA 2010. Lecture Notes in Computer Science(), vol 6425. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16587-0_40
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DOI: https://doi.org/10.1007/978-3-642-16587-0_40
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