Operator Functional State Modeling and



The goal of this project is to develop a performance modeling and prediction from physiological measurements.We propose to develop a novel approach and an efficient software utility for accurate operator functional state assessment and predication by combining and analyzing multi-indicator signals. The approach/utility includes advanced signal processing techniques for data preprocessing, feature analysis and extraction, and advanced modeling technique based on Dyanmic Bayesian Networks(DBN) for operator functional statement modeling and prediction. ††This project includes the following tasks:


o          Specifically, through applying various signal processing and feature extraction techniques, we will compute a set of parameters from each type of signal.Statistical analysis such as correlation study and ANOVA will then be performed to identify a set of features that best correlate with the operatorís functional state. We will study not only static features but also temporal features since the operatorís functional state evolves over time, and temporal features may be more predictive than static features.


o          Develop the machine learning methods to learn the proposed DBN model.The model will integrate the sensory data from different modalities both spatially and temporally to produce a composite score to characterize the operatorís functional state and performance. For this task, we will focus on developing the machine learning methods that can automatically learn the model structure and parameters based on a combination of training data and the available domain expert knowledge.


o          Demonstrate the validity of the DBN model in operatorís state and performance prediction through cross-validation using real data.We will conduct a controlled experiment to systematically compare the output of our system with the groundtruth functional state and with the performance measures.†† Specifically, we will follow the established experimental protocol to estimate the correlation between psychomotor vigilance test performance and the operator state parameters generated by the proposed system for different subjects under different functional states.The performance of our system will be characterized by its sensitivity, specificity, and the percentage of correct detection and misdetection. We will also perform an ROC (Receiver Operating Characteristics) analysis and produce an ROC curve to evaluate the detection accuracy of our system.