Developing a Dual-Track Modeling Approach for Increased Understanding of Sensors and their Forecasting Capabilities

Abstract

Event Abstract Back to Event Developing a Dual-Track Modeling Approach for Increased Understanding of Sensors and their Forecasting Capabilities Raquel C. Galvan-Garza1*, Peter B. Bryan1*, Amanda E. Kraft1, Alison M. Perez1, Matthew J. Pava1, William D. Casebeer1 and Matthias D. Ziegler1 1 Lockheed Martin (United States), United States Forecasting human behavior from sensor-derived biometrics poses a modeling challenge encountered across statistical domains: balancing interpretability against model power. On one hand, polynomial models derived from literature review and a priori assumptions of feature interactions are transparent (i.e., explained variance can be meaningfully interpreted in the original input space) but fail to capture higher-order interactions that were not explicitly predicted. On the other, black-box methods from machine learning are equipped to successfully capture this variance but sacrifice explainability. Here, we evaluate a dual-track approach for modeling of human state. The first track (theory-driven) consists of white-box modeling methodologies while the second track (data-driven) consists of a black-box model, namely a recurrent neural network. The data sets we use in this presentation focus on physiological, interpersonal, and environmental factors; however, we are beginning to use the same methodology with multi-sensor studies that include neuroimaging techniques such as fNIRS and EEG. We will discuss details on how we 1) create theory-driven models for estimating variables such as subjective stress, anxiety, affect, personality, and job performance, 2) create data-driven models using complex neural networks in parallel, and 3) adapt the theory-driven model to include new connections discovered by the data-driven model using a systematic automated approach. Using methods including local sensitivity analysis, we attempt to extract variance-capturing, low-dimensional feature combinations from the data-driven track to augment theory-driven models. We conclude with a discussion of the respective advantages and disadvantages of each track, a summary of challenges intrinsic to this method, and an evaluation of the successes and failures of our complementary modeling approach. The research is based upon work supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via 2017-17042800004. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. Keywords: biometrics, forecasting human behavior, Model selection, theory-driven modeling, Data-driven Modeling, machine learning applied to neuroscience Conference: 2nd International Neuroergonomics Conference, Philadelphia, PA, United States, 27 Jun - 29 Jun, 2018. Presentation Type: Oral Presentation Topic: Neuroergonomics Citation: Galvan-Garza RC, Bryan PB, Kraft AE, Perez AM, Pava MJ, Casebeer WD and Ziegler MD (2019). Developing a Dual-Track Modeling Approach for Increased Understanding of Sensors and their Forecasting Capabilities. Conference Abstract: 2nd International Neuroergonomics Conference. doi: 10.3389/conf.fnhum.2018.227.00029 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 30 Mar 2018; Published Online: 27 Sep 2019. * Correspondence: PhD. Raquel C Galvan-Garza, Lockheed Martin (United States), Bethesda, United States, raquel.c.galvan@lmco.com Mr. Peter B Bryan, Lockheed Martin (United States), Bethesda, United States, peterbbryan@gmail.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Raquel C Galvan-Garza Peter B Bryan Amanda E Kraft Alison M Perez Matthew J Pava William D Casebeer Matthias D Ziegler Google Raquel C Galvan-Garza Peter B Bryan Amanda E Kraft Alison M Perez Matthew J Pava William D Casebeer Matthias D Ziegler Google Scholar Raquel C Galvan-Garza Peter B Bryan Amanda E Kraft Alison M Perez Matthew J Pava William D Casebeer Matthias D Ziegler PubMed Raquel C Galvan-Garza Peter B Bryan Amanda E Kraft Alison M Perez Matthew J Pava William D Casebeer Matthias D Ziegler Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.