EnTrans: Leveraging energy harvesting signal for transportation mode detection

• Main Authors: LAN, Guohao, XU, Weitao, MA, Dong, KHALIFA, Sara, HASSAN, Mahbub, HU, Wen
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Transportation mode detection; energy harvesting; wearable devices; sparse representation; Artificial Intelligence and Robotics; Transportation
• Online Access: https://ink.library.smu.edu.sg/sis_research/7013; https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=8016&context=sis_research
• Institution: Singapore Management University

Monitoring the daily transportation modes of an individual provides useful information in many application domains, such as urban design, real-time journey recommendation, and 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 h 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.