Using fault detection methods to optimize parameters in human-machine interface devices

Using methods from fault diagnosis studies and signal detection theory, an optimal model involving decision making of the human operator is formulated. Parameters are estimated based on a likelihood function methodology to characterize how human responses are elicited. The theoretical model is empirically validated with human subjects and it enables one to estimate certain properties of the human-machine interface (risk adversity).