Operator Functional State Classification Using Neural Networks with Combined Physiological and Performance Features

Abstract

Operator functional state assessment is a critical component of adaptive aiding systems. A combination of physiological and performance variables were used with a neural network to determine operator functional state. A multiple task battery provided three levels of mental workload. The data were randomly divided into two data sets, one was used to train a neural net and the other to test the accuracy of the trained neural net. The results showed an overall correct classification of 86.8% for the test data set. For the three levels of task difficulty the correct classification was 90.5% for low, 81.7% for the medium and 88.3% for the high. These results support the use of combined physiological and performance data to obtain high levels of operator functional state classification accuracy. The optimal approach to utilizing this information during system operation will have to be developed. With current technology the development of small, wearable operator state classifiers is possible.