Biodynamic feedthrough (BDFT) refers to a phenomenon where accelerations cause involuntary limb motions, which can result in unintentional control inputs that can substantially degrade manual control. It is known that humans can adapt the dynamics of their limbs by adjusting their neuromuscular settings, and it is likely that these adaptations have a large influence on BDFT. The goal of this paper is to present a method that can provide evidence for this hypothesis. Limb dynamics can be described by admittance, which is the causal dynamic relation between a force input and a position output. This paper presents a method to simultaneously measure BDFT and admittance in a motion-based simulator. The method was validated in an experiment. Admittance was measured by applying a force disturbance signal to the control device; BDFT was measured by applying a motion disturbance signal to the motion simulator. To allow distinguishing between the operator's responses to each disturbance signal, the perturbation signals were separated in the frequency domain. To show the impact of neuromuscular adaptation, subjects were asked to perform three different control tasks, each requiring a different setting of the neuromuscular system (NMS). Results show a dependence of BDFT on neuromuscular admittance: A change in neuromuscular admittance results in a change in BDFT dynamics. This dependence is highly relevant when studying BDFT. The data obtained with the proposed measuring method provide insight in how exactly the settings of the NMS influence the level of BDFT. This information can be used to gain fundamental knowledge on BDFT and also, for example, in the development of a canceling controller.

Biodynamic feedthrough (BDFT) occurs when vehicle vibrations and accelerations feed through the pilot's body and cause involuntary motion of limbs, resulting in involuntary control inputs. BDFT can severely reduce ride comfort, control accuracy and, above all, safety during the operation of rotorcraft. Furthermore, BDFT can cause and sustain Rotorcraft-Pilot Couplings (RPCs). Despite many studies conducted in past decades -both within and outside of the rotorcraft community -BDFT is still a poorly understood phenomenon. The complexities involved in BDFT have kept researchers and manufacturers in the rotorcraft domain from developing robust ways of dealing with its effects. A practical BDFT pilot model, describing the amount of involuntary control inputs as a function of accelerations, could pave the way to account for adversive BDFT effects. In the current paper, such a model is proposed. Its structure is based on the model proposed by Mayo [1], its accuracy and usability are improved by incorporating insights from recently obtained experimental data. An evaluation of the model performance shows that the model describes the measured data well and that it provides a considerable improvement to the original Mayo model. Furthermore, the results indicate that the neuromuscular dynamics have an important influence on the BDFT model parameters.

When performing a manual control task, vehicle accelerations can cause involuntary limb motions, which can result in unintentional control inputs. This phenomenon is called biodynamic feedthrough (BDFT). In the past decades, many studies into BDFT have been performed, but its fundamentals are still only poorly understood. What has become clear, though, is that BDFT is a highly complex process, and its occurrence is influenced by many different factors. A particularly challenging topic in BDFT research is the role of the human operator, which is not only a very complex but also a highly adaptive system. In literature, two different ways of measuring and analyzing BDFT are reported. One considers the transfer of accelerations to involuntary forces applied to the control device (CD); the other considers the transfer of accelerations to involuntary CD deflections or positions. The goal of this paper is to describe an approach to unify these two methods. It will be shown how the results of the two methods relate and how this knowledge may aid in understanding BDFT better as a whole. The approach presented is based on the notion that BDFT dynamics can be described by the combination of two transfer dynamics: 1) the transfer dynamics from body accelerations to involuntary forces and 2) the transfer dynamics from forces to CD deflections. The approach was validated using experimental results.

Vehicle accelerations may lead to involuntary limb motions. These motions can result into involuntary control inputs when performing a manual control task. This phenomenon is called biodynamic feedthrough (BDFT). This paper aims to show that task interpretation plays an important role in the occurrence of BDFT. Results of an experiment are presented, in which biodynamic feedthrough was measured during three different control tasks. Each control task required the human operator to adapt his/her neuromuscular settings. The results show that the level of biodynamic feedthrough depends on the task the human operator is performing. From further analysis, it can be observed that the experiment results are in good agreement with BDFT measurements found in literature. The comparison confirms that the task interpretation plays an important role in BDFT which cannot be ignored when attempting to understand or mitigate BDFT in practical situations

