Abstract
High-fidelity simulations for dynamics and movement control of the human arm is a complex and challenging problem. To realize such high-quality response in real time using today’s computing power is simply not possible. In this paper we have presented a simplified, neural network-based dynamics model of a digital human arm for simulating its reaching movements. The arm is modeled as an assemblage of 2 rigid bodies connected by joints and muscles. A Hill-type model is used to capture the muscle forces. Activation dynamics via a low-pass filtering operation is applied to trigger musculotendon contractions. Since we are interested in real-time macro response, we have replaced the forward dynamics module with a recurrent neural network approach. The results of our simulation show that the model produces fairly realistic reaching motion of the arm and it is able to do so in real-time.
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References
Dornay, M., Uno, Y., Kawato, M., Suzuki, R.: Simulation of optimal movements using the minimum-muscle-tension-change model. Adv. Neural Info. Processing Syst. 4, 627–634 (1991)
Jordan, M.I., Rumelhart, D.E.: Forward models: supervised learning with distal teacher. Cognitive Sci. 16, 307–354 (1992)
Massone, L.L.E., Myers, J.D.: The role of plant properties in arm trajectory formation: a neural network study. IEEE Trans. Syst. Man Cybern. B 26, 719–732 (1996)
Karniel, A., Inbar, G.F.: A model for learning human reaching movements. Biol. Cybern. 77, 173–183 (1997)
Fagg, A.H., Sitkoff, N., Barto, A.G., Houk, J.C.: A model of cerebellar learning for control of arm movements using muscle synergies. In: 1997 IEEE Int. Symp. Computational Intelligence in Robotics and Automation (CIRA 1997), pp. 6–12 (1997)
Ogihara, N., Yamazaki, N.: Generation of human reaching movement using a recurrent neural network model. In: IEEE SMC 1999 Conf. Proc. of Syst. Man Cybern., vol. 2, pp. 692–697 (1999)
Kashima, T., Isurugi, Y., Shima, M.: Analysis of a muscular control system in human movements. Biol. Cybern. 82, 123–131 (2000)
Lan, N.: Stability analysis for postural control in a two-joint limb system. IEEE Trans. On Neural Syst. Rehab. Eng. 10, 249–259 (2001)
Thelen, D.G., Anderson, F.C., Delp, S.L.: Generating dynamic simulations of movement using computed muscle control. Jour. Biomech. 36, 321–328 (2003)
De Sapio, V., Warren, J., Khatib, O., Delp, S.L.: Simulating the task-level control of human motion: a methodology and framework for implementation. Visual Comp. 21, 289–302 (2005)
Atkeson, C.G.: Learning arm kinematics and dynamics. Ann. Revw. Neurosci. 12, 157–183 (1989)
Winters, J.M., Stark, L.: Analysis of fundamental human movement patterns through the use of in-depth antagonistic muscle models. IEEE Trans. Biomed. Eng. 32, 826–839 (1985)
Zangemeister, W.H., Lehman, S., Stark, L.: Simulation of head movement trajectories: model and fit to main sequence. Biol. Cybern. 41, 19–32 (1981)
Schutte, L.M.: Using musculoskeletal models to explore strategies for improving performance in electrical stimulation-induced leg cycle ergometry. PhD. thesis, Stanford University (1992)
Zajac, F.E.: Muscle and tendon: properties, models, scaling, and application to biomechanics and motor control. Critical Reviews Biomed. Eng. 17, 359–411 (1989)
Zhang, A.: Motion simulation of the human upper limb based on MATLAB. Master thesis, Tianjin University, Tianjin, China, in Chinese (2003)
Pineda, F.J.: Dynamics and architecture for neural computation. Jour. Complexity 4, 216–245 (1988)
Katayama, M., Kawato, M.: Virtual trajectory and stiffness ellipse during multijoint arm movement predicted by neural inverse. Biol. Cybern. 69, 353–362 (1993)
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Yu, H., Han, R.P.S. (2006). Real Time Dynamics and Control of a Digital Human Arm for Reaching Motion Simulation. In: Pan, Z., Cheok, A., Haller, M., Lau, R.W.H., Saito, H., Liang, R. (eds) Advances in Artificial Reality and Tele-Existence. ICAT 2006. Lecture Notes in Computer Science, vol 4282. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941354_43
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DOI: https://doi.org/10.1007/11941354_43
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