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Using kinematic driving data to detect sleep apnea treatment adherence
Journal article   Peer reviewed

Using kinematic driving data to detect sleep apnea treatment adherence

Anthony D McDonald, John D Lee, Nazan S Aksan, Jeffrey D Dawson, Jon Tippin and Matthew Rizzo
Journal of intelligent transportation systems, Vol.21(5), pp.422-434
09/03/2017
DOI: 10.1080/15472450.2017.1369060
PMCID: PMC6192061
PMID: 30344458
url
https://www.ncbi.nlm.nih.gov/pmc/articles/6192061View
Open Access

Abstract

People spend a significant amount of time behind the wheel of a car. Recent advances in data collection facilitate continuously monitoring this behavior. Previous work demonstrates the importance of this data in driving safety but does not extended beyond the driving domain. One potential extension of this data is to identify driver states related to health conditions such as obstructive sleep apnea (OSA). We collected driving data and medication adherence from a sample of 75 OSA patients over 3.5 months. We converted speed and acceleration behaviors to symbols using symbolic aggregate approximation and converted these symbols to pattern frequencies using a sliding window. The resulting frequency data was matched with treatment adherence information. A random forest model was trained on the data and evaluated using a held-aside test dataset. The random forest model detects lapses in treatment adherence. An assessment of variable importance suggests that the important patterns of driving in classification correspond to route decisions and patterns that may be associated with drowsy driving. The success of this approach suggests driving data may be valuable for evaluating new treatments, analyzing side effects of medications, and that the approach may benefit other drowsiness detection algorithms.
 Machine Learning Sleep Disorders driving drowsiness symbolic aggregate approximation

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