Multi-model Approach to Human Functional State Estimation

• Published in: Foundations of Augmented Cognition: Neuroergonomics and Operational Neuroscience pp. 188 - 197
• Main Authors: Durkee, Kevin, Hiriyanna, Avinash, Pappada, Scott, Feeney, John, Galster, Scott
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing 2016
• Series: Lecture Notes in Computer Science
• Subjects: Cognitive states; Context; Human performance; Modeling and simulation; Physiological measurement; UAS; Workload
• DOI: 10.1007/978-3-319-39955-3_18
• ISBN: 3319399543; 9783319399546
• ISSN: 0302-9743

With the growth and affordability of the wearable sensors market, there is increasing interest in leveraging physiological signals to measure human functional states. However, the desire to produce a reliable universal classifier of functional state assessment has proved to be elusive. In efforts to improve accuracy, we theorize the fusion of multiple models into a single estimate of human functional state could outperform a single model operating in isolation. In this paper, we explore the feasibility of this concept using a workload model development effort conducted for an Unmanned Aircraft System (UAS) task environment at the Air Force Research Laboratory (AFRL). Real-time workload classifiers were trained with single-model and multi-model approaches using physiological data inputs paired with and without contextual data inputs. Following the evaluation of each classifier using two model evaluation metrics, we conclude that a multi-model approach greatly improved the ability to reliably measure real-time cognitive workload in our UAS operations test case.