Energy Harvesting-Based Smart Transportation Mode Detection System via Attention-Based LSTM

Detecting the transportation mode of an individual's everyday travel provides useful information in urban design, real-time journey planning, and activity monitoring. In existing systems, the accelerometer and GPS are predominant signal sources which quickly drain the limited battery life of the wearable devices. In this paper, we present an alternative approach for fine-grained transportation mode detection using kinetic energy harvester (KEH). We demonstrate the feasibility of using the output signal from the KEH device as the information source to achieve transportation mode detection. The proposed system is motivated by the fact that different transportation modalities produce distinctive motion patterns which are expected to leave distinctive patterns for context detection. To achieve fine-grained transportation mode detection, we design a transportation detection framework based on attention-based Long Short Term Memory (LSTM). We evaluate our approach using 38.6 hours of transportation data, which is collected from a total of six volunteers in three months' time using our prototype. The evaluation results show that our approach is able to reach an overall accuracy of over 97% to detect fine-grained transportation modalities. In addition, our measurements show that the power consumption of the sampling KEH signal is only 460uW which significantly outperforms the existing transportation mode detection systems.