  Monitoring the daily transportation modes of an individual provides useful
information in many application domains, such as urban design, real-time
journey recommendation, as well as providing location-based services. In
existing systems, accelerometer and GPS are the dominantly used signal sources
for transportation context monitoring which drain out the limited battery life
of the wearable devices very quickly. To resolve the high energy consumption
issue, in this paper, we present EnTrans, which enables transportation mode
detection by using only the kinetic energy harvester as an energy-efficient
signal source. The proposed idea is based on the intuition that the vibrations
experienced by the passenger during traveling with different transportation
modes are distinctive. Thus, voltage signal generated by the energy harvesting
devices should contain sufficient features to distinguish different
transportation modes. We evaluate our system using over 28 hours of data, which
is collected by eight individuals using a practical energy harvesting
prototype. The evaluation results demonstrate that EnTrans is able to achieve
an overall accuracy over 92% in classifying five different modes while saving
more than 34% of the system power compared to conventional accelerometer-based
approaches.