A biodynamic feedthrough (BDFT) model is proposed that describes how vehicle accelerations feed through the human body, causing involuntary limb motions and so involuntary control inputs. BDFT dynamics strongly depend on limb dynamics, which can vary between persons (between-subject variability), but also within one person over time, e.g., due to the control task performed (within-subject variability). The proposed BDFT model is based on physical neuromuscular principles and is derived from an established admittance model-describing limb dynamics-which was extended to include control device dynamics and account for acceleration effects. The resulting BDFT model serves primarily the purpose of increasing the understanding of the relationship between neuromuscular admittance and biodynamic feedthrough. An added advantage of the proposed model is that its parameters can be estimated using a two-stage approach, making the parameter estimation more robust, as the procedure is largely based on the well documented procedure required for the admittance model. To estimate the parameter values of the BDFT model, data are used from an experiment in which both neuromuscular admittance and biodynamic feedthrough are measured. The quality of the BDFT model is evaluated in the frequency and time domain. Results provide strong evidence that the BDFT model and the proposed method of parameter estimation put forward in this paper allows for accurate BDFT modeling across different subjects (accounting for between-subject variability) and across control tasks (accounting for within-subject variability).

The goal of this paper is to better understand how the neuromuscular system of a pilot, or more generally an operator, adapts itself to different types of haptic aids during a pitch control task. A multi-loop pilot model, capable of describing the human behaviour during a tracking task, is presented. Three different identification techniques were investigated in order to simultaneously identify neuromuscular admittance and the visual response of a human pilot. In one of them, the various frequency response functions that build up the pilot model are identified using multi-inputs linear time-invariant models in ARX form. A second method makes use of cross-spectral densities and diagram block algebra to obtain the desired frequency response estimates. The identification techniques were validated using Monte Carlo simulations of a closed-loop control task. Both techniques were compared with the results of another identification method well known in literature and based on crossspectral density estimates. All those methods were applied in an experimental setup in which pilots performed a pitch control task with different haptic aids. Two different haptic aids for tracking task are presented, a Direct Haptic Aid and an Indirect Haptic Aid. The two haptic aids were compared with a baseline condition in which no haptic force was used. The data obtained with the proposed method provide insight in how the pilot adapts his control behavior in relation to different haptic feedback schemes. From the experimental results it can be concluded that humans adapt their neuromuscular admittance in relation with different haptic aids. Furthermore, the two new identification techniques seemed to give more reliable admittance estimates.

In this paper, identification methods are proposed to estimate the neuromuscular and visual responses of a multiloop pilot model. A conventional and widely used technique for simultaneous identification of the neuromuscular and visual systems makes use of cross-spectral density estimates. This paper shows that this technique requires a specific noninterference hypothesis, often implicitly assumed, that may be difficult to meet during actual experimental designs. A mathematical justification of the necessity of the noninterference hypothesis is given. Furthermore, two methods are proposed that do not have the same limitations. The first method is based on autoregressive models with exogenous inputs, whereas the second one combines cross-spectral estimators with interpolation in the frequency domain. The two identification methods are validated by offline simulations and contrasted to the classic method. The results reveal that the classic method fails when the noninterference hypothesis is not fulfilled; on the contrary, the two proposed techniques give reliable estimates. Finally, the three identification methods are applied to experimental data from a closed-loop control task with pilots. The two proposed techniques give comparable estimates, different from those obtained by the classic method. The differences match those found with the simulations. Thus, the two identification methods provide a good alternative to the classic method and make it possible to simultaneously estimate human's neuromuscular and visual responses in cases where the classic method fails.

Vehicle accelerations may feed through the human body, causing involuntary limb motions which may lead to involuntary control inputs. This phenomenon is called biodynamic feedthrough (BDFT). Signal cancellation is a possible way of mitigating biodynamic feedthrough. It makes use of a BDFT model to estimate the involuntary control inputs. The BDFT effects are removed by subtracting the modeled estimate of the involuntary control input from the total control signal, containing both voluntary and involuntary components. The success of signal cancellation hinges on the accuracy of the BDFT model used. In this study the potential of signal cancellation is studied by making use of a method called optimal signal cancellation. Here, an identified BDFT model is used off-line to generate an estimate of the involuntary control inputs based on the accelerations present. Results show that reliable signal cancellation requires BDFT models that are both subject and task dependent. The task dependency is of particular importance: failing to adapt the model to changes in the operator's neuromuscular dynamics dramatically decreases the quality of cancellation and can even lead to an increase in unwanted effects. As a reliable and fast on-line identification method of the neuromuscular dynamics of the human operator currently does not exist, real-time signal cancellation is currently not feasible