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Monitoring the daily transportation modes of an individual provides usefulinformation in many application domains, such as urban design, real-timejourney recommendation, as well as providing location-based services. Inexisting systems, accelerometer and GPS are the dominantly used signal sourcesfor transportation context monitoring which drain out the limited battery lifeof the wearable devices very quickly. To resolve the high energy consumptionissue, in this paper, we present EnTrans, which enables transportation modedetection by using only the kinetic energy harvester as an energy-efficientsignal source. The proposed idea is based on the intuition that the vibrationsexperienced by the passenger during traveling with different transportationmodes are distinctive. Thus, voltage signal generated by the energy harvestingdevices should contain sufficient features to distinguish differenttransportation modes. We evaluate our system using over 28 hours of data, whichis collected by eight individuals using a practical energy harvestingprototype. The evaluation results demonstrate that EnTrans is able to achievean overall accuracy over 92% in classifying five different modes while savingmore than 34% of the system power compared to conventional accelerometer-basedapproaches.

EnTrans:Leveraging Kinetic Energy Harvesting Signal for Transportation Mode Detection