TY - GEN
T1 - Variations on a theme
T2 - 58th International Annual Meeting of the Human Factors and Ergonomics Society, HFES 2014
AU - McLaurin, Elease
AU - McDonald, Anthony D.
AU - Lee, John D.
AU - Aksan, Nazan
AU - Dawson, Jeffrey
AU - Tippin, Jon
AU - Rizzo, Matthew
N1 - Publisher Copyright:
Copyright 2014 Human Factors and Ergonomics Society.
Copyright:
Copyright 2016 Elsevier B.V., All rights reserved.
PY - 2014
Y1 - 2014
N2 - This paper introduces Probabilistic Topic Modeling (PTM) as a promising approach to naturalistic driving data analyses. Naturalistic driving data present an unprecedented opportunity to understand driver behavior. Novel strategies are needed to achieve a more complete picture of these datasets than is provided by the local event-based analytic strategy that currently dominates the field. PTM is a text analysis method for uncovering word-based themes across documents. In this application, documents were represented by drives and words were created from speed and acceleration data using Symbolic Aggregate approximation (SAX). A twenty-topic Latent Dirichlet Allocation (LDA) topic model was developed using words from 10,705 documents (real-world drives) by 26 drivers. The resulting LDA model clustered the drives into meaningful topics. Topic membership probabilities were successfully used as features in subsequent analyses to differentiate between healthy drivers and those suffering from Obstructive Sleep Apnea.
AB - This paper introduces Probabilistic Topic Modeling (PTM) as a promising approach to naturalistic driving data analyses. Naturalistic driving data present an unprecedented opportunity to understand driver behavior. Novel strategies are needed to achieve a more complete picture of these datasets than is provided by the local event-based analytic strategy that currently dominates the field. PTM is a text analysis method for uncovering word-based themes across documents. In this application, documents were represented by drives and words were created from speed and acceleration data using Symbolic Aggregate approximation (SAX). A twenty-topic Latent Dirichlet Allocation (LDA) topic model was developed using words from 10,705 documents (real-world drives) by 26 drivers. The resulting LDA model clustered the drives into meaningful topics. Topic membership probabilities were successfully used as features in subsequent analyses to differentiate between healthy drivers and those suffering from Obstructive Sleep Apnea.
UR - http://www.scopus.com/inward/record.url?scp=84957671415&partnerID=8YFLogxK
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U2 - 10.1177/1541931214581443
DO - 10.1177/1541931214581443
M3 - Conference contribution
C2 - 26190948
AN - SCOPUS:84957671415
T3 - Proceedings of the Human Factors and Ergonomics Society
SP - 2107
EP - 2111
BT - 2014 International Annual Meeting of the Human Factors and Ergonomics Society, HFES 2014
PB - Human Factors an Ergonomics Society Inc.
Y2 - 27 October 2014 through 31 October 2014
ER -