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Conference Paper

Biodynamic Feedthrough: Current Status and Open Issues

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Venrooij,  J
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons192609

Olivari,  M
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Project group: Cybernetics Approach to Perception & Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Project group: Motion Perception & Simulation, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons83839

Bülthoff,  HH
Project group: Cybernetics Approach to Perception & Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
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

Venrooij, J., Olivari, M., & Bülthoff, H. (2016). Biodynamic Feedthrough: Current Status and Open Issues. IFAC-PapersOnLine, 49(19), 120-125.


Cite as: https://hdl.handle.net/21.11116/0000-0000-7A7A-F
Abstract
Biodynamic feedthrough (BDFT) occurs when vehicle accelerations feed through the body of a human operator, causing involuntary limb motions, which in turn result in involuntary control inputs. Manual control of many different vehicles is known to be vulnerable to BDFT effects, such as that of helicopters, aircraft, electric wheelchairs and hydraulic excavators. This paper provides a brief review of BDFT literature, which serves as a basis for identifying the fundamental challenges that remain to be addressed in future BDFT research. One of these challenges, time-variant BDFT identification, is discussed in more detail. Currently, it is often assumed that BDFT dynamics are (quasi)linear and time-invariant. This assumption can only be justified when measuring BDFT under carefully crafted experimental conditions, which are very different from real-world situations. As BDFT dynamics depend on neuromuscular dynamics, they are typically time-varying. This paper investigates the suitability of a recently developed time-variant identification approach, based on a recursive least-squares algorithm, which has been successfully used to identify time-varying neuromuscular dynamics.