Calculation of Optimized Movement Trajectories for the Human Forearm

Li Li Yang

doi:10.4028/www.scientific.net/AMM.401-403.1347

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 401-403Calculation of Optimized Movement Trajectories for...

Calculation of Optimized Movement Trajectories for the Human Forearm

Abstract:

Using the minimum variance model, optimal human forearm trajectories formation was investigated using a discrete time linear quadratic regulator. First, the continuous dynamics of the human forearm were established on the basis of the relation between muscle torque and neural control signal, and then we transferred the continuous system dynamics to discrete time notation. Finally we expressed the objective function of minimum variance model using a discrete time linear quadratic regulator and employed Riccati recursion to obtain the optimal movement trajectories of the human forearm. The results of example simulation show that the optimal movement trajectory of the forearm follows a smooth curve, and the speed curve of the hand is single peaked and bell shaped. These are in good agreement with the inherent kinematic properties of optimal movement, and therefore the method is effective for calculating the optimal movement trajectory of the human forearm.

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Periodical:

Applied Mechanics and Materials (Volumes 401-403)

Pages:

1347-1352

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.401-403.1347

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Online since:

September 2013

Authors:

Li Li Yang

Keywords:

Human Forearm Movement, Optimized Movement Trajectory, Signal-Dependent Noise

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References

[1] T. Flash, N. Hogan, The coordination of arm movements: An experimentally confirmed mathematical model, Journal of Neuroscience. 5(1985) 1688-1703.

DOI: 10.1523/jneurosci.05-07-01688.1985

Google Scholar

[2] C. M. Harris, D. M. Wolpert, Signal-dependent noise determines motor-planning, Nature, 394 (1998) 780-784.

DOI: 10.1038/29528

Google Scholar

[3] H. Miyamoto, E. Nakano, D. M. Wolpert, M. Kawato, Tops (Task Optimization in the Presence of Signal-Dependent Noise) Model, Systems and Computers in Japan, 35 (2004) 48-58.

DOI: 10.1002/scj.10377

Google Scholar

[4] G. Simmons, Y. Demiris, Optimal robot arm control using the minimum variance model, Journal of Robotic Systems, 22(2005) 677-690.

DOI: 10.1002/rob.20092

Google Scholar

[5] H. Tanaka, J. W. Krakauer, N. Qian, An optimization principle for determining movement duration", Journal of Neurophysiology, 95(2006)3875-3886.

DOI: 10.1152/jn.00751.2005

Google Scholar

[6] Y. Uno, M. Kawato, R. Suzuki, Formation and control of optimal trajectory in human multi-joint arm movement, Biological Cybernetics, 61(1989) 89~101.

DOI: 10.1007/bf00204593

Google Scholar