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原文传递 Using kinematic driving data to detect sleep apnea treatment adherence

题名：	Using kinematic driving data to detect sleep apnea treatment adherence
其他题名：	Agrawal,R.,Faloutsos,C.,&Swami,A.(1993).Efficient similarity search in sequence databases.In D.B.Lomet(Ed.).Proceedings of the 4th International Conference,FODO'93 Chicago,Illinois,USA,October 13–15,1993(Vol.8958546,pp.69–84).Chicago,IL:Springer Berlin Heidelberg.https://doi.org/10.1007/3-540-57301-1_5
正文语种：	英文
作者：	Anthony D. McDonald
关键词：	driving;drowsiness;machine learning;sleep disorders;symbolic aggregate approximation
摘要：	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 slidingwindow. 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 thatmay 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.
出版年：	2017
论文唯一标识：	J-96Y2017V21N05006
英文栏目名称：	Articles
期刊名称：	Journal of Intelligent Transportation Systems Technology Planning and Operations
拼音刊名(出版物代码)：	J-96
卷：	21
期：	05
页码：	422-434