Mining Users ’ Significant Driving Routes with Low-power Sensors

While there is significant work on sensing and recogni-tion of significant places for users, little attention has been given to users ’ significant routes. Recognizing these rou-tine journeys, opens doors to the development of novel ap-plications, like personalized travel alerts, and enhancement of user’s travel experience. However, the high energy con-sumption of traditional location sensing technologies, such as GPS or WiFi based localization, is a barrier to passive and ubiquitous route sensing through smartphones. In this paper, we present a passive route sensing frame-work that continuously monitors a vehicle user solely through a phone’s gyroscope and accelerometer. This ap-proach can differentiate and recognize various routes taken by the user by time warping angular speeds experienced by the phone while in transit and is independent of phone ori-entation and location within the vehicle, small detours and traffic conditions. We compare the route learning and recog-nition capabilities of this approach with GPS trajectory anal-ysis and show that it achieves similar performance. More-over, with an embedded co-processor, common to most new generation phones, it achieves energy savings of an order of magnitude over the GPS sensor