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Identifying Time-Varying Neuromuscular Response: Experimental Evaluation of a RLS-based Algorithm

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Olivari,  M
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Nieuwenhuizen,  FM
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Bülthoff,  HH
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Olivari, M., Nieuwenhuizen, F., Bülthoff, H., & Pollini, L. (2015). Identifying Time-Varying Neuromuscular Response: Experimental Evaluation of a RLS-based Algorithm. In AIAA Modeling and Simulation Technologies Conference 2015: held at the SciTech Forum 2015 (pp. 284-298). Red Hook, NY, USA: Curran.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002A-479F-0
Abstract
Methods for identifying neuromuscular response commonly assume time-invariant neuromuscular dynamics. However, neuromuscular dynamics are likely to change during realistic control scenarios. In a previous paper we presented a method for identifying time-varying neuromuscular dynamics based on a Recursive Least Squares (RLS) algorithm. To date, this method has only been validated in a Monte Carlo simulation study. This paper presents an experimental validation of the same method. In the experiment, three different disturbance-rejection tasks were performed: a position task with the human instructed to minimize the stick deflection in front of an external force disturbance, a relax task with the instruction to relax the arm, and a time-varying task with the instruction to alternate between position and relax tasks. The position and relax tasks induce different time-invariant neuromuscular dynamics, whereas the time-varying task induces time-varying neuromuscular dynamics. The RLS-based method was used to estimate neuromuscular dynamics in the three tasks. The neuromuscular estimates were reliable both in time-invariant and time-varying tasks. These findings indicate that the RLS-based method can be used to estimate time-varying neuromuscular responses in human-in-the loop experiments.