EnTrans:Leveraging Kinetic Energy Harvesting Signal for Transportation Mode Detection
This addresses the battery drain issue in wearable devices for applications like urban design and location-based services, representing an incremental improvement in energy efficiency.
The paper tackled the problem of high energy consumption in transportation mode detection by using a kinetic energy harvester as the signal source, achieving over 92% accuracy in classifying five modes and saving more than 34% system power compared to accelerometer-based methods.
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.