A maximum likelihood approach for identifying human operator remnant in a tracking task

By applying a maximum likelihood approach to identification with empirical data from a tracking task, the output signal uncorrelated with the input signal (a definition of human operator remnant) can be determined. To obtain this remnant signal, a linear, stationary model describing the human is utilized. The innovations signal, is computed from the difference in the empirical data and the model´s output. The remnant can then be identified using the innovations sequence by computing the component of the output signal which is uncorrelated (or orthogonal) to the input signal. Data from a Roll Axis Tracking Simulator is analyzed and remnant is identified to two phases of tracking (with and without motion information).