Parsimonious and identifiable driver models for online identification and interface adaptation.
Measuring and modeling how humans use preview information of a trajectory to follow for anticipation or feedforward control.
Quantitative data-driven perception modeling that separates effects of different types of cueing errors in different axes.
Developing models for how humans rely on feedforward control responses in realistic discrete maneuvering tasks.
Identifying and modeling how humans use visual and physical motion feedback for control.
Developing methods for quantitative modeling of time-varying changes in human control behavior.
Data-driven and assumption-free selection of the optimal human control model structure and response dynamics.
Determining motion perception thresholds from active control task data using multi-channel human control modeling.
Quantitative analysis of touchscreen operation in a moving and vibrating environment.
Proving enhanced training effectiveness by going beyond realism in training simulators.
Detailed and quantitative measurement of lost motor skills in neurological diseases such as Parkinson’s using human control modeling. Neurological disorders, s...
Improving the match between helicopter flight dynamics and simulator cueing.
Quantitative analysis of manual control skill acquisition, retention, and transfer.
Quantitative and objective evaluation of the effect of degraded simulator motion cueing fidelity on pilot control behavior.
Identifying high-fidelity stall dynamics models from stall flight test data.
Improved measurements of simulation motion states by combining data from multiple sensors.