Measuring, modeling and mitigating biodynamic feedthrough

· MPI Series in Biological Cybernetics Libro 45 · Logos Verlag Berlin GmbH
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Vehicle accelerations affect the human body in various ways. In some cases, accelerations cause involuntary motions of limbs like arms and hands. If someone is engaged in a manual control task at the same time, these involuntary limb motions can lead to involuntary control forces and control inputs. This phenomenon is called biodynamic feedthrough (BDFT). The control of many different vehicles is known to be vulnerable to BDFT effects, such as that of helicopters, aircraft, electric wheelchairs and hydraulic excavators.

The fact that BDFT reduces comfort, control performance and safety in a wide variety of vehicles and under many different circumstances has motivated numerous efforts into measuring, modeling and mitigating these effects. It is known that BDFT dynamics depend on vehicle dynamics and control device dynamics, but also on factors such as seating dynamics, disturbance direction, disturbance frequency and the presence of seat belts and arm rests. The most complex and influential factor in BDFT is the human body. It is through the human body dynamics that the vehicle accelerations are transferred into involuntary limb motions and, consequently, into involuntary control inputs. Human body dynamics vary between persons with different body sizes and weights, but also within one person over time.

The goal of the research was to increase the understanding of BDFT to allow for effective and efficient mitigation of the BDFT problem. The work dealt with several aspects of biodynamic feedthrough, but focused on the influence of the variable neuromuscular dynamics on BDFT dynamics. The approach of the research consisted of three parts: first, a method was developed to accurately measure BDFT. Then, several BDFT models were developed that describe the BDFT phenomenon based on various different principles. Finally, using the insights from the previous steps, a novel approach to BDFT mitigation was proposed and experimentally validated.

 

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