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Identifying Time-Varying Neuromuscular Response: a Recursive Least-Squares Algorithm with Pseudoinverse

MPG-Autoren
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Olivari,  M
Department Human Perception, Cognition and Action, 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;

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

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Zitation

Olivari, M., Nieuwenhuizen, F., Bülthoff, H., & Pollini, L. (2015). Identifying Time-Varying Neuromuscular Response: a Recursive Least-Squares Algorithm with Pseudoinverse. In IEEE International Conference on Systems, Man, and Cybernetics (SMC 2015) (pp. 3079-3085). Piscataway, NJ, USA: IEEE.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-002A-442F-4
Zusammenfassung
Effectiveness of hap tic guidance systems depends on how humans adapt their neuromuscular response to the force feedback. A quantitative insight into adaptation of neuromuscular response can be obtained by identifying neuromuscular dynamics. Since humans are likely to vary their neuromuscular response during realistic control scenarios, there is a need for methods that can identify time-varying neuromuscular dynamics. In this work an identification method is developed which estimates the impulse response of time-varying neuromuscular system by using a Recursive Least Squares (RLS) method. The proposed method extends the commonly used RLS-based method by employing the pseudo inverse operator instead of the inverse operator. This results in improved robustness to external noise. The method was validated in a human in-the-loop experiment. The neuromuscular estimates given by the proposed method were more accurate than those obtained with the commonly used RLS-based method.