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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Using kinematic driving data to detect sleep apnea treatment adherence


    Beteiligte:
    McDonald, Anthony D. (Autor:in) / Lee, John D. (Autor:in) / Aksan, Nazan S. (Autor:in) / Dawson, Jeffrey D. (Autor:in) / Tippin, Jon (Autor:in) / Rizzo, Matthew (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2017-09-03


    Format / Umfang :

    13 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Driving Performance and Obstructive Sleep Apnea

    Brown, Timothy L. / Dow, Ben / Trask, Doug et al. | Transportation Research Record | 2009


    Influence of high risk of obstructive sleep apnea on adherence to antihypertensive treatment in outpatients

    Righi, Camila G. / Martinez, Denis / Gonçalves, Sandro C. et al. | Wiley | 2017

    Freier Zugriff

    Efficient Deep Learning Based Hybrid Model to Detect Obstructive Sleep Apnea

    Hemrajani, Prashant / Dhaka, Vijaypal Singh / Rani, Geeta et al. | BASE | 2023

    Freier Zugriff

    Obstructive sleep apnea

    Joseph Arnold / M Sunilkumar / V Krishna et al. | DOAJ | 2017

    Freier Zugriff