Application of Time-Series Modeling to Human Operator Dynamics

Time-series analysis is applied to model human operator dynamics in pursuit and compensatory tracking modes. The normalized residual criterion is used as a one-step analytical tool to encompass the processes of identification, estimation, and diagnostic checking. A parameter constraining technique is introduced to develop more reliable models of human operator dynamics. The human operator is adequately modeled by a second-order dynamic system both in pursuit and compensatory tracking modes. In comparing the data sampling rates, 100 ms between samples is adequate and is shown to provide better results than a 200 ms sampling. The residual power spectrum and eigenvalue analysis show that the human operator is not a generator of periodic characteristics.