Hidden Markov Model Based Operator Functional State Classification

High risk operating task in complex human machine system is vulnerable to the operator's break-down of functional state. To effectively estimate the OFS to avoid such serious issue is now one of the most challenging topics for researchers. One potential solution is to well classify the OFS.The experiment is based on aCAMS simulation software (automation-enhanced Cabin Air Management System). In this paper, a method of choosing the best feature for the individual subject based on correlation spectrum analysis is raised. With the new features extracted, classical Hidden Markov Model is introduced to OFS classification problem. The result shows that HMM is decent in OFS classification applications with its strong capability of modeling time serial signals. At last, the parameter choosing principals for HMM in OFS classification is researched.