Abstract
Biodynamic feedthrough (BDFT) refers to a phenomenon where accelerations cause involuntary limb motions which, when coupled to a control device, can result in unintentional control inputs. The goal of this study is to increase the understanding of biodynamic feedthrough, with a focus on the influence of the neuromuscular system. Biodynamic feedthrough has been studied for many years, but its fundamental processes are only poorly understood. Many factors were reported to play a role in the occurrence of biodynamic feedthrough and many of these show mutual interactions. In several studies it was mentioned that the parameters of the neuromuscular system influence the occurrence of BDFT and that these can differ from person to person (inter-subject variability). However, only very little studies have recognized the variability in neuromuscular settings that a single person can express (intra-subject variability) and no known research was devoted to investigating this relation to date. In this study, it was hypothesized that BDFT is strongly influenced by the setting of the neuromuscular system, i.e. the neuromuscular admittance. This hypothesis was tested by performing an experiment in which both the neuromuscular admittance and the biodynamic feedthrough were measured using a motion-based simulator. The neuromuscular admittance was varied using three control tasks, each requiring a different level of admittance. The simultaneous measurement of admittance and biodynamic feedthrough was made possible by offering two disturbance signals that were separated in the frequency domain. The results show a strong dependency of biodynamic feedthrough (BDFT) on neuromuscular admittance. The obtained experimental data was used to develop a biodynamic feedthrough model, capable of describing BDFT for various settings of the neuromuscular system. The BDFT model was constructed by augmenting a neuromuscular model to account for the effect of motion disturbances. An advantage of the proposed modeling approach is that it considerably simplifies the process of parameterization of the BDFT model. In comparison to the measured data, the model proved to describe BDFT dynamics correctly for different subjects and different settings of the neuromuscular system, both in the frequency and in the time domain. However, the parameter values do not readily agree with results that were expected based on theory. Further research is required to investigate the implications of the model approach to the model's physical interpretability.
