AK
A. Khoshnewiszadeh
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The anticipated arrival of touchscreens on the commercial flight deck will make pilots vulnerable to erroneous screen inputs under vibration, creating a potential safety hazard. Biodynamic feedthrough (BDFT) has shown to be a key obstacle in continuous touchscreen dragging tasks under simulated turbulence, disrupting task performance. This research therefore focuses on the implementation of a model-based approach to mitigate the adverse effects of BDFT. A human-in-the-loop experiment with 18 participants was performed. The experiment consisted of two simulator sessions, the first with the goal of collecting data used for identifying two BDFT models: a subject-average (SA) and a one-size-fits-all (OSFA) model. In the second session these two models were tested for their ability to cancel BDFT in the same two-dimensional pursuit task they were identified from, and in an additional point-to-point dragging task emulating a waypoint modification. The model-based BDFT cancellation approach was tested for two combinations of motion disturbance axis and touchscreen input direction: sway (side-to-side motion) with horizontal screen inputs and heave (up-down motion) with vertical screen inputs. The results showed it is possible to cancel between 80-90% of the BDFT in both cases despite poor cancellation for certain specific participants. The point-to-point dragging task showed much less BDFT than the continuous task used for BDFT model development, however, making the cancellation ineffective. Overall, the results show that while model-based BDFT cancellation is possible, it is crucial to account for individual variability and the specific touchscreen task.
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The anticipated arrival of touchscreens on the commercial flight deck will make pilots vulnerable to erroneous screen inputs under vibration, creating a potential safety hazard. Biodynamic feedthrough (BDFT) has shown to be a key obstacle in continuous touchscreen dragging tasks under simulated turbulence, disrupting task performance. This research therefore focuses on the implementation of a model-based approach to mitigate the adverse effects of BDFT. A human-in-the-loop experiment with 18 participants was performed. The experiment consisted of two simulator sessions, the first with the goal of collecting data used for identifying two BDFT models: a subject-average (SA) and a one-size-fits-all (OSFA) model. In the second session these two models were tested for their ability to cancel BDFT in the same two-dimensional pursuit task they were identified from, and in an additional point-to-point dragging task emulating a waypoint modification. The model-based BDFT cancellation approach was tested for two combinations of motion disturbance axis and touchscreen input direction: sway (side-to-side motion) with horizontal screen inputs and heave (up-down motion) with vertical screen inputs. The results showed it is possible to cancel between 80-90% of the BDFT in both cases despite poor cancellation for certain specific participants. The point-to-point dragging task showed much less BDFT than the continuous task used for BDFT model development, however, making the cancellation ineffective. Overall, the results show that while model-based BDFT cancellation is possible, it is crucial to account for individual variability and the specific touchscreen task.
Bachelor thesis
(2016)
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H. van Donge, S. Heyer, A.A.J. Hooijen, A. Khoshnewiszadeh, R.R.J. Krook, J. Meyer, J.R. van der Ploeg, T.S.C. Pollack, T.C. Schouten, S.C.E. Smets, P.C. Roling, A.C. in 't Veld, A. Elham