Prediction of Human Operator Performance

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Abstract
The Kalman Filter technique is applied to the problem of predicting human operator performance in the execution of a wide variety of tasks described by an exponential improvement model. Reliable predictions can be used as a guide by management on the efficiency of task design, operator selection, and operator training functions. Results of industrial case studies involving mechanical and electrical assemblies show that realistic predictions can be made even when the model parameters are nonstationary. Steady-state detection is also included in the paper to permit the isolation of the ``improvement plateau'' phenomenon which indicates a false performance ceiling. In such instances both the initial improvement phase and the recovery phase are described by exponential models.

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