Transport Mode Detection with Realistic Smartphone Sensor Data

Peter Widhalm (Vortragende:r), Philippe Nitsche, Norbert Brändle

Publikation: Beitrag in Buch oder TagungsbandBeitrag in Tagungsband mit PosterpräsentationBegutachtung


We propose a novel method for automatic detection of the transport mode of a person carrying a Smartphone. Existing approaches assume idealized positioning data with no GPS signal losses, require informa tion from additional external sources such as real time bus locations, or only allow for a coarse distinction between very few categories (e.g. ´still´, ´walk´, ´motorized´). Our approach is designed to deal with cluttered real-world Smartphone data and can distinguish between fine-grained transport mode categories. It is robust against GPS signal losses by including positioning data obtained from the cellular network and data from accelerometer readings. Mode detection is performed by a two-stage classification technique using randomized ensemble of classifiers combined with a Hidden Markov Model. We report promising results of an experimental performance analysis with real-world data collected by 15 volunteers during their everyday routines over a period of two months.
TitelProceedings of the 21st International Conference on Pattern Recognition (ICPR2012)
PublikationsstatusVeröffentlicht - 2012
Veranstaltung21st International Conference on Pattern Recognition (ICPR2012) -
Dauer: 11 Nov. 201215 Nov. 2012


Konferenz21st International Conference on Pattern Recognition (ICPR2012)

Research Field

  • Ehemaliges Research Field - Mobility Systems


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