Touchscreen usability degrades severely under turbulence due to biodynamic feedthrough (BDFT), which displaces the user’s finger from the intended input location. This study introduces and evaluates a moving keypad concept where the keypad shifts in real-time according to predicted BDFT. To generate these BDFT predictions, individualized-per-participant BDFT models were identified under turbulence for 18 participants in the SIMONA Research Simulator using two pointing tasks: a Relax Task, capturing BDFT while a user points passively to the screen, and a Position Task, where a user actively points to a fixed target on the screen. These models were then used to move the keypad during an evaluation task in which participants entered a four-digit pincode followed by an enter key to submit. The evaluation task was performed under six conditions: two baseline static keypad conditions (with and without turbulence), and four moving keypad conditions where "Full" and "Reduced" model variants of the Position Task and Relax Task were used to move the keypad. The Reduced model variants were created by lowering the model gain and increasing the damping ratio of the Full models to slow the keypad motion. In the static keypad baseline conditions, turbulence significantly degraded keypad performance, increasing misclicks by 621% from 0.28 to 2.02 misclicks per pincode. This confirms that BDFT degrades touchscreen performance. In the moving keypad conditions, the Full models drove the keypad with excessive speed, producing more than 5 misclicks per pincode, making these conditions effectively unusable. The Reduced models produced slower keypad motion and saw better keypad performance with less misclicks than the Full Models. Among them, the Position-Reduced condition was the only configuration that significantly improved tapping performance, lowering misclicks by 32% (to 1.38 misclicks per pincode) compared to the baseline static keypad under turbulence. The results indicate that effective mitigation requires slower and more damped keypad motion than predicted by pointing task models. This work demonstrates the feasibility of transparent, real-time BDFT mitigation for touchscreen interfaces and provide a promising direction toward reliable and effective touchscreen operation under turbulence.

In Haptic Shared Control (HSC), human-like reference generators and adaptive strategies have shown promising potential for minimizing human-machine conflicts, though these advances have thus far been limited to simple control tasks. This paper extends the application of low-conflict HSC to a more realistic driving task. An Adaptive HSC (A-HSC) design for constant-velocity steering under changing visibility is proposed, where the A-HSC dynamically adjusts its steering support to align with the human driver's behaviour. In a human-in-the-loop simulator experiment with 16 subjects, the proposed A-HSC adapted successfully to the drivers' steering behaviour, converging to an average look-ahead time of 0.46s in low visibility and 1.01s in high visibility. When visibility decreased during the task, the driver's trust and control authority influenced the adaptation, with half the drivers complying with the A-HSC support allowing it to retain high-visibility settings. The A-HSC outperformed the fixed low-visibility HSC system in reducing driver control effort, minimizing conflicts and improving subjective acceptance. However, it ranked lower than the fixed high-visibility HSC overall. To further enhance A-HSC adaptability and driver acceptance, future research should investigate how varying the authority balance between the HSC and the driver affects adaptation, and explore more intuitive HSC structures, potentially based on the driver's visual aim point.