Using Smartwatch Inertial Sensors to Recognize and Distinguish Between Car Drivers and Passengers

Thomas Cano Hald, David Holmgaard Junker, Mads Mårtensson, Mikael Skov, Dimitrios Raptis

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

7 Citations (Scopus)

Abstract

People increasingly interact with social media or other apps on their smartphones while driving car. This is naturally a major safety concern, and it remains unclear how to avoid or limit such interaction. We investigate this problem through human activity recognition (HAR) where we developed a system called IRIS, which collects smartwatch accelerometer data and analyses the data through machine learning and predicts if the data origins from a driver or a passenger. We report from a field experiment with 24 participants acting as drivers or passengers where we achieved an overall prediction accuracy of 87%. We further found that various road segments had less effect on the accuracy than anticipated, but we also found that passenger tasks had a negative effect on recognition accuracy. We discuss several implications from findings.
Original languageEnglish
Title of host publicationProceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications
Number of pages11
PublisherAssociation for Computing Machinery
Publication date23 Sept 2018
Pages74-84
ISBN (Electronic)978-1-4503-5946-7
DOIs
Publication statusPublished - 23 Sept 2018
Event10th International ACM Conference on Automotive User Interfaces - Toronto, Canada
Duration: 23 Sept 201825 Sept 2018

Conference

Conference10th International ACM Conference on Automotive User Interfaces
Country/TerritoryCanada
CityToronto
Period23/09/201825/09/2018

Keywords

  • Accelerometer data
  • Driving
  • Human activity recognition
  • Sensor data
  • Smartwatch

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