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Automatic car driving detection using raw accelerometry data.

M Strączkiewicz1, J K Urbanek, W F Fadel

  • 1Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, Krakow, Poland.

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A new algorithm, Driving Activity Detection via Accelerometry (DADA), accurately identifies driving periods from wearable sensor data. This helps correct activity count bias, showing driving adds 16% to walking activity counts.

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Area of Science:

  • Biomedical Engineering
  • Wearable Technology
  • Signal Processing

Background:

  • Wearable devices are popular for physical activity measurement.
  • Raw data (activity counts) can be biased by non-movement vibrations, like driving.
  • Accurate physical activity assessment requires accounting for driving-induced bias.

Purpose of the Study:

  • To develop and validate an algorithm for detecting driving periods using accelerometry data.
  • To quantify the bias in activity counts introduced by driving.
  • To improve the accuracy of physical activity monitoring.

Main Methods:

  • Developed the Driving Activity Detection via Accelerometry (DADA) algorithm.
  • Utilized short-time Fourier transform (STFT) on raw accelerometry data.
  • Focused on specific frequency ranges characteristic of vehicle vibrations.
  • Tested on data from 24 subjects using wrist-worn ActiGraph devices.

Main Results:

  • The DADA algorithm demonstrated excellent performance with a median AUC of 0.94 for driving detection.
  • Driving activity was found to contribute, on average, 16% to the activity counts typically generated during walking.
  • The algorithm effectively identifies driving periods, a major source of sedentary bias.

Conclusions:

  • DADA is a highly effective algorithm for detecting driving using accelerometry.
  • Quantifying driving bias is crucial for accurate physical activity assessment.
  • This method enhances the reliability of data from wearable activity monitors.